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Software Solutions For Energy Industry
We are a leading provider of IT services for the energy sector. Our dedicated software offers the perfect solution for energy companies seeking advanced, secure, and flexible tools to support growth and ensure the reliability of their energy infrastructure. With our products, energy management becomes simpler, more efficient, and highly effective.
Custom Software Development Services For Energy Industry
ADVANCED PLATFORMS –
SOFTWARE SOLUTIONS FOR ENERGY COMPANIES
REMOTE MONITORING TOOLS – SMART POWER INDUSTRY SOFTWARE SOLUTIONS
Benefits of Our IT Solutions for the Energy Industry
Enhanced reliability
Improved energy flow control and faster response to failures.
Cost savings
Process optimization and reduction of energy losses.
Regulatory compliance
All solutions adhere to international standards, including IEC 61850.
Our Experience in Energy
Sector Software Development
Energy Software Development Services- Software Maintenance
The power industry requires reliable systems that are essential for ensuring continuous energy supply. Regular software maintenance helps prevent costly downtime, enhances operational security, and improves efficiency, while also adapting systems to rapidly evolving technological and regulatory requirements.
With our software maintenance services, you benefit from:
- Continuous real-time monitoring of software performance.
- Early detection of potential issues and proactive resolution before failures occur.
- Log and system data analysis to identify anomalies.
- Regular installation of updates and security patches in line with vendor recommendations.
- System modernization to ensure compliance with new regulations such as NIS2 and GDPR.
- Software configuration adjustments to meet the evolving needs of your enterprise.
- Performance analysis and optimization to maintain smooth operation even under high workloads.
- Continuous technological advancements to optimize efficiency.
- Fast technical support in case of failures or issues.
- Maintaining compatibility with new devices, SCADA systems, and Operational Technology (OT) infrastructure.
- Adding new features and modules as technology and business needs evolve.
Comprehensive IT Support for the Energy Sector
As an energy software development company for the energy sector, we offer comprehensive IT solutions for the whole industry. Our services cover a wide range of IT solutions tailored to the unique needs of the sector, ensuring support at every stage of project implementation and infrastructure management.
Custom Energy Software Development – Back Office Solutions
Back office solutions for the energy industry, such as the implementation of Salesforce Sales Cloud, enable the centralization of customer and product data, automation of sales processes, and integration with key business systems. This allows operational teams to manage information more efficiently, reducing the time needed to gather data from multiple sources, ultimately improving performance and customer service.
Power Grid Automation – Reliability You Can Trust
Power grid automation is essential for modern energy management. It ensures a stable, reliable, and efficient electricity supply to consumers. The advanced technologies and algorithms we provide enable precise monitoring, control, and optimization of network operations, enhancing efficiency, security, and reliability for users.
Why Invest in Power Grid Automation from TTMS?
Power grid automation is more than just a technological solution—it is a strategic investment in safety, efficiency, and the reliability of energy supply. It enables operators to optimize costs, reduce the risk of failures, and dynamically adapt the grid to evolving user demands.
Choose cutting-edge technologies that support the growth and seamless operation of your energy infrastructure.
Case Study – IT Solutions
for the Energy Sector
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Best Energy Software Companies in 2025 – Global Leaders in Energy Tech
The energy sector is undergoing a rapid digital transformation in 2025. Leading energy technology companies around the world are delivering advanced software to help utilities and energy providers manage power more efficiently, reliably, and sustainably. From smart grid management and real-time analytics to AI-driven maintenance and automation, the top energy software companies offer solutions that drive efficiency and support the transition to cleaner energy. Below is a ranking of the best energy software companies in 2025, highlighting their focus areas, scale, and why they stand out. These leading energy management software companies are empowering the industry with cutting-edge IT development, AI integration, and services tailored for the energy domain. 1. Transition Technologies MS (TTMS) Transition Technologies MS (TTMS) is a Poland-headquartered IT services provider that has emerged as a dynamic leader in energy sector software. Founded in 2015 and now over 800 specialists strong, TTMS leverages its expertise in custom software, cloud, and AI to deliver bespoke solutions for energy companies. TTMS has deep roots in the European energy industry – it’s part of a larger capital group that has supported major power providers for years. The company builds advanced platforms for real-time grid monitoring, remote asset management, and automated fault detection, all with robust cybersecurity and compliance (e.g. IEC 61850, NIS2) in mind. TTMS’s engineers have helped optimize energy operations in refineries, mines, wind and solar farms, and energy storage facilities by consolidating systems and introducing smarter analytics. By combining enterprise technologies (as a certified Microsoft, Adobe, and Salesforce partner) with industry know-how, TTMS delivers end-to-end software that improves efficiency and reliability in energy management. Its recent projects include developing AI-enhanced network management tools to prevent blackouts and implementing digital platforms that integrate distributed energy resources. For energy companies seeking agile development and innovative solutions, TTMS offers a unique blend of domain experience and cutting-edge tech skill. TTMS: company snapshot Revenues in 2024: PLN 233.7 million Number of employees: 800+ Website: https://ttms.com/software-solutions-for-energy-industry/ Headquarters: Warsaw, Poland Main services / focus: Real-time network management systems (RT-NMS), SCADA integration, predictive maintenance, IoT & AI analytics, cybersecurity compliance (NIS2), cloud-based energy monitoring, and digital transformation for utilities 2. Siemens Siemens is a global industrial technology powerhouse and a leader in energy management software and automation solutions. With origins dating back over 170 years, Siemens provides utilities and industrial firms with advanced platforms for grid control, power distribution, and smart infrastructure management. Its portfolio includes SCADA and smart grid software (e.g. Spectrum Power and SICAM) that enable real-time monitoring of electricity networks, as well as IoT and AI-based analytics to predict and prevent outages. Siemens also integrates renewable energy and storage into grid operations through its cutting-edge control systems. Known for its deep R&D capabilities and engineering excellence, Siemens continues to drive innovation in energy technology – from digital twin simulations of power plants to intelligent building energy management. As one of the world’s largest tech companies in this space, Siemens offers end-to-end solutions that help modernize energy systems and ensure reliable, efficient power delivery. Siemens: company snapshot Revenues in 2024: €75.9 billion Number of employees: 327,000+ Website: www.siemens.com Headquarters: Munich, Germany Main services / focus: Industrial automation, energy management, smart grid software, IoT solutions 3. Schneider Electric Siemens is a global industrial technology leader in energy management software and automation. For over 170 years, it has provided utilities and industries with advanced platforms for grid control, power distribution, and smart infrastructure. Its SCADA and smart grid tools (like Spectrum Power and SICAM) enable real-time monitoring and use AI analytics to prevent outages. Siemens also integrates renewables and storage through advanced control systems. With strong R&D and engineering expertise, the company delivers end-to-end energy solutions that modernize power systems and ensure efficiency and reliability. Schneider Electric: company snapshot Revenues in 2024: €38.15 billion Number of employees: 155,000+ Website: www.se.com Headquarters: Rueil-Malmaison, France Main services / focus: Digital automation, energy management, power systems, sustainability solutions 4. General Electric (GE Vernova) General Electric’s energy division, now known as GE Vernova, is one of the top energy software and equipment companies in the world. GE Vernova combines the legacy of GE’s power generation and grid businesses into a focused energy technology company. It produces everything from heavy-duty gas turbines and wind turbines to advanced software for managing power plants and electric grids. On the software side, GE’s solutions (such as the GE Digital Grid suite) help utilities orchestrate the flow of electricity, monitor grid stability, and integrate renewable sources via intelligent control systems. The company leverages industrial IoT and AI to enable predictive maintenance – for instance, analyzing sensor data from turbines or transformers to foresee issues and optimize performance. With a century-long heritage in electrification, GE Vernova remains a go-to provider for end-to-end energy infrastructure needs, pairing its industrial hardware with modern software to drive efficiency and decarbonization efforts globally. General Electric (GE Vernova): company snapshot Revenues in 2024: $34.9 billion Number of employees: 75,000 Website: www.gevernova.com Headquarters: Cambridge, Massachusetts, USA Main services / focus: Power generation equipment, grid infrastructure, energy software, industrial IoT 5. IBM IBM is a pioneer in applying enterprise software, cloud and artificial intelligence to the energy sector. As a global IT leader, IBM provides utilities and energy companies with solutions to modernize their operations and harness data effectively. One flagship offering is IBM Maximo for Asset Management, which helps energy and utility firms monitor the health of critical infrastructure (like transformers, pipelines, and power stations) and schedule maintenance proactively. IBM’s IoT platforms and analytics enable smart grid capabilities – for example, balancing electricity supply and demand in real time or detecting anomalies in power networks. The company’s consulting arm also partners with energy providers on digital transformation projects, from improving cybersecurity of grid systems to implementing AI-driven demand forecasting. With its breadth of experience across industries, IBM serves as a trusted technology partner for energy companies aiming to improve reliability, efficiency, and customer service through software innovation. IBM: company snapshot Revenues in 2024: $62.8 billion Number of employees: 270,000+ Website: www.ibm.com Headquarters: Armonk, New York, USA Main services / focus: Cloud & AI solutions, enterprise software, IoT for energy, consulting services 6. Accenture Accenture is a global IT consulting and professional services company that plays a major role in the energy industry’s digital initiatives. With a dedicated Energy & Utilities practice, Accenture helps power companies implement custom software solutions, upgrade legacy systems, and deploy emerging technologies like AI and blockchain. The firm has led large-scale smart grid rollouts, customer information system implementations, and analytics programs for utility providers worldwide. Accenture’s strength lies in end-to-end delivery: from strategy and design to development and systems integration, ensuring new tools fit seamlessly into an organization. For instance, Accenture might develop a cloud-based energy trading platform for a utility or streamline an oil & gas company’s supply chain with automation software. Its vast global team (hundreds of thousands of IT experts) and experience across many industries make Accenture a go-to partner for energy companies seeking to modernize and become more data-driven. In short, Accenture is a leader in energy software development services, guiding clients through complex technology transformations that improve efficiency and business outcomes. Accenture: company snapshot Revenues in 2024: $65.0 billion Number of employees: 770,000+ Website: www.accenture.com Headquarters: Dublin, Ireland Main services / focus: IT consulting, digital transformation, software development, AI services 7. ABB ABB is a Swiss-based engineering and technology company renowned for its industrial automation and electrification solutions, including a strong portfolio of energy software. Through its ABB Ability™ platform and related offerings, the company provides digital tools for monitoring and controlling power grids, renewable energy installations, and smart buildings. ABB’s energy management software helps utility operators supervise substations, optimize load flow, and integrate distributed energy resources like solar panels and batteries. The firm also delivers control systems for power plants and factories, combining them with IoT sensors and AI analytics to improve performance and safety. In the realm of electric mobility, ABB’s software manages electric vehicle charging networks and energy storage systems to support the evolving grid. With over a century in the power sector, ABB blends deep technical know-how with modern software development, making it one of the top energy management software companies driving reliability and efficiency across global energy infrastructure. ABB: company snapshot Revenues in 2024: $32.9 billion Number of employees: 110,000+ Website: www.abb.com Headquarters: Zurich, Switzerland Main services / focus: Robotics, industrial automation, electrification, energy management software Energize Your Operations with TTMS’s Expertise As this ranking shows, the energy software landscape is full of global tech giants – but Transition Technologies MS (TTMS) combines agility, industry insight, and technical excellence that truly set it apart. Belonging to the Transition Technologies Capital Group, which has supported the energy sector for over 30 years, TTMS benefits from deep engineering heritage and access to a powerful R&D ecosystem. This background enables us to deliver tailor-made digital solutions that modernize and optimize energy operations across the entire value chain. One example is our recent digital transformation project for a major European energy automation company, where TTMS developed a scalable application that unified multiple legacy systems, streamlined workflows, and significantly improved operational efficiency. The platform not only enhanced monitoring and control processes but also introduced automation that reduced downtime and increased data accuracy. The results: faster decision-making, lower maintenance costs, and a future-ready digital infrastructure. Another success story comes from a client in the Grynevia Group, a company with over 30 years of experience in the mining and industrial energy sectors. Facing growing sales complexity and data fragmentation, TTMS implemented Salesforce Sales Cloud to replace scattered Excel sheets with a centralized CRM system. The solution provided instant reporting, full visibility of the sales pipeline, and smoother communication between teams. As a result, the company gained control over its business processes, strengthened decision-making, and laid a solid foundation for future digitalization across production and energy operations. If you’re looking to modernize your energy operations with advanced software, TTMS is ready to be your trusted partner. From real-time network management and cybersecurity compliance to AI-driven analytics, our solutions are built to help energy companies achieve greater efficiency, reliability, and sustainability. Harness the power of innovation in the energy sector with TTMS – and let us help you drive measurable results in 2025 and beyond. How is AI changing the way energy companies predict demand and manage grids? AI allows energy providers to move from reactive to predictive management. Machine learning models now process massive data streams from smart meters, weather systems, and market conditions to forecast consumption patterns with unprecedented accuracy. This enables utilities to balance supply and demand dynamically, reduce waste, and even prevent blackouts before they happen. Why are cybersecurity and compliance becoming critical factors in energy software development? The growing digitalization of grids and critical infrastructure makes the energy sector a prime target for cyberattacks. Regulations such as the EU NIS2 Directive and the Cyber Resilience Act require strict data protection, incident reporting, and system resilience. For software vendors, compliance is not only a legal necessity but also a key trust factor for clients operating national infrastructure. What role do digital twins play in the modernization of energy systems? Digital twins – virtual replicas of physical assets like turbines or substations – are revolutionizing energy management. They allow operators to simulate real-world conditions, test system responses, and optimize performance without risking downtime. As a result, companies can predict maintenance needs, extend asset lifespan, and make data-driven investment decisions. How can smaller or mid-sized utilities benefit from advanced energy software traditionally used by large corporations? Thanks to cloud computing and modular SaaS models, powerful energy management platforms are no longer reserved for global utilities. Mid-sized providers can now access AI analytics, predictive maintenance, and smart grid monitoring through scalable, cost-efficient tools. This democratization of technology accelerates innovation across the entire energy landscape. What future trends will define the next generation of energy technology companies? The next wave of leaders will blend sustainability with data intelligence. Expect to see more AI-driven microgrids, peer-to-peer energy trading platforms, and blockchain-based verification of renewable sources. The industry is moving toward autonomous energy ecosystems where technology enables self-optimizing, resilient, and transparent power networks – redefining what “smart energy” truly means.
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AI in Procurement for Energy: 2026 Insights
AI is making its way into procurement teams at energy companies, transforming the way they work every day. It now helps predict future needs, negotiate better deals, choose the most trustworthy suppliers, and keep spending under control. In a world where commodity prices can shift overnight and competitors fight hard for every contract, every dollar saved counts. For energy companies, the takeaway is simple – to survive and grow, they need to treat AI as a trusted partner in building a competitive edge and protecting the future of their business. 1. What Is AI in Procurement – Definitions and Key Technologies Artificial intelligence in procurement refers to intelligent systems that automate, analyze, and streamline purchasing tasks using advanced algorithms and data processing technologies. At the core of these systems is machine learning – algorithms that improve themselves by learning from historical data. Natural language processing (NLP) automates tasks such as document analysis, contract review, and supplier communications. Advanced data analytics, combining statistical methods with AI, turns raw data into actionable insights for procurement teams. These systems continuously learn from completed transactions and adapt to changing business conditions. Generative AI (GenAI) – technology that can create new content such as RFPs, contract summaries, or supplier messages – represents the latest step in the evolution of AI in procurement. According to the EY Global CPO Survey 2025, as many as 80% of chief procurement officers plan to adopt generative AI in their procurement processes. 2. The Evolution of AI in the Energy Sector The adoption of AI in procurement for the energy industry has come a long way – from simple task automation to advanced predictive analytics and real-time decision-making. Initially, the goal was to digitize manual processes. Today, AI-driven solutions combine deep learning with behavioral science to enhance sourcing, negotiations, and supplier relationship management. The transformation of the energy sector – including the shift to renewables, deregulation of markets, and the explosive growth of available data – has significantly accelerated AI adoption. Artificial intelligence is no longer just support – it has become a strategic driver of change. Recent analyses show that applying AI in renewable energy companies can improve operational efficiency by as much as 15–25%. Key areas include supply chain management and optimization of energy market transactions (McKinsey & Company, The Future of AI in Energy, 2024). 3. Key Benefits of Implementing AI in Procurement Increased operational efficiency – by automating repetitive tasks such as invoice matching or contract analysis, procurement teams can focus on more strategic activities. Better forecasting and demand management – data-driven predictions enable more accurate purchasing and inventory planning. Energy savings – AI helps optimize energy consumption across operational processes. Sustainability and ESG compliance – automated reporting ensures alignment with environmental and ethical goals. Applications of AI in Procurement – Examples Intelligent contract management AI automates the entire contract lifecycle, extracts key clauses, flags inconsistencies, and suggests corrections in line with internal company policies. NLP tools compare new documents with approved templates, improving compliance and reducing the risk of errors. Supplier evaluation and selection AI systems analyze data in real time to assess suppliers in terms of performance, risk, and compliance with requirements. They also help generate RFPs and predict which partners are most likely to meet specific criteria. Real-time data and faster decision-making AI-driven analytics enable continuous monitoring of market changes, anomaly detection, and quick responses to emerging opportunities. Automated communication and document creation Generative AI drafts messages, RFPs, contract summaries, and other documents, relieving procurement teams of time-consuming administrative work. Key Risks in Implementing AI – and How to Minimize Them Data quality and integrity The biggest risk to successful AI adoption is the lack of reliable, consistent data. Issues such as fragmented formats, incomplete historical records, or missing standards can disrupt AI performance entirely. To address this, companies need strong data governance frameworks, ongoing quality monitoring, and training programs that help teams assess and improve data accuracy. System integration and outdated technologies Many organizations still rely on siloed, legacy systems that are difficult to connect. Lack of integration remains one of the main barriers. Solutions include gradual consolidation of procurement tools, using middleware or data lakes to unify data, and reducing technical debt step by step. Infrastructure limitations and energy consumption AI systems require stable and significant energy resources. When deploying them, companies should consider locating data centers near existing energy sources, diversifying energy contracts with renewables, and working closely with infrastructure operators to secure reliable power supply. Regulatory and compliance complexity As AI plays a bigger role in strategic procurement, regulatory oversight is tightening. To navigate this, organizations should collaborate actively with regulators, establish cross-functional compliance teams, and join industry working groups that shape realistic standards. Cybersecurity risks AI expands the potential attack surface. That’s why companies need to adopt a zero-trust approach, deploy advanced threat detection tools, and make cybersecurity risk assessments a mandatory part of every AI-related project. Talent shortages and skills gap The energy sector faces a major shortage of experts who combine knowledge of both AI and energy. According to the World Economic Forum’s 2025 report, this talent gap is slowing innovation and adoption of new technologies. Local infrastructure limitations and the lack of capable technology partners to support global rollouts at the local level also add to the challenge. An additional barrier is cultural – a reluctance to take risks and a preference for incremental change. Many organizations still lean toward gradual improvements rather than bold transformations, which delays the full potential of AI in procurement. 4. How TTMS Sees the Future of AI in Energy Procurement The energy sector is entering a new phase of digital transformation, where artificial intelligence not only streamlines operations but also begins to shape procurement strategies. From TTMS’s perspective, the coming years will bring a strong acceleration of AI adoption in this area – both among large energy groups and smaller operators. “Energy companies that want to successfully implement AI in procurement should start by organizing their data – its structure, quality, and accessibility. The key is to build a unified information ecosystem that enables algorithms to learn from real processes. At TTMS, we support our clients in building these foundations – from ERP system integration to the deployment of cloud solutions that ensure scalability and security of procurement operations.” — Marek Stefaniak, Sales Director for Energy Technologies, TTMS Automating procurement with generative AI We predict that generative AI will soon become a standard tool for automating procurement documents – from RFPs and contracts to comparative analyses and supplier communications. This will radically reduce administrative workloads and shorten the entire procurement cycle. TTMS is already implementing solutions based on large language models, enabling operational teams to interact naturally with data – even without technical expertise. Advanced predictive analytics AI models will increasingly support demand forecasting, risk assessment, and procurement planning based on market, weather, regulatory, and geopolitical data. Companies that invest in integrating these data streams into procurement processes will gain a major competitive advantage. TTMS already supports clients in building such integrated data environments, combining OT and IT systems and developing analytics platforms and predictive models tailored to the energy market. Edge AI and real-time decisions Edge AI will play a growing role, particularly in dynamic areas such as energy trading, balancing, and supply chain management. Real-time procurement decisions will become a necessity rather than a competitive edge. AI as a driver of ESG strategy and procurement transparency In response to regulatory demands and market pressure, companies will require tools that not only automate but also report on ESG compliance, carbon footprint, and supplier ethics. An example is the SILO system from Transition Technologies – software for power plants that optimizes combustion, reduces emissions, and generates critical environmental reporting data. Integrated with AI-powered procurement tools, such systems enable plants to meet ESG requirements while precisely planning fuel and reagent purchases, delivering measurable savings. A new cost landscape: an investment that pays off At TTMS, we see artificial intelligence as a key enabler of procurement transformation – especially in sectors exposed to volatile market prices, geopolitical risks, and raw material availability. AI does more than automate processes and cut costs – it strengthens organizations’ ability to respond quickly to rapidly changing conditions. With advanced analytics and predictive models, companies can forecast price trends, assess risks, and make informed procurement decisions before the market reacts. In our view, the ability to make intelligent, data-driven predictions – based on historical, real-time, and contextual data – will soon become one of the most critical factors for survival and growth in competitive energy, raw materials, and industrial markets. The tangible benefits of AI in energy procurement include: Higher efficiency of procurement teams Reduction of errors and inefficient processes Better risk management across the supply chain Greater transparency and regulatory compliance 5. How TTMS Supports the Energy Sector in Smarter Procurement with AI – and Beyond 5.1 Conclusions: Where Are AI-Powered Energy Procurement Processes Heading? Procurement in the energy sector is undergoing a profound transformation, with artificial intelligence as the driving force. AI is no longer just a supporting tool – today it is a central part of business strategy, enabling real cost savings, boosting operational efficiency, and strengthening resilience against market volatility. At Transition Technologies MS, we have been supporting energy companies in their digital transformation for years. We deliver comprehensive IT solutions that integrate data from multiple sources, automate processes, and empower smarter decision-making. In procurement, we enable the deployment of AI-powered tools that forecast demand, predict energy prices, optimize purchasing strategies, and mitigate risks. 5.2 The Energy Sector of the Future with TTMS Today’s energy industry faces major challenges: market instability, increasing regulatory demands, and both climate and digital transformation. The answer lies in intelligent, scalable, and integrated systems built on artificial intelligence and data. TTMS helps energy companies build data-driven procurement strategies, automate operations, and implement AI tools that deliver real efficiency gains and competitive advantage. In addition, we provide: Advanced solutions that integrate data from multiple OT and IT sources Development of predictive systems and energy monitoring platforms Creation of secure, resilient IT environments Support with regulatory compliance and cybersecurity Our experience spans partnerships with leading energy companies in Poland and across Europe. We know that success depends on combining technology with expertise and a deep understanding of business context. Want to learn how we can support your company? Explore our energy sector services Discover our AI solutions for business Contact us via Contact Form What are the main benefits of implementing AI in energy procurement? Artificial intelligence in energy procurement boosts operational efficiency, reduces costs, and minimizes risks across the supply chain. It enables more accurate demand forecasting, automates time-consuming administrative tasks, accelerates decision-making, and ensures full compliance with industry regulations and ESG goals. As a result, companies gain both short-term savings and long-term resilience in an increasingly volatile energy market Which AI technologies are most commonly used in energy procurement? The most widely applied technologies include machine learning for advanced analysis and prediction, natural language processing (NLP) for contract review and supplier communications, and generative AI (GenAI) for automatically creating RFPs, contract summaries, and reports. Edge AI is also gaining momentum, enabling real-time decision-making in fast-changing market environments such as energy trading and supply chain management. What are the biggest challenges in adopting AI for energy procurement? The main barriers are poor data quality and lack of standardization, difficulties in system integration, high energy requirements of AI infrastructure, complex regulatory frameworks, and a shortage of specialists who combine expertise in both AI and energy. Overcoming these challenges requires strong data governance strategies, modernization of legacy technologies, and continuous upskilling of employees to build the necessary competencies. How does AI support ESG strategies in the energy sector? AI automates the collection and analysis of data on CO₂ emissions, energy efficiency, and supplier ethics. This allows companies to quickly report compliance with environmental regulations, track progress toward sustainability goals, and ensure transparency in supply chain management. By embedding ESG considerations into procurement processes, AI helps energy companies not only meet external requirements but also strengthen their reputation and stakeholder trust.
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The Cyber Resilience Act in the energy sector – obligations, risks, and how to prepare for 2025?
The EU’s Cyber Resilience Act (CRA) marks a turning point in the way digital products are secured across Europe. By 2027, all software will need to comply with CRA requirements, and as early as next year, companies will face mandatory cybersecurity incident reporting. This issue is particularly critical for the energy sector, where outdated and poorly secured systems are still in use. A lack of proper safeguards can lead to severe consequences – not only financial but also operational and social. CRA applies to all software in the EU starting in 2027. For the energy sector, this means obligations such as SBOM, secure-by-design, and incident reporting. TTMS supports companies in preparing for and implementing CRA requirements. Ignoring the regulation may result in fines, market exclusion, and exposure to real cyberattacks. 1. Why is the energy sector especially vulnerable? The energy sector is the backbone of modern society – the economy, public administration, and daily life all depend on its stability. As critical infrastructure, electricity supply must be uninterrupted. Any disruption can cause serious social and economic fallout – from halting transport and communications to crippling hospitals or emergency services. Yet, this infrastructure relies on complex control systems such as SCADA, RTU, EMS, or HMI. Many of them were designed in an era when cybersecurity was not a top design priority. Built primarily for performance and reliability, they are often ill-equipped to withstand today’s digital threats. The challenge intensifies with the convergence of OT and IT systems. More elements of physical infrastructure are now connected to corporate networks, increasing the attack surface and complicating risk management. Cybercriminals no longer need physical access to a power plant or substation – a single vulnerability in a remote-control system may be enough. Adding to the risk is technological legacy. Many organisations still rely on outdated operating systems and applications deeply embedded in technological processes. These cannot be easily updated or replaced, making them an easy target for cyberattacks. 1.1 The threat is not theoretical – real incidents prove it. In 2017, a cyberattack targeted the German company Netcom BW, a telecommunications network operator owned by EnBW, one of Germany’s largest energy providers. The attacker was a Russian national and a member of Berserk Bear, a group linked to Russia’s FSB intelligence service. The goal was to infiltrate communication infrastructure used not only by Netcom BW but also by energy system operators. While the companies assured that the core energy infrastructure remained intact, the attack exposed vulnerabilities in the supply chain and the dependencies between IT systems and critical energy assets. This is a warning that cannot be ignored. Incidents like this highlight that cybersecurity cannot stop at the boundaries of a power plant or transmission grid – it must extend to technology suppliers, communication systems, and all interconnected digital components. This is precisely why the implementation of the EU’s Cyber Resilience Act is not only a legal requirement but also a strategic step towards building a resilient energy sector for the future. 2. CRA – What Does It Mean for Energy Companies and How Can TTMS Help? The new EU regulation introduced by the Cyber Resilience Act (CRA) imposes binding cybersecurity obligations on software providers across the energy sector. For many organisations, this means reorganising development processes, implementing new tools, and ensuring both formal and technical compliance. This is where Transition Technologies MS steps in, offering both advisory and technological support. 2.1 Mandatory SBOMs (Software Bill of Materials) CRA requires every company delivering software to maintain a complete list of components, libraries, and dependencies used in their product. How TTMS helps: We implement tools that automate the creation and updating of SBOMs in popular formats (e.g. SPDX, CycloneDX), integrating them with CI/CD pipelines. We also support risk analysis of open-source components and help establish dependency management policies. 2.2 Secure-by-Design Development CRA enforces the obligation to embed security into products from the very first design stage. How TTMS helps: We provide threat modelling workshops, application architecture security audits, and the implementation of secure DevSecOps practices. Our support also includes penetration testing and code reviews at every stage of the product lifecycle. 2.3 Vulnerability Management The regulation requires organisations to detect, classify, and patch vulnerabilities quickly – not only in their own code but also in third-party components. How TTMS helps: We build and integrate vulnerability management processes – from static scanning (SAST) and dynamic testing (DAST) to real-time vulnerability monitoring systems. We help implement procedures aligned with best practices (e.g. CVSS, CVD). 2.4 Incident Reporting Every major security incident must be reported to ENISA or the local CSIRT within 24 hours. How TTMS helps: We create incident response plans (IRPs), implement detection and automated reporting systems, and train IT and OT teams in CRA-compliant procedures. TTMS can also act as an external cyber emergency response partner. 2.5 EU Declaration of Conformity Software providers must deliver a formal document confirming compliance with CRA requirements – this is not only a declaration but also a legal responsibility. How TTMS helps: We support companies in creating and maintaining CRA-required documentation, including declarations of conformity, security policies, and technical support plans. We provide pre-implementation audits and assistance in preparing for regulatory inspections. 2.6 Additional Support and Parallel Development Implementing CRA requirements does not have to mean halting other development projects. At TTMS, we provide additional resources in a staff augmentation model, enabling organisations to continue software development in parallel with the process of adapting applications to new regulations. This way, energy companies can maintain their pace of innovation while effectively meeting legal requirements. Moreover, we offer comprehensive cybersecurity testing support across three key areas: Infrastructure audits and penetration testing Application audits and penetration testing Source code audits All these services are delivered by TTMS in cooperation with Transition Technologies Software (TTSW), ensuring complete security both at the system and application level. Why Work with TTMS? Proven experience in the energy sector – deep knowledge of SCADA, EMS, DMS, and OT/IT environments. Dedicated Quality and Cybersecurity experts – supporting organisations throughout the entire CRA compliance cycle. Ready-to-use solutions and tools – from SBOM management to incident response and risk analysis. Security-as-a-Service – flexible support models tailored to client needs. 3. Ignoring CRA Could Cost More Than You Think Non-compliance with the Cyber Resilience Act is not just a formal issue – it is a real risk to business continuity and market presence in the EU. CRA foresees severe financial penalties – up to €15 million or 2.5% of global annual turnover – for failing to meet software security requirements. In addition, non-compliant products may be completely excluded from the EU market, which for many companies – especially those in critical infrastructure – could mean the loss of key contracts. Neglecting security also increases the risk of real cyberattacks that may paralyse systems, leak sensitive data, and cause massive financial and reputational losses. A notable example is the ransomware attack on the Norwegian company Norsk Hydro in March 2019. The global aluminium producer and energy provider had its IT systems worldwide shut down, forcing plants to switch to manual operations. The direct and indirect costs exceeded $70 million, and the company struggled for weeks to restore operations and rebuild market trust. Although this case dates back a few years, the number of similar attacks has been rising steadily amid Europe’s ongoing hybrid warfare. In 2025, Poland reported two major cybersecurity incidents in public institutions – one involving a personal data breach caused by an email system intrusion, and another targeting industrial control systems. Cases like these show that failing to act proactively on cybersecurity can cost far more than investing in CRA compliance. It is not only a legal obligation but also a condition for maintaining competitiveness and business resilience in the digital era. 4. Cyber Resilience Act – Consequences of Non-Compliance and Real Risks of Cyberattacks Failure to comply with CRA can result in: Financial penalties of up to €15 million or 2.5% of global annual turnover Exclusion from the EU market Increased risk of cyberattacks leading to system paralysis and massive financial losses 4.1 When Should You Start Acting? The Clock Is Ticking The Cyber Resilience Act was adopted in October 2024. While full compliance will not be required until December 2027, one of the key obligations – reporting security incidents within 24 hours – will already apply from September 2026. This means that companies – especially those in critical infrastructure sectors such as energy – have less than a year to prepare procedures, train teams, implement the right tools, and test their systems. Implementing CRA is not about a single document – it requires a comprehensive change in how software is developed and maintained, covering security, documentation, vulnerability management, and formal compliance. Leaving compliance until the last minute is a recipe for errors, system gaps, and costly consequences. Organisations that start preparing now will gain not only a time advantage but also a strategic one, demonstrating to partners and customers that they take cybersecurity seriously – before being forced to. This is precisely where Transition Technologies MS (TTMS) can make the difference. Our expert teams support organisations at every stage of CRA readiness – from analysing current processes and conducting security audits, to implementing SBOM and vulnerability management tools, developing incident reporting procedures, and preparing formal compliance documentation. TTMS does more than advise – we implement real technical solutions, deliver training, and provide ongoing support as part of a long-term partnership. If your organisation operates in the energy sector, do not delay CRA compliance – the consequences of inaction can be severe both operationally and financially. Talk to one of our cybersecurity experts and discover how TTMS can help you navigate this process smoothly and effectively. Visit ttms.pl/energy to learn more about the software and solutions we build for energy companies. Looking for a quick summary? Check out our FAQ section, where we have gathered the most important questions and answers from this article. When does the Cyber Resilience Act (CRA) come into force and what is the timeline? The Cyber Resilience Act was officially adopted in October 2024. Full compliance with its provisions will be mandatory from December 2027. However, from September 2026, companies will already be required to report security incidents within 24 hours. This leaves limited time for organisations to analyse, prepare, and implement the necessary processes – especially in the energy sector, where action must be both fast and methodical. Which products and systems in the energy sector are covered by CRA? The regulation applies to all “products with digital elements,” meaning both physical devices and software that can connect to a network. In practice, this includes critical energy management and control systems such as SCADA, RTU, EMS, DMS, and HMI – the backbone of digital energy infrastructure. If your software operates in this environment, CRA directly affects your organisation. What specific obligations does CRA impose on energy companies? Energy companies must introduce Software Bills of Materials (SBOMs), design systems with a secure-by-design approach, manage and patch vulnerabilities quickly, report major incidents to relevant institutions within strict deadlines, and prepare an EU Declaration of Conformity for their products. These are not mere formalities – they have a tangible impact on the security and resilience of entire energy systems. What are the risks for companies that ignore CRA requirements? Non-compliance may result in fines of up to €15 million or 2.5% of a company’s global annual turnover – whichever is higher. In addition, non-compliant products may be removed from the EU market entirely. Beyond financial penalties, ignoring CRA also exposes companies to real cyber risks, such as ransomware attacks. The Norsk Hydro case showed how a single incident can cause operational paralysis, data loss, and reputational damage with long-term consequences. Does every company have to report incidents, even if there was no service disruption? Yes. CRA requires reporting of any major security incident or actively exploited vulnerability within 24 hours of detection. A follow-up report must then be submitted within 72 hours, and a final summary within 14 days. This applies not only to incidents that cause outages but also to those that could potentially affect product or user security. The aim is to ensure early transparency and rapid mitigation across the entire EU market.
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Digital Transformation of Energy Management: 2025 Guide
1. Digital Transformation of Energy Management: 2025 Guide The energy sector sits at a fascinating crossroads where old-school operations meet cutting-edge digital tech. Here’s something that’ll grab your attention: half a trillion dollars was invested globally in data centers in 2024 alone. That’s massive infrastructure change happening right now. Organizations are dealing with mounting pressure for sustainability, efficiency, and rock-solid reliability. Digital transformation isn’t just nice to have anymore—it’s become essential for staying operational. Energy companies across the globe get it now. Embracing digital technologies isn’t about grabbing shiny new tools; it’s about completely rethinking how operations work. Industry leaders have been deep in Europe’s energy transformation trenches and seen firsthand how smart digital moves can completely revolutionize infrastructure management. When you combine artificial intelligence, Internet of Things, and advanced analytics, you create incredible opportunities to optimize energy systems while meeting those tough environmental and regulatory demands. The numbers don’t lie about urgency: data centers alone account for roughly 2% of global electricity and are projected to reach almost 12% of U.S. power demand by 2030. This explosive growth in digital infrastructure demand makes efficient energy management critical for both economic and environmental reasons. 2. Understanding Digital Transformation in Energy Management for 2025 Digital transformation in energy management represents a complete evolution that weaves advanced technologies into every corner of energy operations. This goes way beyond simple automation—we’re talking about intelligent systems that predict, adapt, and optimize energy flows in real-time. Industry leaders are seeing real results: energy companies actively implementing digital technologies are achieving operational cost reductions of 20-30%. That’s the kind of financial impact that gets board attention. Several interconnected forces drive this transformation. Rising global energy demands paired with increasing environmental awareness create pressure for more efficient, sustainable operations. Meanwhile, tech advances have made sophisticated digital solutions more accessible and affordable than ever. Modern energy management systems use interconnected technologies to create seamless operational environments. IoT sensors continuously watch equipment performance across distributed networks, while AI analyzes huge datasets to predict maintenance needs and optimize energy distribution. The results speak for themselves: productivity gains of 5-15% are reported among power producers and utility companies that have integrated these digital technologies. The transformation also supports renewable energy integration, which brings unique challenges because of variable generation patterns. Digital systems can predict renewable generation patterns, automatically adjust grid operations, and coordinate distributed energy resources to maintain stability. This capability becomes increasingly vital as the energy mix shifts toward cleaner sources. TTMS has been leading this digital evolution, developing advanced platforms specifically designed for managing complex energy systems. Our software solutions enable precise real-time energy flow management, automated fault detection and response, and customizable operational settings tailored to specific system requirements. These capabilities transform how energy companies approach infrastructure management, shifting from reactive to proactive operational models. 3. Core Technologies Revolutionizing Energy Management 3.1 Smart Grid Infrastructure and Grid Modernization Smart grid technology represents the backbone of modern energy management, transforming traditional electrical grids into intelligent, responsive networks. The impact is measurable: in the United States, intelligent network management systems have led to a 44% reduction in power outages, translating to billions of dollars in savings through improved reliability. Modernized grid systems use automation, advanced communication technologies, and sophisticated controls to enhance reliability, efficiency, and flexibility. They enable utilities to respond dynamically to changing demands while integrating diverse energy sources. Smart grid transformation requires comprehensive upgrades to existing infrastructure. These systems automatically detect faults, reroute power, and optimize distribution based on real-time demand, reducing operational costs while improving service reliability. 3.1.1 Advanced Metering Infrastructure (AMI) Advanced Metering Infrastructure (AMI) transforms traditional meter reading into comprehensive data collection and analysis. AMI provides granular energy consumption data for accurate billing and personalized recommendations. These systems detect unusual patterns indicating equipment problems or theft, identify power quality issues, and reveal peak demand periods, helping utilities optimize strategies. AMI enables time-of-use pricing that encourages consumers to shift usage to off-peak periods, reducing peak generation needs and promoting efficient infrastructure use. 3.1.2 Distributed Energy Resource Management Systems (DERMS) Distributed Energy Resource Management Systems (DERMS) coordinate and optimize decentralized energy assets across the grid, including solar panels, wind turbines, batteries, and demand response programs. Using advanced algorithms, DERMS forecast renewable output, predict demand, and coordinate asset dispatch to ensure efficient renewable energy use while maintaining grid reliability. Beyond operational efficiency, DERMS enable business models like virtual power plants, allowing aggregated distributed resources to participate in energy markets, creating revenue for asset owners while enhancing system reliability. 3.2 Internet of Things (IoT) and Industrial IoT Applications The Internet of Things revolution connects previously isolated energy assets into integrated networks, providing unprecedented visibility and control. IoT deployment creates comprehensive sensing networks that monitor equipment performance, environmental conditions, and operations in real-time. Industrial IoT applications in energy management focus on mission-critical systems requiring high reliability and security, operating in harsh environments while providing accurate data for critical decisions. These robust systems are suitable for monitoring high-voltage equipment, generation facilities, and transmission infrastructure. 3.2.1 Smart Sensors and Real-Time Monitoring Smart sensors continuously track temperature, pressure, vibration, and electrical characteristics, providing data to optimize equipment performance and predict maintenance needs. Advanced sensors detect subtle changes indicating developing problems, such as bearing wear or electrical hot spots, preventing minor issues from becoming major outages. When integrated with analytics platforms, these systems enable condition-based maintenance programs that reduce costs while improving reliability and extending asset life cycles. 3.2.2 Connected Energy Assets and Equipment Connected energy assets enable centralized monitoring and control of distributed infrastructure, allowing remote diagnostics and automated adjustments to optimize system performance. Data from these assets feeds into management systems that track performance trends and maintenance history, supporting informed decision-making. These assets can participate in automated control schemes that optimize energy flows, such as batteries charging during low-price periods and discharging during peak demand to maximize value while supporting grid stability. 3.3 Artificial Intelligence and Machine Learning Integration Artificial intelligence and machine learning technologies process the vast amounts of data generated by modern energy systems to uncover patterns, optimize operations, and automate decision-making processes. As one industry CTO notes, “Artificial Intelligence is becoming a key pillar in the energy sector, enabling companies to personalize their services and optimize processes”, improving both energy efficiency and customer relationships. AI and ML systems continuously learn from operational data, improving their accuracy and effectiveness over time. This learning capability enables energy systems to adapt to changing conditions and optimize performance based on historical patterns and current circumstances, resulting in more efficient operations, reduced costs, and improved reliability. 3.3.1 Predictive Analytics for Energy Forecasting Predictive analytics use historical data, weather patterns, and operational parameters to forecast energy demand, renewable generation, and equipment performance, enabling utilities to optimize schedules and prepare for peak periods. Weather-dependent renewables require sophisticated forecasting models. Solar generation forecasts account for cloud cover and atmospheric conditions, while wind predictions consider speed, direction, and turbulence. Demand forecasting incorporates weather, economic activity, and social patterns to predict electricity consumption, supporting resource planning and market participation while helping utilities balance supply availability with peak demand requirements. 3.3.2 AI-Powered Energy Optimization Algorithms AI-powered optimization algorithms automatically adjust system parameters to minimize energy waste, reduce costs, and maximize efficiency by processing complex problems with multiple variables and constraints. Building energy management systems use AI to coordinate heating, cooling, and lighting based on occupancy, weather, and energy prices, learning occupant preferences to balance comfort with minimal energy use. Grid-level optimization algorithms coordinate generation resources, storage systems, and demand response programs, considering fuel costs, renewable availability, and grid constraints to optimize dispatch schedules for cost-efficiency and reliability. 3.4 Digital Twin Technology for Energy Infrastructure Digital twin technology creates virtual replicas of physical energy assets that mirror their real-world counterparts in real-time. These digital models combine sensor data, operational parameters, and system characteristics to provide comprehensive insights into asset performance and behavior. The virtual nature of digital twins allows for experimentation and scenario testing that would be impossible or dangerous with physical assets. Operators can test different operating strategies, evaluate the impact of proposed modifications, and assess system responses to various conditions, supporting informed decision-making and risk mitigation. 3.4.1 Virtual Modeling of Energy Systems Virtual modeling creates detailed representations of energy systems, capturing physical characteristics, constraints, and performance behaviors through engineering principles and data. Multi-domain models represent electrical, mechanical, thermal, and control aspects to simulate component interactions and predict system behavior. These models support engineering analysis, design evaluation, operational planning, and training for operators to develop optimal strategies. 3.4.2 Simulation and Scenario Planning Simulation capabilities enable energy organizations to test responses to hypothetical events such as equipment failures, demand spikes, or extreme weather conditions. These simulations help develop contingency plans, evaluate system resilience, and identify potential vulnerabilities. Monte Carlo simulations can evaluate system performance under uncertainty by running thousands of scenarios with different input parameters. These statistical approaches provide insights into the range of possible outcomes and the probability of different events, supporting risk assessment and informed decisions about system design and operating strategies. 3.5 Blockchain and Distributed Ledger Technologies Blockchain technology introduces transparency, security, and automation to energy transactions and data management. Distributed ledger systems create immutable records of energy transactions, enabling peer-to-peer trading, automated contract execution, and secure data sharing. The decentralized nature of blockchain systems eliminates the need for traditional intermediaries in energy transactions. Smart contracts can automatically execute trades, settlements, and payments based on predefined conditions, reducing transaction costs and processing times while ensuring transparent and secure exchanges. 3.5.1 Peer-to-Peer Energy Trading Platforms Peer-to-peer energy trading platforms enable direct transactions between energy producers and consumers without traditional utility intermediaries. These platforms use blockchain technology to facilitate secure, transparent trades while automatically handling settlements and payments. Residential solar panel owners can sell excess generation directly to neighbors through P2P platforms, creating local energy markets that reduce transmission losses and support community energy independence. The trading platforms handle price discovery, matching buyers and sellers, and ensuring fair market operations. 3.5.2 Energy Certificate and Carbon Credit Management Blockchain technology provides secure, transparent tracking of renewable energy certificates and carbon credits throughout their lifecycle. These systems create tamper-proof records of certificate issuance, ownership transfers, and retirement, ensuring the integrity of environmental markets. Smart contracts can automatically issue certificates when renewable energy is generated and verified by IoT sensors. The certificates can then be traded on blockchain-based marketplaces with full transparency and traceability, eliminating manual processes and reducing the risk of double-counting or fraud. 4. Real-World Success Stories: Digital Energy Management in Action The impact of digital transformation becomes clear when examining actual implementations. Recent case studies from Europe and North America demonstrate the tangible benefits of strategic digital adoption. 4.1 RWE’s AI-Driven Grid Optimization German energy giant RWE has deployed artificial intelligence and big data analytics across its operations, achieving grid stabilization improvements of up to 15%. The company deployed Germany’s first commercial megabattery and expanded AI-driven forecasting capabilities to support more accurate renewable energy integration and improved grid operation across Germany, Czech Republic, and the United States. 4.2 Duke Energy’s Smart Grid Revolution Duke Energy’s comprehensive smart grid deployment, featuring IoT sensors and smart meters, has delivered impressive results. The utility achieved a 30-50% reduction in equipment downtime through predictive maintenance capabilities. Enhanced grid reliability, real-time performance tracking, and automated demand adjustment have enabled widespread real-time energy consumption analysis and optimization. 4.3 Enlog’s Energy Efficiency Breakthrough European energy management company Enlog has demonstrated the power of AI-powered energy management through its IoT sensor networks. The company’s “Smi-Fi” system achieved electricity consumption reductions of up to 23% for business clients by seamlessly integrating IoT into legacy electrical systems for predictive demand modeling and consumption reduction. TTMS’s Unified Application Drives Efficiency in Energy Operations TTMS has successfully streamlined and optimized processes for a global energy management leader by consolidating and migrating legacy environments into a unified, scalable platform. Since partnering in 2010, TTMS established a dedicated team—now comprising approximately 60 specialists—to develop, maintain, and continuously enhance this integrated solution. The comprehensive application replaced multiple dispersed tools, addressing significant challenges including the absence of centralized management for relay security tools and fragmented legacy systems. By implementing a unified platform, TTMS achieved substantial operational improvements, such as enhanced process efficiency, reduced maintenance costs, and significantly improved scalability. This transformation enables the client to seamlessly expand and evolve their systems without undergoing extensive migrations. This long-term collaboration highlights the practical value of strategic digital transformation, demonstrating measurable efficiency gains, cost reductions, and sustainable operational excellence. These success stories illustrate the practical benefits of digital transformation, moving beyond theoretical advantages to demonstrate measurable operational improvements and cost savings. 5. Strategic Implementation of Digital Energy Management 5.1 Building a Digital Energy Management Roadmap Developing a comprehensive digital transformation strategy requires careful assessment of current capabilities, clear definition of objectives, and systematic prioritization of technology investments. Organizations must balance ambitious transformation goals with practical implementation constraints, creating roadmaps that deliver measurable value while building toward long-term objectives. Industry analysis indicates that over 30% of surveyed professionals identify closing energy projects that demonstrate measurable, transparent value as the industry’s top focus for 2025. This emphasis on demonstrable ROI shapes how organizations approach digital transformation planning. The strategic planning process begins with evaluating existing infrastructure, processes, and capabilities to identify gaps between current and desired states, highlighting high-impact areas for digital technologies. Technical, financial, and organizational factors must be considered for successful implementation. TTMS implements digital energy management through assessment and customized solutions, with experience from Europe’s leading energy providers demonstrating the importance of aligning technology with organizational needs and constraints. 5.2 Data Integration and Management Strategies Successful digital transformation requires effective data integration that unifies information from diverse sources into actionable insights. Energy organizations typically have data scattered across operational technology, business applications, and external systems. Data management must handle both structured and unstructured data from SCADA systems to weather forecasts. Integration architecture needs to balance real-time processing requirements with historical analytics capabilities, performance needs, cost, and scalability. Strong data quality and governance frameworks ensure integrated information remains accurate, consistent, and secure, establishing standards for data handling while protecting sensitive information. 5.3 Cloud Computing and Edge Computing Solutions Cloud computing provides scalable infrastructure and analytics for digital energy management without major hardware investments. Edge computing processes data locally, reducing latency for critical operations that need immediate responses. Hybrid architectures optimize performance by using edge computing for time-critical operations while leveraging cloud for complex analytics and centralized management. TTMS develops integrated solutions combining both technologies, enabling real-time grid monitoring while ensuring seamless hardware connectivity. 6. Overcoming Digital Transformation Challenges 6.1 Cybersecurity and Data Protection Strategies Digital transformation expands energy organizations’ attack surface through connected systems, IoT devices, and cloud platforms. For critical energy infrastructure, cybersecurity is fundamental, not optional. Multi-layered security combines network security, endpoint protection, and application security with encryption, robust authentication, and continuous monitoring. The evolving threat landscape requires ongoing security updates, vulnerability assessments, and 24/7 monitoring with AI-powered threat detection and response. 6.2 Securing Critical Energy Infrastructure Critical energy infrastructure requires specialized security measures that address both cyber and physical threats. Control systems, generation facilities, and transmission networks must be protected from attacks that could disrupt service or damage equipment. Air-gapped networks isolate critical control systems from external connections, reducing the risk of remote attacks. When connectivity is required, secure communication channels and strict access controls limit exposure. Regular security assessments identify potential vulnerabilities and ensure that protection measures remain effective against evolving threats. 6.3 Legacy System Integration and Interoperability Energy organizations must carefully integrate new digital technologies with diverse legacy systems to maintain operational continuity. System integration strategies need to address technical compatibility, data format differences, and workflow alignment, with middleware solutions bridging gaps and API management platforms providing standardized interfaces. Comprehensive testing—including functional verification, performance assessment, and failure mode analysis—along with incremental migration strategies help ensure safe, correct operation while reducing risk. 6.4 API Management and System Integration Application Programming Interfaces provide standardized methods for different systems to communicate. Effective API management ensures security, reliability, and documentation. RESTful APIs enable cross-platform system integration, simplifying connectivity while maintaining flexibility for future additions. Monitoring tools track API performance to identify issues and optimization opportunities, while rate limiting prevents system overload and ensures fair resource allocation. 6.5 Investment Planning and ROI Considerations Digital transformation requires significant investments balanced against financial constraints, with clear value propositions for stakeholders. Total cost of ownership analysis must consider implementation costs, operational expenses, maintenance, upgrades, and system impacts. Phased implementation spreads costs while delivering incremental benefits, with early wins building support for continued investment. Organizations typically see positive ROI within 2-5 years. 6.6 Cost-Benefit Analysis Framework Comprehensive cost-benefit analysis evaluates financial impacts (cost savings, revenue increases, risk reduction) and non-financial impacts (improved safety, customer satisfaction, regulatory compliance) of digital transformation initiatives. Quantitative analysis monetizes benefits like reduced maintenance costs, improved energy efficiency, and decreased outage duration. Companies implementing digital technologies typically achieve 20-30% operational cost reductions. Risk assessment evaluates potential negative outcomes and probabilities to balance investment decisions, while mitigation strategies reduce negative impacts while preserving benefits. 6.7 Change Management and Skills Development Successful digital transformation requires organizational change that goes beyond technology implementation. People, processes, and culture must evolve to realize the full benefits of digital technologies. Communication strategies keep stakeholders informed about transformation goals, progress, and expected impacts. Regular updates build awareness and support while addressing concerns and resistance. Leadership commitment and visible sponsorship demonstrate organizational priority and encourage employee participation. Training and development programs equip employees with skills needed to operate new technologies and processes. Competency frameworks identify required capabilities and guide development activities. Continuous learning approaches ensure that skills remain current as technologies evolve. 6.8 Building Digital-First Energy Culture Cultural transformation involves changing mindsets, behaviors, and practices to embrace digital approaches to energy management. Digital-first culture prioritizes data-driven decision-making, continuous improvement, and innovation. Innovation programs encourage employees to identify opportunities for digital solutions and propose improvements to existing processes. Recognition and reward systems reinforce desired behaviors and celebrate successful innovations. Collaboration tools and practices enable cross-functional teams to work effectively on digital initiatives. Digital workspaces and communication platforms support distributed teams while knowledge management systems preserve and share insights. 7. Emerging Trends and Future Outlook for 2025 7.1 Energy-as-a-Service (EaaS) Business Models Energy-as-a-Service (EaaS) transforms traditional energy models into service-based approaches where providers handle infrastructure, management, and optimization while customers pay for services rather than equipment. Subscription models offer predictable costs and guaranteed service levels, simplifying budgeting while providers manage maintenance, optimization, and compliance. EaaS enables quick adoption of advanced technologies without significant capital investment by leveraging economies of scale across multiple customers. 7.2 Autonomous Energy Systems and Self-Healing Grids Autonomous energy systems represent the next grid intelligence evolution, offering self-monitoring, diagnosis, and healing capabilities. They automatically detect faults, isolate affected areas, and restore service without human intervention. Self-healing grid technologies minimize outages by reconfiguring power flows around damaged components. Distribution automation isolates faults within seconds and immediately restores power to unaffected areas. Machine learning analyzes historical and real-time data to predict failures before they occur, enabling proactive maintenance and system adjustments that prevent outages rather than just responding to them. 7.3 Integration with Electric Vehicle Infrastructure The growing EV adoption presents both challenges and opportunities for energy management. While EV charging increases electricity demand during peak periods, smart charging technologies can manage this load and support grid operations. Smart charging systems coordinate charging with grid conditions, renewable availability, and electricity pricing, delaying charging during peak demand and accelerating when renewables are abundant. Bidirectional charging allows EVs to provide grid services like frequency regulation, demand response, and backup power, 7.4 Expert Predictions for 2025 Industry leaders are optimistic about the continued acceleration of digital transformation. As one senior analyst notes: “The energy and digital revolutions must advance hand in hand. Their convergence is not inevitable, but it is essential for building a more efficient, sustainable, and future-ready energy transition”. Key priorities for 2025 include: AI and Automation: Personalizing services, optimizing resource management, and enabling predictive maintenance IoT and Big Data: Real-time monitoring, predictive maintenance, and dynamic demand response 5G Connectivity: Enabling real-time data integration at scale with immersive technologies like VR/AR for training Grid Modernization: Smart grids, decentralized energy resources, and advanced grid-edge analytics According to the Spacewell Energy Survey 2024, “Technology remains a cornerstone of energy management innovation. The ability to fine-tune energy usage through data analytics and intelligent automation allows organizations to reduce waste, cut costs, and meet evolving regulatory demands.” 7.5 Sustainability and ESG Reporting Automation ESG reporting requirements are expanding due to stakeholder demands for transparency. Automated systems collect, analyze and report sustainability metrics in real-time, monitoring energy usage, emissions, and resources while identifying trends and anomalies. Standardized frameworks with automated data collection reduce administrative burden, improve data quality, and ensure accurate performance metrics through operational system integration. 8. Getting Started with TTMS: Your Digital Energy Management Action Plan 8.1 Initial Assessment and Technology Selection Starting your digital transformation journey requires evaluating current capabilities and challenges. TTMS conducts thorough assessments of existing systems, integration opportunities, and organizational readiness. Technology selection must align with operational requirements and strategic objectives. TTMS helps evaluate options and recommend solutions that balance functionality, cost, and implementation complexity based on our energy sector experience. Stakeholder engagement throughout the process ensures solutions address real operational needs and gain organizational support, helping identify requirements and build commitment to transformation goals. 8.2 Phase-by-Phase Implementation Strategy TTMS advocates phased digital transformation, starting with foundational technologies like data integration and monitoring. Later phases introduce advanced analytics and automation. Each phase includes clear objectives and success metrics, with regular reviews to adjust strategies based on lessons learned. Parallel development and testing methodologies minimize operational disruption while ensuring new systems meet all requirements. 8.3 Measuring Success and Continuous Improvement Success measurement frameworks track technical performance and business value delivery through indicators like system reliability, cost savings, and customer satisfaction. Continuous improvement processes ensure digital systems evolve to meet changing needs. TTMS provides ongoing support to maximize technology investments. Benchmarking against industry standards helps organizations understand their performance and identify improvements. TTMS leverages energy sector experience to provide comparative insights and recommendations If you are intrested in digital transformation of your energy company contact us now! What is digital transformation of energy management? Digital transformation of energy management involves integrating advanced technologies such as IoT, AI, and blockchain into energy operations to improve efficiency, reliability, and sustainability. This transformation encompasses everything from smart grid infrastructure to automated energy optimization systems. How do IoT and AI improve energy management? IoT devices provide real-time monitoring and control capabilities across energy infrastructure, while AI algorithms analyze data to optimize operations, predict maintenance needs, and automate decision-making. Together, these technologies enable more responsive and efficient energy systems. What ROI can organizations expect from digital energy investments? Organizations implementing digital technologies typically see operational cost reductions of 20-30% and productivity gains of 5-15%. Most organizations achieve positive ROI within 2-5 years, with some seeing benefits within 18 months. What are the main challenges in implementing digital energy solutions? Key challenges include integrating new technologies with legacy systems, ensuring cybersecurity, managing data integration complexity, securing adequate investment, and developing organizational capabilities. Successful implementation requires comprehensive planning and phased approaches. How can organizations measure the ROI of digital energy investments? ROI measurement should consider both quantifiable benefits such as cost savings and efficiency improvements, and strategic advantages including improved reliability and sustainability performance. Comprehensive cost-benefit analysis frameworks help organizations evaluate investment outcomes. What cybersecurity measures are essential for digital energy systems? Essential measures include multi-layered security architectures, encryption of data and communications, robust access controls, continuous threat monitoring, and incident response procedures. Security must be integrated into system design rather than added as an afterthought.
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Blackout 2025: Preventing Power Outages with Real-Time Network Management Systems (RT-NMS)
On April 28, 2025, the eyes of all of Europe turned to the Iberian Peninsula. This was due to a sudden failure that, in just five seconds, deprived almost 100% of the territory of two countries—Spain and Portugal—of electricity. It is estimated that at the peak of the event, more than 50 million people had no access to electric power. The incident caused serious disruptions to public transportation, communications, healthcare, and financial services. The cause of the failure is still under investigation, and various hypotheses are being considered. In this article, we will examine one of them—related to maintaining the stability of the power grid. We will attempt to explain the role that RT-NMS systems play in preventing critical situations caused by sudden changes in energy production. 1. How RT-NMS Systems Improve Power Grid Stability and Prevent Blackouts Real-Time Network Management Systems are advanced IT platforms used by energy system operators (TSOs and DSOs) to monitor, control, and optimize the operation of the power grid in real time. Thanks to these systems, it is possible to respond on an ongoing basis to changes in energy production, transmission, and consumption. What do these systems do? They collect data from thousands of sensors, meters, transformer stations, and renewable energy farms. They monitor network parameters—such as voltage, frequency, line load, and power flows. They detect anomalies—for example, overloads, failures, voltage drops, and instabilities. They make automatic decisions—such as disconnecting a section of the grid or activating reserves. They enable remote control—of energy flows, power plants, and battery storage systems. They help forecast risks—through integration with weather forecasts and AI algorithms. These systems work very closely together, creating an integrated ecosystem that enables comprehensive management of the energy infrastructure—from power plants to end users. Each of the systems has its own specialization, but their synergy is key to ensuring the safety and efficiency of the grid. A Practical Example in Action: ➡ When photovoltaic farms suddenly stop producing electricity (e.g., due to cloud cover), SCADA detects the power drop → EMS activates reserves in a gas-fired power plant → DMS reduces consumption in less critical areas → the system maintains voltage and prevents a blackout. 2. Renewable Energy Challenges for Grid Stability and Frequency Control Experts point out that real-time network management systems were not sufficiently prepared for the blackout that occurred on April 28, 2025, in Spain and Portugal. Although there was no technical failure of these systems, their ability to respond rapidly to sudden disturbances was limited. Pratheeksha Ramdas, a senior analyst at Rystad Energy, noted in an interview with The Guardian that while renewable energy sources cannot be definitively blamed for the blackout, their growing share in the energy mix may make it harder to absorb frequency disturbances. She emphasized that many factors—such as system failure or weak transmission lines—could have contributed to the event. Meanwhile, Miguel de Simón Martín, a professor at the University of León, stated in WIRED that grid stability depends on three key factors: a well-connected transmission network, appropriate interconnections with other systems, and the presence of so-called “mechanical inertia” provided by traditional power plants. He pointed out that the Spanish power grid is poorly interconnected with the rest of Europe, which limits its ability to respond to sudden disruptions. 3. Critical Factors in Real-Time Power Grid Management Systems The rapid response of the power system to disruptions is the result of many interrelated elements. Automation alone is not enough – what matters is the quality of data, availability of resources, efficient organization and anticipation of possible scenarios. Below we discuss the key areas that are critical to effective real-time operation. 3.1 Technological foundations of rapid response in the power system How quickly and effectively a power grid management system can react to sudden disturbances—such as failures, overloads, or rapid drops in power—is not a matter of chance. Many interdependent elements are at play: from technology and network architecture to the quality of data and control algorithms, all the way to how the people responsible for system security are organized. Let’s take a closer look at these components. In order for the power system to respond effectively to disturbances, real-time data availability is essential. The faster data from meters, sensors, and devices reaches the system, the faster it can react. This requires fast communication protocols, a large number of measurement points (telemetry), and minimal transmission delays (latency). The second key element is automated decision-making algorithms based on artificial intelligence and machine learning. These enable systems to independently detect anomalies and make immediate decisions without human involvement. An example would be the automatic activation of power reserves or redirection of energy flow. Another necessary condition for effective response is the availability of power reserves and energy storage. Even the best-designed system cannot react effectively if it lacks sufficient resources. Fast reserves include industrial batteries, gas-fired power plants with short start-up times, and flexible consumers such as industries capable of temporarily reducing energy usage. Integration with distributed energy resources (DER)—such as photovoltaic farms, wind turbines, prosumers, or energy storage systems—is also crucial. The system must have visibility and control over these elements, because a lack of integration may cause them to disconnect automatically during disturbances instead of supporting grid stability. 3.2 Organizational factors and the importance of planning The design of the power grid itself—its topology and redundancy—is another important aspect. The more flexible and disturbance-resistant the grid is, for example through interconnections with other countries, the easier it is to respond. “Islanded” grids, like the one on the Iberian Peninsula, have significantly fewer options for importing energy in emergency situations. Operator and crisis team capabilities cannot be overlooked. Even the most advanced and automated systems require the presence of well-trained personnel who can quickly interpret data and respond appropriately in unusual situations. Lastly, the level of prediction and planning plays a critical role. The better the system can forecast risks—such as drops in renewable energy output or sudden demand spikes—the better it can prepare, for instance by activating power reserves in advance. 4. Lessons from the Iberian Power Outage: Root Causes and System Response Although experts consider the stability of technological infrastructure in the energy sector to be crucial in the context of the recent blackout, the Spanish system operator has not issued an official statement on the matter. The latest official statement from Red Eléctrica de España (REE) regarding the April 28, 2025 blackout confirms that by 7:00 a.m. on April 29, 99.95% of electricity demand had been restored. Additionally, REE submitted all the required data to the Commission for Energy Crisis Analysis. So, what was the official cause of the April blackout on the Iberian Peninsula? We will likely find out after the appropriate authorities complete their investigation. 5. Is the U.S. and Europe at Risk of the Next Major Power Grid Blackout? According to a report by the North American Electric Reliability Corporation (NERC), about half of the United States is at risk of power shortages within the next decade. Regions such as Texas, California, New England, the Midwest, and the Southwest Power Pool (SPP) may experience power outages, especially during extreme weather events or periods of peak demand. The situation is no different in Europe. The European Union faces the challenge of modernizing its energy grid. More than half of its transmission lines are over 40 years old, and infrastructure investments are struggling to keep up with the rapid development of renewable energy sources. The International Energy Agency (IEA) recommends doubling investments in energy infrastructure to $600 billion annually by 2030 to meet the demands of the energy transition. It is worth noting that the traditional power grid was designed around large, predictable energy sources: coal, gas, hydroelectric, and nuclear power plants. Today, however, the energy mix increasingly relies on renewable sources, which are inherently unstable. The sun sets, the wind calms down—and if the right technological safeguards are not in place at that moment, the grid starts to lose balance. This can be avoided through technological transformation in the energy sector. 6. TTMS IT Solutions for Energy: Real-Time Grid Management and Blackout Prevention Today’s power grid management is not just about responding to outages, but more importantly, predicting and preventing them in real time. An efficient IT infrastructure and the availability of physical assets and predictive data are the foundation of digital system resilience. Check out how TTMS supports this. 6.1 Real-time responsive IT infrastructure Modern real-time IT infrastructure plays a key preventive role in ensuring the continuous operation of power systems. Advanced network management systems—such as SCADA, EMS, and DMS—constantly monitor critical grid parameters, including voltage, power flow, and frequency. In the event of a sudden disturbance, this infrastructure triggers immediate responses—dynamically rerouting power flows, activating available reserves, and communicating with distributed energy resources (DER) and storage systems. 6.2 The importance of physical executive resources However, the effectiveness of these actions depends not only on the software but also on the availability of appropriate physical resources. A system cannot respond effectively if it lacks actual execution capabilities. These include gas-fired power plants with short start-up times, industrial batteries capable of delivering energy instantly, frequency stabilizing devices (e.g., capacitors), and cross-border infrastructure enabling the import of electricity from outside the country. In practice, these elements determine the grid’s resilience to disturbances. 6.3 Risk forecasting and integration of TTMS solutions An essential complement to this entire ecosystem are predictive tools—including forecasting models based on artificial intelligence. Thanks to these tools, it is possible to identify risks in advance and respond proactively. If the system predicts a production drop of several gigawatts within the next few minutes, it can preemptively activate storage resources, initiate load reduction among industrial consumers, or reconfigure the transmission network. Transition Technologies MS (TTMS) supports the energy sector in building digital resilience and managing the grid in real time. We provide comprehensive IT solutions that enable the integration of SCADA, EMS, DMS, and DERMS systems with predictive tools, allowing for uninterrupted monitoring and automatic responses to network anomalies. We help our partners implement intelligent mechanisms for managing energy production, distribution, and storage, as well as design predictive models using AI and weather data. As a result, operators can better plan their actions, reduce the risk of blackouts, and make faster, better-informed decisions. Today’s energy infrastructure is no longer just cables and devices—it is an integrated, intelligent ecosystem in which digital decision-making mechanisms and physical resources complement each other. It is this synergy that determines the system’s stability in times of crisis. Explore how TTMS can help your utility ensure real-time energy resilience. Contact us or visit our Energy IT Solutions page. Looking for quick insights or a fast recap? Start with our FAQ section. Here you’ll find clear, to-the-point answers to the most important questions about the 2025 blackout, real-time energy management systems, and the future of power grid stability. FAQ What caused the April 2025 blackout in Spain and Portugal? The exact cause of the April 2025 blackout is still under investigation by relevant authorities. However, experts point to the growing complexity of the power grid and challenges in maintaining stability amid a rising share of renewable energy sources. Although Red Eléctrica de España ruled out a cyberattack and reported no intrusion into control systems, factors like poor interconnections with the European grid and a lack of mechanical inertia may have contributed. Real-time systems were not technically at fault but struggled to react fast enough to a sudden disturbance. A final report is expected after the official analysis concludes. How do RT-NMS systems prevent blackouts? Real-Time Network Management Systems (RT-NMS) help prevent blackouts by continuously monitoring energy production, transmission, and consumption across the grid. They collect data from sensors and devices, detect anomalies, and make automated decisions—such as rerouting power or activating reserves. Integrated with tools like SCADA, EMS, and DMS, they enable fast, remote response to disruptions. When paired with AI algorithms and predictive analytics, RT-NMS systems can even anticipate potential risks before they escalate. Their effectiveness depends on both smart software and access to physical resources like storage or backup power. What are the challenges of integrating renewable energy with power grids? Renewable energy sources like solar and wind are variable and less predictable than traditional power generation. This instability can cause frequency imbalances or sudden power drops, especially when clouds block sunlight or wind dies down. Without proper grid integration and fast-reacting systems, these fluctuations can threaten stability. Experts emphasize the importance of real-time monitoring, mechanical inertia, and predictive tools to absorb such disturbances. Poorly connected grids, like the one on the Iberian Peninsula, face additional challenges due to limited backup from neighboring networks. What technologies are needed to modernize energy infrastructure? Modern energy infrastructure requires advanced real-time IT systems—such as SCADA, EMS, and DMS—capable of detecting and responding to network anomalies within seconds. AI-driven forecasting tools enhance proactive risk mitigation, while fast communication protocols and low-latency telemetry ensure rapid data transfer. Physical assets like industrial batteries, fast-start gas turbines, and cross-border transmission lines are also critical. Integration with distributed energy resources (DERs) and energy storage systems increases flexibility and resilience. A combined digital-physical approach is key to supporting the renewable energy transition and preventing future blackouts.
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How to Measure ROI (Return of Investment) of Salesforce Implementation for SMB?
Every investment should generally pay off. But is this always the case? For this reason, companies that need to choose a CRM tool should have a comprehensive understanding of the potential profits and costs so that their decision is made consciously. Among decision-makers, the ROI indicator is a proven measure and guide for their decisions. Before we proceed to measure it in the context of a Salesforce implementation, let’s first analyse its definition. What is Return on Investment (ROI) and how do you calculate it? Return on Investment (ROI) is an indicator that measures the effectiveness of an investment or project. It is expressed as a percentage of profits divided by investment costs. To calculate ROI, divide the net profit from the project (profits minus costs) by the costs incurred for it and multiply by 100. ROI allows you to assess whether the investment was profitable and enables you to compare the effectiveness of different investments. The higher the percentage, the more profitable the investment. However, before using the above formula, it is necessary to carry out several steps that will enable us to precisely determine the components. Definition of goals and key performance indicators (KPIs): Be clear about your goals, whether it’s increasing sales efficiency, improving customer relationships, or streamlining your marketing efforts. Then identify key performance indicators (KPIs) in line with your goals, including lead conversion rates, improved sales productivity, and customer satisfaction metrics. Cost calculation: Determine and document all costs associated with your Salesforce implementation, including licensing fees, technical customization expenses, training, and ongoing support. Make a comprehensive list of costs – both one-off and recurring – to get a detailed picture of your expenses. Determining the benefits: Measure Salesforce’s impact on business processes by assessing improvement in identified KPIs. Where possible, define benefits in monetary terms – note the measurable and non-measurable benefits mentioned earlier. Then compare the profits from the period before the implementation of the CRM system to the period after the implementation to obtain a specific amount that can be used in the calculations. Subtracting costs from benefits: Calculate net gains or losses by subtracting total costs from total benefits. This way we get a clear picture of the financial situation. If we receive a positive result, it means a profitable investment for us. However, a negative result suggests that the costs outweigh the benefits. Application of the ROI formula: Use the ROI formula to determine your return percentage. The percentage received in our score reflects the return on investment. A higher percentage means a better return on our investment. Monitoring and optimization: Let’s keep in mind that the return on investment in Salesforce is a process spread over time. We must constantly monitor and repeatedly evaluate the impact of the Salesforce system, as well as optimize the implementation based on changing business needs and technological progress. Let’s also remember not to rely solely on the ROI formula and monitor non-measurable profits, which also constitute significant value for the company. Why is measuring ROI in Salesforce not obvious? In the context of Salesforce implementation in small and medium-sized enterprises (SMEs), calculating ROI is an important issue for the overall assessment of the effectiveness of a CRM solution. However, calculating ROI in Salesforce is not always as straightforward as we might hope. This complexity arises from the fact that profits can be divided into two groups, as detailed examples will illustrate below. Measurable Profits: Increasing the number of leads obtained from the campaign. Increasing the number of sales opportunities. Increasing sales revenue by improving sales opportunity management. Increasing conversions through more effective use of customer data. Saving time by automating processes and replacing repetitive and manual activities and administrative tasks. Cost control: better budget management and reduction of excess costs. Improved data analysis: better analysis of customer data and sales activities, which can lead to a better understanding of customer needs and better sales strategies. Increased customer loyalty: through better customer service and more personalized interactions, companies can gain greater customer loyalty, which translates into recurring revenue. Unmeasurable Profits: Improved customer relationships: Better customer service can lead to better customer relationships, which may be difficult to measure but are crucial to a company’s long-term success. Greater team effectiveness: A better customer relationship management tool can lead to better communication and team collaboration, which can improve the efficiency of the entire company. Greater consistency in data: Having one central system for managing customer data can result in greater data consistency and accuracy, which can improve the quality of business decisions. Increased customer trust: Better customer data management can lead to greater customer trust in your company, which can be key to acquiring new customers and retaining existing ones. Improving your brand image: Using a modern and effective CRM system can improve your brand image in the eyes of customers and business partners, which can impact your company’s overall reputation. Both measurable and non-measurable profits are important for the success of implementing a new Salesforce CRM system for SMEs. If you want to learn more about the benefits of implementing Salesforce in your company, read the material: https://ttms.com/what-is-crm/ The second part of the formula involves the costs incurred during the implementation of the project on the Salesforce platform. These costs are divided into two parts: Implementation Costs It consists of the work of specialists who adapt the platform to the individual needs of the company and processes specific to a given industry. Feel free to learn more about the process of implementing Salesforce in your company. Salesforce License Costs This category includes the costs of environment and user licenses, which are typically paid in advance for a fixed period. The link below provides an article where you can learn more about the process of implementing Salesforce in your company. Positive Effects of Salesforce Implementation Supported by Market Research Companies faced with the decision to choose Salesforce as their CRM platform are not entirely certain how this change will affect their business in the long run. Therefore, it is advisable to rely on reliable data from previously completed projects. According to research conducted by Salesforce in 2022 on a group of over 3,500 enterprises, 89% of companies achieved a positive ROI, averaging 29% after 9 months of implementing the CRM system. Additionally, over 85% of users of newly implemented systems expressed their willingness to continue using the new tool. Below, we present some additional data included in the ROI measured by Salesforce in the aforementioned study: 30% improvement in the speed of response in communication with customers, leads, and employees. 30% reduction in decision-making time. 30% increase in productivity of CRM system users. 28% increase in sales results. 27% increase in successful business opportunities. 28% reduction in the time to close sales opportunities. 29% increase in sales productivity. Source: https://www.salesforce.com/news/press-releases/2022/11/07/companies-report-cost-savings-with-salesforce/ Of course, these are not all the indicators measured by Salesforce, but the ones mentioned above. Research shows that most companies that have decided to implement Salesforce achieve measurable benefits, and their ROI reaches a very high value shortly after implementation. Calculation of ROI on the Selected Example Let’s make an example calculation of year-to-year profit growth (YOY) after implementing a new CRM tool. Let’s assume that profits increased by PLN 80,000 and the cost of the project to implement a new CRM tool was PLN 20,000. In this case, the ROI calculation will look like this: Final Conclusions Measuring ROI in Salesforce and maximizing the ROI from implementing a CRM system in SMEs is crucial for making informed business decisions. By setting clear goals, calculating costs, and determining benefits, a company can assess the effectiveness of its investment activities. Calculating Salesforce ROI and incorporating current industry insights streamlines the process, ensuring SMBs maximize their Salesforce implementation potential. To sum up, the journey of implementing a SaaS system in SMEs goes beyond the framework of the project itself. It involves continuous evaluation, optimization, and maximizing return on investment. Further information on optimizing Salesforce for business success will be regularly posted on our website www.ttms.com and related media. What is Return on Investment (ROI) and how is it calculated? ROI measures the efficiency of an investment by dividing the net profit by the costs and multiplying by 100. It helps assess the profitability and compare different investments. Why is measuring ROI for Salesforce implementation important? Measuring ROI helps determine the financial impact of Salesforce implementation, guiding informed business decisions. It highlights both measurable and non-measurable benefits, ensuring a comprehensive evaluation of the CRM system’s effectiveness. What are the steps to calculate ROI for Salesforce? The steps include defining goals and KPIs, calculating associated costs, determining both measurable and non-measurable benefits, subtracting costs from benefits to get net gains or losses, and applying the ROI formula to get the percentage. What are some measurable benefits of implementing Salesforce? Measurable benefits include an increased number of leads and sales opportunities, higher sales revenue and conversion rates, time savings through process automation, better budget management, improved data analysis, and increased customer loyalty. What are some non-measurable benefits of implementing Salesforce? Non-measurable benefits include improved customer relationships and trust, enhanced team communication and collaboration, consistent and accurate data management, and a better brand image and reputation. What are the typical costs involved in Salesforce implementation? Implementation costs include customization and specialist work, while licensing costs cover environment and user licenses, often paid in advance. How can TTMS help in preparing a Salesforce implementation plan? TTMS provides comprehensive needs assessments, strategic alignment with business goals, a user-centric approach, risk mitigation strategies, and continuous support. Their expertise ensures a successful and efficient Salesforce implementation tailored to your organization’s needs. What are the positive effects of Salesforce implementation according to market research? Research by Salesforce in 2022 showed that 89% of companies achieved a positive ROI, averaging 29% after nine months. Benefits included a 30% increase in response speed, 30% reduction in decision-making time, 30% boost in productivity, and a 28% rise in sales results.
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FAQ
What is software support for energy industry?
Software support for the energy industry provides ongoing maintenance, monitoring, security updates, and user assistance to keep critical energy applications reliable, compliant, and optimized for performance.
What are energy sector software solutions?
Energy sector software solutions are purpose-built tools for utilities, oil and gas, renewables, and grid operators that enable asset management, SCADA/IoT integration, predictive maintenance, field service, regulatory reporting, and real-time analytics.
What is software for energy industry?
Software for the energy industry includes platforms and applications that digitize operations—from data acquisition and forecasting to trading, scheduling, and risk management—improving safety, efficiency, and profitability.
What is custom energy software development?
Custom energy software development is the end-to-end design and build of tailored applications and integrations that match unique operational workflows, data models, and compliance needs across generation, transmission, and distribution.
What is software for power industry?
Software for the power industry focuses on generation, transmission, and distribution use cases, offering solutions like outage management, DER orchestration, grid analytics, demand forecasting, and market participation tools.