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As the world races to decarbonize and transition to cleaner energy sources, a quiet revolution is unfolding behind the scenes — one powered not by solar panels or wind turbines, but by algorithms. Artificial intelligence (AI) is rapidly emerging as a critical force in reshaping how we produce, distribute, and consume energy. From predicting demand spikes to optimizing renewable energy flows in real time, AI isn’t just enhancing the energy sector, it’s redefining its very foundation.  By 2030, innovations driven by AI are expected to contribute as much as $1.3 trillion in economic value with the potential to cut global greenhouse gas emissions by up to 10%. This reduction is comparable to the European Union’s total annual emissions. As machine intelligence evolves, it’s bringing new transformative solutions to the energy sector and solving some of our planet’s most pressing environmental challenges.

AI-powered predictive maintenance

One of the most transformative applications in the energy sector today is AI-powered predictive maintenance. Traditional energy infrastructure, such as wind turbines, solar panels, and drilling rigs, suffers from wear and tear that can lead to costly failures, repairs, and downtime. By applying AI to the massive data collected via IoT (Internet of Things) sensors, energy companies can monitor the health of their equipment in real-time, predicting failures before they occur or quickly deploying alternative solutions, without  the need for on-site operators. IoT refers to an interconnected network of physical devices, sensors, appliances, and machines that communicate and exchange data over the Internet, often autonomously and with minimal human intervention.

This is the strategy used at solar and wind energy installations where early detection and identification of potential equipment issues minimize the risk of costly failures and reputational damage from unexpected interruptions to operations. AI-driven predictive maintenance has been shown to reduce unplanned downtime by 35%, significantly enhancing operational efficiency and lowering maintenance costs. Additionally, companies implementing AI in their maintenance protocols have achieved up to a 30% reduction in maintenance expenses. By proactively monitoring equipment, AI not only improves the reliability of renewable energy systems but also contributes to more consistent and cost-effective energy production.

Revolutionizing smart grids and energy distribution

AI is playing a pivotal role in modernizing smart grids, significantly enhancing their efficiency and resilience. By analyzing vast amounts of real-time data, machine learning supports proactive automated grid management, allowing for the anticipation of electricity consumption trends as well as the streamlining of energy distribution process. This approach minimizes waste and reduces the likelihood of outages. AI also improves traditional grids by analyzing real-time data to balance supply and demand, buffering renewable energy into existing grids, making them smarter, more reliable, and robust.

According to the U.S. Department of Energy, machine learning is being used to support modernization of the grid. AI is being employed to forecast and mitigate grid disruptions caused by extreme weather events or cyberattacks, thereby ensuring a consistent power supply. Additional benefits of this approach are “cost-effectiveness and minimizing the impact of variability in renewable energy generation. This includes using AI to improve load forecasting and state estimation, even with limited or missing data.” 

In a significant industry collaboration, tech giants have partnered with major energy firms to form the Open Power AI Consortium, which aims to develop AI models and datasets tailored for the energy sector. The objective is to advance grid reliability, improve asset performance, and reduce operational costs, thereby advancing overall proficiency of electricity grids.

Optimizing battery performance

One of the challenges in renewable energy is source inconsistency—solar panels do not generate power at night, and wind turbines are ineffective without wind. Advanced AI is being leveraged to address these limitations by optimizing rechargeable batteries and guaranteeing a stable power supply. 

Electric vehicle manufacturers have integrated AI into their battery management strategies to improve battery performance and longevity—battery health predictions, maximizing charging cycles, extending the overall battery lifespan. Stanford University researchers have demonstrated the potential of AI models to predict lithium-ion battery lifespan with remarkable accuracy (up to 95%), “a feat previously impossible.”

By learning how batteries are typically used, AI systems can allocate energy more efficiently, thereby extending battery life for frequently used apps or systems and conserving power elsewhere. This adaptive approach not only improves performance but also enhances energy efficiency over time.

AI is proving essential in overcoming key challenges in battery performance, such as degradation from excessive charging, exposure to extreme temperatures, aging, and inconsistent usage patterns. By analyzing how these factors interact—along with environmental influences like ambient temperature, storage conditions, and operational load—AI can predict their effects on battery health and make dynamic adjustments to optimize performance.

Through processing large volumes of data, AI uncovers insights that are difficult to detect manually, enabling real-time control over variables such as charging speed, temperature, and energy distribution. This not only boosts reliability and efficiency but also extends battery life. Ultimately, the use of AI in battery management represents a major leap forward in making energy systems smarter, more cost-effective, and more environmentally sustainable.

Broadening the use of renewable energy

The financial commitment required to drive the global energy transition is immense. According to the International Energy Agency, meeting the net-zero emissions requirements by 2050 would require the annual investment in technology and infrastructure to reach $4 trillion by 2030. This investment is to be directed toward modernizing existing energy delivery systems, such as upgrading transmission and distribution grids, while also accelerating the adoption of renewable energy sources and advanced storage solutions. Fortunately, funding momentum is building—for instance, “the US Infrastructure Investment and Jobs Act (IIJA) and the cumulative $130 trillion commitment through the Glasgow Financial Alliance for Net Zero (GFANZ).”

The expansion of renewable energy sources over the past two decades has had a major financial impact, with 2023 alone seeing an estimated savings of over $400 billion in electricity sector fuel costs. This substantial reduction underscores how investing in clean energy not only supports environmental goals but also strengthens the resilience and stability of energy systems.

In addition to cost savings, AI is facilitating the rapid deployment of renewable energy projects by streamlining complex processes. AI tools are being developed to improve the way energy projects are sited and permitted, addressing challenges such as grid limitations, energy demand, and environmental impact assessments. These advancements enable quicker decision-making and proficient project execution, contributing to the accelerated adoption of clean energy solutions, bringing our society closer to a carbon-neutral future.

Trading and market dynamics

Energy markets are volatile, influenced by geopolitical instability, climate variability, and supply chain disruptions. In response to this volatility, energy trading firms are increasingly leveraging machine learning to improve decision-making and competitiveness.

Machine learning is transforming how energy position trades are executed by qualifying real-time analysis of massive and complex datasets—ranging from weather patterns and energy grid data to geopolitical events and commodity prices. These systems can identify patterns or anomalies faster and at a scale far beyond any manual human processes. As a result, energy companies are increasingly turning to AI and automation to make trading decisions, from forecasting short-term electricity prices to evaluating trading positions across multiple markets in milliseconds. The current evolution of algorithmic trading in power markets is evident in the European Energy Exchange (EEX), one of the leading platforms for energy trading in Europe. The exchange anticipates significant expansion in trading activity throughout 2025, driven by increased involvement from entities seeking to manage renewable energy risks and utilize its clearing services. In January, trading volumes for its main European power futures product surged by 37% compared to the same month last year, following a 63% rise in 2024.

By applying AI and intelligent process automation to energy trades, firms can respond quickly to shifting supply-demand balances and regulatory changes. The obvious results are not only faster and more accurate trades but also reduced risk exposure and higher profits. As energy systems become increasingly digitized and decentralized, smart trading agents are reshaping global energy markets.

The carbon footprint dilemma

While AI is driving sustainability gains across various sectors (including the energy sector), its own energy demands pose new challenges.

The unfolding narrative of generative AI comes at a price and the price is vast amounts of electricity consumed by growing compute resources and data centers. Powering AI queries, often requires significantly more energy than traditional digital tasks. This in turn can place significant stress on energy grids and derail the sustainability targets of major technology firms. “The global building boom of data centers — needed to meet the demand for generative AI — will likely emit the equivalent of 2.5 billion metric tons of carbon dioxide between now and the end of the decade. That total is comparable to 40 percent of annual U.S. emissions and will increase pressure on Silicon Valley to ramp up support for carbon-cutting technologies.”

This dual reality, AI as both a solution to and a driver of energy consumption, highlights the need for a strategic approach. To ensure AI remains a net-positive force for sustainability, businesses and policymakers must prioritize energy-efficient hardware, algorithmic optimization, and the integration of renewable energy sources in AI operations. Compared to conventional approaches, intelligent manufacturing systems have demonstrated the ability to decrease energy consumption, material waste, and CO₂ emissions by 30% to 50%, compared to legacy production methods. These efficiencies are achieved through advanced data analysis that pinpoints operational bottlenecks and optimizes real-time decision-making on and off of the factory floor.

If managed responsibly, AI has the potential not just to offset its own footprint but to serve as a catalyst for a more sustainable, greener global economy.

Conclusion

The role of AI in the energy sector is unquestionably poised to grow. With continual advancements in machine learning, automation, and predictive analytics, AI will create new standards and redefine how we generate, store, and consume energy.

As industries and governments invest in AI-powered innovations, we move closer to a world where energy is abundant, sustainable, and intelligently managed. This AI-driven energy revolution is not just a technological shift—it is the key to a smarter, more resilient, and greener future.

At Entefy, we are passionate about breakthrough technologies that save people time so they can live and work better. The 24/7 demand for products, services, and personalized experiences is compelling businesses to optimize and, in many cases, reinvent the way they operate to ensure growth.

To learn more, be sure to read our previous blogs about the inevitable impact of AI on businesses, the three phases of the enterprise AI journey, and the 18 essential skills needed to bring your AI applications to life.

ABOUT ENTEFY

Entefy is an enterprise AI software and hyperautomation company. Entefy’s patented, multisensory AI technology delivers on the promise of the intelligent enterprise, at unprecedented speed and scale.

Entefy products and services help organizations transform their legacy systems and business processes—everything from knowledge management to workflows, supply chain logistics, cybersecurity, data privacy, customer engagement, quality assurance, forecasting, and more. Entefy’s customers vary in size from SMEs to large global public companies across multiple industries including financial services, healthcare, retail, and manufacturing.

To leap ahead and future proof your business with Entefy’s breakthrough AI technologies, visit www.entefy.com or contact us at contact@entefy.com.

Machine learning (ML) and artificial intelligence (AI) continue to transform business operations worldwide—everything from intelligent automation to unlocking hidden insights for better decision-making. A recent study positions the long-term annual impact of AI between $2.6 trillion to $4.4 trillion across a series of corporate use cases. That said, for enterprises, implementing AI is neither easy nor simple. It requires proficiency in essential business, technology, and design skills to bring enterprise applications to life, from ideation to production implementation. Understanding where to begin, or more important, determining the right next steps in the AI journey is critical for success.

To kickstart AI transformations, leaders must first consider which phase on the AI journey continuum best describes their department or organization. The enterprise AI journey phases can be categorized as follows:

Early Phase: Learning, discovery, and AI readiness 

This stage is highly analytical, focused on assessing AI fit and laying the groundwork for modernization. At this point, it is important to educate the C-suite and other key stakeholders on AI as well as its strengths and limitations, resourcing needs, potential risks and rewards, plus practical, high-value use cases. This early phase includes the following, setting the foundation for informed decision-making and strategic alignment:

• Market discovery
• General team education
• Risk/reward sensing
• AI readiness
• Problem statements
• Solutioning options
• Data assessment
• Model evaluation

Mid Phase: AI/ML experiments, prototypes, and evaluation

Companies in this middle stage have moved beyond early exploration and are actively engaged in proof-of-concept (PoC) and prototype development as part of their initial AI/ML implementation. Here, an organization would engage a series of experiments and conduct evaluations to identify operational challenges, address stakeholder concerns, and perform early ROI assessments. Successfully navigating this phase requires mitigation of risks, refining AI models, and ensuring alignment with long-term business objectives. This mid phase includes:

• AI/ML feasibility considerations
• Exposing known unknowns
• Risk mitigation
• Early ROI assessment
• Limited adoption testing

Mature Phase: Production implementation and continual refinement

At this stage, AI is typically fully deployed, integrated into operations, and delivering tangible ROI. Organizations in this phase focus on continual optimization, fine-tuning AI-driven processes and automation, and ongoing innovation to establish competitive advantages. The goal is not just to simply maintain AI capabilities but to refine and expand them in order to improve operations as AI evolves. The mature phase of the AI journey includes:

• Productivity building
• Continual de-risking
• ROI optimization
• Technical differentiation
• Maintenance
• Perpetual refinements

After identifying the right phase, innovation and modernization will take center stage. It will take a collective effort within the organization to seize viable opportunities with AI. Below are three strategic ways to take advantage of those opportunities and utilize AI to drive optimizations across your enterprise.

1. Improved decision-making and insights

In virtually all organizations, making the right decision is fundamental for managers and business leaders. And now, more than ever, the need to make the right decisions is putting additional pressure on decision makers. “85% of business leaders have experienced decision stress, and three-quarters have seen the daily volume of decisions they need to make increase tenfold” over the recent years.

For enterprises, poor decision-making can hinder an organization’s growth, stability, and success. It can cause damage to a company’s reputation, lower employee morale, undermine strategic objectives, and result in financial losses. “Poor decision making is estimated to cost firms on average at least 3% of profits.” Many organizations struggle to extract meaningful value from their data using traditional analytics.

Advanced AI, on the other hand, gives employees the ability to make impactful data-driven decisions. AI can unearth hidden insights, which can help with:

• Cost reductions
• Enhanced customer experience and engagement
• Decreased customer attrition
• Fraud detection

2. Intelligent process and workflow automation

Deploying intelligent automation to gain time and resources has become a priority on enterprises on their AI journey. Employees often engage in repetitive tasks that, while necessary, can create bottlenecks and are prone to human error. By automating such processes, teams can redirect their focus toward higher-value initiatives which drive business growth.

Technologies such as robotic process automation (RPA) can handle tasks such as invoicing and payment processing with increased efficiency and accuracy. The recent surge in AI adoption and intelligent process automation (IPA) however, has further amplified the capabilities of process automation, enabling more complex and adaptive workflows.

Recently, the rise of Agentic AI represents a shift from traditional automation to AI systems capable of autonomous decision-making. Unlike other AI-powered systems, which tend to follow certain rules or heavily rely on human input and oversight, Agentic AI operates autonomously with minimal human supervision. Agentic AI relies on a proactive machine intelligence approach to iterative problem solving (from perception to reasoning), performing tasks, and continuous self-learning. Agentic AI systems come with sophisticated reasoning, independent decision-making, and the ability to adapt and take self-directed actions to solve multi-step problems. 

For enterprises, this means AI can go beyond simple task automation to proactively manage workflows and adapt dynamically to new conditions. For instance, in IT security, AI-driven security agents can detect and neutralize threats without human intervention, reducing response times and minimizing risks. For retailers, AI agents can be predict supply chain disruptions and autonomously adjust procurement and logistics strategies. In banking, AI-powered financial agents analyze risk factors, and detect fraud without manual human oversight.  

3. Team collaboration and communication

In today’s fast-paced business environment, optimizing team collaboration and communication is essential for enhancing productivity. As organizations continue to generate vast amounts of data, efficient management and access to this information becomes a critical driver of success. Next-generation communication tools and sophisticated knowledge management systems are central to improving how teams collaborate and share information.

For modern enterprises, information has become a powerful asset, often described as the new gold. The ability to make this information easily accessible, searchable, and shareable within an organization is fundamental to improving team performance. Companies often rely on complex knowledge management systems that house vast amounts of diverse data—structured, unstructured, or semi-structured data. These systems support collaboration by allowing employees to retrieve essential information quickly, fostering smoother communication and collaboration across teams. However, without the proper tools to navigate this wealth of information, teams struggle to find what they need, when they need it.

Advanced AI can help streamline access to diverse data types stored across various platforms, creating a unified search experience that spans everything from PDFs to spreadsheets, images, audio files, and even videos. This means that no matter where information resides within an organization, employees can quickly find relevant data and insights.

Natural language processing (NLP), a branch of AI focused on the interaction between computers and human language, aims to enable machines to read, understand, interpret, and generate text in a way that is meaningful. Today, the popularity of LLMs (large language models) are transforming the way machines interact with language. An LLM (a key technology used within the field of NLP), is a type of general-purpose language model pre-trained on massive datasets to learn the patterns of language. This training process often requires significant computational resources and optimization of billions of parameters. Once trained, LLMs can be used to perform a variety of tasks, such as generating text, translating languages, and answering questions. LLMs can also be used to improve written communication by summarizing documents, refining grammar, and enhancing tone. These functions boost clarity and help ensure that communication within teams is precise and professional, reducing misunderstandings and promoting more effective collaboration.

Furthermore, AI’s ability to analyze sentiments and emotions in digital communication plays a crucial role in enhancing team dynamics and external customer relations. By using sentiment analysis, AI tools can detect emotional cues in conversations, such as frustration, dissatisfaction, or enthusiasm. This is particularly useful for identifying and addressing potential issues within teams or with customers before they escalate. For example, AI can flag conversations where negative sentiment is high, allowing managers to intervene and de-escalate tense situations. Similarly, by understanding emotional triggers, organizations can tailor their communication strategies to boost employee morale, strengthen brand loyalty, and improve customer satisfaction.

Strategic considerations for AI implementations

After identifying key areas where AI can add value, consider the following questions:

• Which use cases can quickly provide value?
• Is there sufficient managerial or executive support for the intended use case(s)?
• Are ROI expectations realistic among stakeholders?
• Are changes required to the compute infrastructure for this purpose? This involves hardware and IT capacity planning.
• Do we have access to the right data for the intended use case(s)? In the exploration phase, consider how the 5 Vs of data can accelerate discovery and unlock hidden value.
• What are common missteps to avoid in implementing AI?
• Do we have the required skills internally or externally via vendors to make this a reality? It takes 18 separate skills to bring an AI solution to life from ideation to production level implementation.

Continuous learning and expert collaboration

AI is a vast and ever-evolving field. Familiarizing yourself with key AI terms and concepts is essential. Understanding the distinctions between traditional data analytics and the latest in AI including agentic AI, analytical AI, generative AI, and hyperautomation can prove highly valuable in assessing potential opportunities along your AI journey.

Embarking on this new path requires a strategic approach, focusing on specific business challenges and leveraging AI’s transformative potential to drive innovation and efficiency within your organization. On this journey, you may benefit by partnering with specialized AI firms and experts to accelerate the overall learning process for your team. These professionals can provide support in uncovering the narratives within your business, its data, and processes, and help you avoid common missteps.

It’s also important to ensure that any implementation of AI is compliant with ethical standards and the rapidly-evolving regulatory landscape. AI promises to reshape our experiences in ways both subtle and profound. Ideally, AI is developed and deployed responsibly, ethically, and in a manner that benefits humanity. Building trustworthy AI involves addressing various concerns such as algorithmic biases, data privacy, transparency, accountability, and the potential societal impacts of AI. At present, multiple corporate and governmental initiatives are underway to create ethical guidelines, codes of conduct, and regulatory frameworks that promote fairness, accountability, and transparency in AI implementations. 

For additional information, read our recent guide related to creating an effective corporate AI policy and our blog about the costly problems caused by legacy IT systems.

ABOUT ENTEFY

Entefy is an enterprise AI software and hyperautomation company. Entefy’s patented, multisensory AI technology delivers on the promise of the intelligent enterprise, at unprecedented speed and scale.

Entefy products and services help organizations transform their legacy systems and business processes—everything from knowledge management to workflows, supply chain logistics, cybersecurity, data privacy, customer engagement, quality assurance, forecasting, and more. Entefy’s customers vary in size from SMEs to large global public companies across multiple industries including financial services, healthcare, retail, and manufacturing.

To leap ahead and future proof your business with Entefy’s breakthrough AI technologies, visit www.entefy.com or contact us at contact@entefy.com.

The coming years will be defined by intelligent innovation driven by artificial intelligence (AI). As AI continues to advance, it will revolutionize how businesses operate, offering an unparalleled opportunity for organizations to enhance their decision-making, operations, and customer experiences. AI isn’t just a trend—it’s a transformative technology that has already begun reshaping entire industries. In this post, we explore how AI is redefining the future of business and why it’s critical for the C-suite to embrace the shift.

AI’s Inescapable Impact Across Sectors and Industries

In recent years, artificial intelligence has undergone a remarkable transformation, evolving from a niche technology largely confined to research labs and specific industries, to a business critical tool. Organizations worldwide, from small startups to large enterprises, are now leveraging AI to enhance their products and services, streamline operations, optimize supply chains, and automate routine processes and workflows. The ability of AI to process vast amounts of data and identify patterns that would be nearly impossible for humans to detect has made it indispensable in a number of areas including predictive analytics, decision-making, and customer personalization. As a result, companies are increasingly investing in AI-driven solutions to not only reduce operational costs or grow revenue but also gain a competitive edge, build resiliency, and enable data-driven innovation. 

AI is becoming integral to transforming traditional business models. For example, industries such as health care are utilizing AI to enhance diagnostic capabilities, streamline administrative processes, and even develop personalized treatment plans. Similarly, in financial services, AI-powered tools are being used to strengthen fraud detection, algorithmic trading, and risk management. In the retail industry is embracing AI for inventory optimization, demand forecasting, and personalized marketing strategies. These broad and diverse applications highlight the pervasive influence of AI in modern business operations. 

The global AI market, currently valued at nearly $300 billion, is expected to grow at an exponential rate—$1.8 trillion by 2030. This rapid growth underscores the growing importance of AI as a strategic investment for businesses seeking to stay competitive in an increasingly digital and data-driven world. As AI continues to mature and its capabilities expand, organizations will be required to navigate a complex landscape of ethical considerations, regulatory frameworks, and technological advancements. Nevertheless, the undeniable trajectory of AI’s integration into business operations signifies its status as an essential tool for future success. For example:

AI in Finance is already making waves with its ability to predict market trends and identify financial risks. Companies like Goldman Sachs and JPMorgan Chase have deployed AI systems to enhance trading strategies, automate trading decisions, and improve risk management. AI-driven predictive analytics tools enable financial institutions to gain valuable insights into consumer behavior, fraud detection, and investment strategies. 91% of financial firms have already implemented AI or have concrete plans to do so, highlighting the industry’s rapid embrace of intelligent automation and data-driven decision-making.

AI-powered predictive analytics tools are providing financial institutions with deeper insights into consumer behavior, enabling them to refine customer experiences and develop personalized offerings. AI models are also enhancing investment strategies by analyzing historical data, market sentiment, and macroeconomic indicators to predict future market movements, helping financial firms make smarter, data-driven investment decisions.

AI in Health Care is also taking off. In the U.S., health care is a massive, consequential sector encompassing two major industry groups: (i) health care equipment and services, and (ii) pharmaceuticals, biotechnology, and related life sciences. According to the Congressional Budget Office, “from 2024 to 2033, the CBO forecasts federal subsidies for health care will total $25 trillion, or 8.3% of GDP.” AI is revolutionizing health care by assisting physicians in diagnosing diseases, predicting patient outcomes, personalizing treatments, drug discovery and development, and much more.

A notable advancement in this field is the application of AI in medical image analysis. AI models can swiftly and accurately interpret complex imaging data, aiding in the early detection of conditions such as cancer and cardiovascular diseases. For instance, AI has been utilized to predict heart attacks with up to 90% accuracy, enabling timely interventions.

Moreover, AI-powered wearables are transforming patient monitoring by continuously tracking vital signs and alerting healthcare providers to potential health issues before they become critical. This proactive approach not only enhances patient outcomes but also alleviates the burden on healthcare systems.

These developments underscore AI’s pivotal role in modernizing healthcare, offering tools for more precise diagnoses, personalized treatments, and efficient patient monitoring.

AI in Retail is about adopting AI and intelligent automation to not only enhance personalization but also to create smarter, more efficient operational processes. AI-driven recommendation engines are transforming how brands predict customer preferences and tailor shopping experiences in real time. This level of personalization helps build deeper connections with consumers, driving loyalty and increasing conversion rates. AI is also playing a crucial role in inventory optimization, cost reductions, and demand forecasting, helping retailers minimize out of stock and overstock problems.

The retail industry and the consumer discretionary sector overall are moving toward intelligent automation. A report surveying over 400 retail industry professionals found that over 80% of retailers are actively integrating AI, with a strong focus on enhancing operational efficiency and personalization. Similarly, Fortune 500 retail executives revealed that 90% have initiated generative AI experiments, with 64% conducting pilots and 26% scaling solutions to optimize supply chains and customer interactions. Meanwhile, a 2025 US Retail Industry Outlook report states that AI-powered chatbots increased Black Friday conversion rates by 15%, while 60% of retail buyers credited AI tools with improving demand forecasting and inventory management in 2024. These developments underscore AI’s pivotal role in driving growth, efficiency, and competitive advantage in retail. 

AI in Energy is helping optimize grid management, improve renewable energy efficiency, and enhancing energy storage. AI-powered forecasting helps utilities forecast demand, prevent outages, and improve grid reliability. The Electric Power Research Institute (EPRI), alongside Nvidia and Microsoft, recently launched the Open Power AI Consortium to develop AI models that enhance energy management.

In a recent report, the U.S. Depart of Energy (DOE) has outlined “how AI can accelerate the development of a 100% clean electricity system.” This includes the overall improvement to grid planning with the use of generative AI on high-resolution climate data from the National Renewable Energy Laboratory. Another opportunity is the enhancement of grid resilience by using AI to help with diagnosis and response to disruptions. Additionally, there is the opportunity for discovery of new materials pertaining to clean energy technologies.

AI in the energy market is expected to grow from $19 billion last year to $23 billion this year. The size of this market is projected to grow to $51 billion by 2029,  at a 20.6% CAGR.

Why AI Has Become a Strategic Priority for C-Level Executives

As AI continues to transform industries across the globe, the question for businesses is no longer if they should adopt AI, but how effectively they can leverage it to stay competitive and relevant. 98% of CEOs say there would be some immediate business benefit from implementing AI and ML. Half of them acknowledge their organization is unprepared to adopt AI/ML due to lack of some or all of the tools, skills, and knowledge necessary to embrace these technologies.

AI’s transformative potential is far-reaching, reshaping operations by driving efficiency, automating complex tasks, and enabling data-driven decision-making at an unprecedented scale. In a recent survey, “78 percent of respondents say their organizations use AI in at least one business function, up from 72 percent in early 2024 and 55 percent a year earlier.” This widespread adoption is being further accelerated by the rapid rise of generative AI, with 71 percent of respondents now stating that their organizations utilize generative AI (in at least one business function). This figure is up from 65 percent earlier in 2024.

For C-suite executives, AI represents a fundamental shift in how businesses operate and compete. 64% of CEOs consider AI a top investment priority, and 76% of them do not envision AI fundamentally impacting job numbers. Leaders who fail to embrace AI run the risk of being outpaced by competitors who harness the power of AI to optimize workflows, enhance customer experiences, improve product development, and drive revenue growth. Organizations are making significant structural changes to harness the full potential of AI, with larger companies often leading the charge. In this rapidly evolving environment, the C-level executives must not only recognize the strategic importance of AI but also take an active role in its implementation.

In a recent cloud and AI business survey, 12% of those surveyed were highlighted as “Top Performers.” These businesses are already ahead of the curve, benefiting from their AI and cloud investments, and defining success. To take better advantage of AI, and generative AI more specifically, 63% of Top Performers are expanding their cloud budgets. The 88% of the companies participating in the survey (those not categorized as Top Performers) are seeing early returns too on their new AI investments. For instance, “41% say they’ve already seen improved customer experience through GenAI while 40% say they’ve already achieved increased productivity. Across each of the 10 categories we asked about, many companies say they’ve already achieved value — but Top Performers stand out because they’re 2X more likely than other companies to have done so.”

Preparing for the AI-Driven Future

Looking forward, AI will become more integrated in every aspect of business operations. AI, machine learning, and automation, including the fast evolving domains of Generative AI and Agentic AI, will continue to expand, creating new opportunities and challenges for executives. The companies that successfully implement AI will unlock new revenue streams, improve operational efficiency, and drive innovation.

The next decade will be defined by intelligent innovation driven by AI. The window of early AI adoption in business is closing fast and many organizations are feeling the competitive squeeze to include AI transformation on their roadmaps. Those who hesitate may find themselves outpaced by competitors who act early.

At Entefy, we are passionate about breakthrough technologies that save people time so they can live and work better. The 24/7 demand for products, services, and personalized experiences is compelling businesses to optimize and, in many cases, reinvent the way they operate to ensure resiliency and growth.

Begin your enterprise AI journey here and be sure to read our previous articles on key AI terms and the 18 skills needed to bring AI applications to life.

ABOUT ENTEFY

Entefy is an enterprise AI software and hyperautomation company. Entefy’s patented, multisensory AI technology delivers on the promise of the intelligent enterprise, at unprecedented speed and scale.

Entefy products and services help organizations transform their legacy systems and business processes—everything from knowledge management to workflows, supply chain logistics, cybersecurity, data privacy, customer engagement, quality assurance, forecasting, and more. Entefy’s customers vary in size from SMEs to large global public companies across multiple industries including financial services, healthcare, retail, and manufacturing.

To leap ahead and future proof your business with Entefy’s breakthrough AI technologies, visit www.entefy.com or contact us at contact@entefy.com.

In an era defined by rapid technological disruption, businesses that fail to evolve risk obsolescence. Artificial intelligence (AI) is no longer an emerging trend; it is the driving force behind the next wave of corporate transformation. While legacy systems have been the backbone of enterprise operations for decades, their inherent limitations are becoming a critical liability in today’s hyper-competitive landscape. Organizations that continue to rely on outdated technologies and infrastructure risk falling behind faster than ever before.

Legacy IT systems often lack the agility required to adapt to contemporary business demands, hindering the implementation of robust data governance frameworks. The failure of these initiatives can lead to compromised data quality, increased security vulnerabilities, and missed opportunities for leveraging data-driven insights. Furthermore, the inability to effectively manage and govern data impairs decision-making processes, ultimately affecting an organization’s competitiveness.

The workforce capable of maintaining aging systems is diminishing, as many original developers approach retirement. This demographic shift exacerbates the urgency for modernization. Collectively, these factors underscore the pressing need for organizations to transition away from legacy systems to more agile, efficient, and secure IT infrastructure. Beyond internal challenges, the reluctance to modernize IT infrastructure can erode stakeholder confidence. It’s important to secure stakeholder buy-in for data and analytics strategies, and aligning these initiatives with business objectives is crucial for success. Without modernization, organizations may struggle to demonstrate the value of data and analytics (D&A) projects, leading to diminished support from key stakeholders.

In essence, clinging to outdated systems not only escalates operational costs but also jeopardizes the organization’s strategic position in a data-centric world. To mitigate these risks, it is imperative for businesses to prioritize IT modernization, ensuring that data governance initiatives are both effective and aligned with overarching business goals.

The True Cost of Legacy IT Systems

Older IT systems, often built decades ago, have become significant impediments to organizational agility and innovation. A striking example is that approximately 70% of the software utilized by Fortune 500 companies was developed more than 20 years ago. These outdated IT systems present several critical challenges. These systems tend to:

• Increase operational costs: Maintaining legacy systems is financially burdensome. It generally leads to higher maintenance and support costs. Legacy systems demand significant financial and operational resources due to specialized skill requirements, frequent downtime, and ongoing fixes. As these systems age, finding professionals with the necessary expertise becomes increasingly difficult, often leading to higher wages and recruitment challenges.
  
  Additionally, outdated infrastructure is more prone to inefficiencies and breakdowns, causing disruptions that impact productivity and revenue. Over time, the cost of frequent patches, repairs, and workarounds continues to rise, making legacy systems a costly burden that diverts resources from innovation and modernization efforts.

• Create innovation stagnation: The use of obsolete programming languages and architectures hampers an organization’s ability to adjust to competitive changes, integrate new technologies, and innovate effectively.
  
  Outdated technology often lacks the flexibility to integrate modern features, applications, or emerging technologies, making it difficult to adopt new digital strategies. As a result, businesses may struggle to keep pace with evolving customer expectations and industry advancements. Additionally, the inability to quickly adapt to market shifts or launch new products and services can lead to missed revenue opportunities, allowing more agile competitors to gain an advantage. In a fast-changing digital landscape, clinging to outdated IT systems can impede long-term growth and market relevance.

• Jeopardize security and compliance: Outdated IT systems pose significant security and regulatory challenges, increasing the risk of cyber threats and compliance failures. Without regular updates and security patches, cyber systems become vulnerable to attacks, potentially leading to data breaches, financial losses, and reputational harm. Additionally, as regulatory requirements evolve, vintage systems may struggle to meet new compliance standards, exposing companies to legal penalties and eroding trust with customers, partners, and regulators. Failure to modernize in this regard can leave organizations vulnerable to both security threats and costly regulatory consequences.

• Threaten stability and resiliency: Limited automation and a heavy reliance on manual processes within legacy systems create significant vulnerabilities, which can lead to operational inefficiencies and increased risk of errors. Legacy IT systems often lack the advanced tools and capabilities required to streamline tasks, causing dependence on human intervention for routine operations. As a result, they become prone to instability, as manual inputs are more susceptible to inconsistencies and mistakes. Additionally, troubleshooting and resolving issues within such systems can grow more complex with time, making problems harder to diagnose. This further entrenches inefficiencies, leaving organizations vulnerable to prolonged downtimes and costly disruptions.

• Reduce agility and flexibility. Legacy IT systems can slow down a company’s ability to adapt to changing market conditions and customer expectations. Making modifications or adding new features often involves complex, time-consuming processes, delaying time to market and reducing responsiveness to industry shifts. Additionally, these systems were designed for specific use cases, making them rigid and difficult to change as business needs evolve. This lack of flexibility creates operational inefficiencies, limits innovation, and prevents organizations from capitalizing on emerging opportunities in a fast-moving digital landscape.

• Cause scalability challenges. Another challenge with legacy IT systems is the struggle to support growing business demands, leading to performance bottlenecks and operational inefficiencies. Many technologies were built for a different era of computing and not well suited to handle expanded workloads, resulting in slower processing speeds, data congestion, or system failures under heavy use. Additionally, scaling these systems to accommodate higher transaction volumes, changing user demands, or data growth can be complex and costly. Unlike modern cloud-based solutions designed for seamless scalability, legacy infrastructure can delay expansion and limit an organization’s ability to adapt to evolving business needs.
  
  
• Produce inefficiencies and poor user experience. Outdated interfaces and systems often require employees to navigate cumbersome processes, leading to slower workflows, increased frustration, and longer training times. For customers, aging technology often leads to sluggish online experiences, inefficient customer service operations, and limited functionality, ultimately harming the brand’s reputation and driving customers toward more agile competitors with better digital experiences.

• Raise the risk of obsolescence. Relying on outdated IT systems increases the risk of falling behind as technology evolves. When vendors phase out support for older software and hardware, businesses struggle to find technical assistance, leaving them vulnerable to security risks, system failures, and rising maintenance costs. Companies that fail to modernize also risk losing their competitive edge to more agile competitors who leverage advanced technology to enhance efficiency, improve customer experiences, and innovate faster.

While avoiding modernization may appear to save on capital expenditures in the short term, holding on to legacy IT systems can hurt an enterprise’s operations and competitiveness in the mid-long term.

The Competitive Advantages of AI Transformation

Many enterprises are embarking on AI transformation to get ahead of competition and future proof their business. AI is reshaping the way we think about operations and efficiency.

AI is poised to significantly bolster the global economy in the near future, contributing approximately $20 trillion to the global economy by 2030. This would represent 3.5% of global GDP. This substantial economic impact is anticipated to result from AI’s influence across various fields, including contact center operations, translation, accounting, and machinery inspection.

By 2038, companies adopting generative AI at scale could unlock over $10.3 trillion in additional economic value. The number of AI initiatives focused on driving business growth is expected to increase six-fold this year, with growth and expansion becoming the dominant goals for AI in 67% of companies by 2029. Further, 69% of executives believe AI brings new urgency to reinvention, affecting how technology systems and processes are designed, built, and operated.

The International Monetary Fund (IMF) highlights that AI’s integration into various industries could affect nearly 40% of global employment, with advanced economies facing significant transformations. The IMF emphasizes the need for proactive measures to harness AI’s benefits while mitigating potential challenges, such as job displacement.

AI transformation provides businesses, especially SMEs and large enterprises, a multitude of competitive advantages that enhance efficiency, drive innovation, and foster growth. One of the most significant benefits is improved decision-making, as AI can analyze vast amounts of data and deliver valuable insights, allowing businesses to make more informed and accurate choices. Predictive analytics also empowers organizations to anticipate market trends and customer behavior, enabling proactive decisions that capitalize on emerging opportunities.

Advanced machine intelligence enhances operational efficiency by surfacing real insights in voluminous, complex datasets and automating repetitive tasks and optimizing processes, reducing costs, and streamlining workflows. This leads to better resource utilization and the ability to scale operations without significant increases in overhead.

Another critical advantage is the ability to deliver personalized customer experiences. Using machine learning, AI-powered systems can analyze individual customer preferences and tailor products, services, and marketing strategies to create more meaningful interactions. With tools like intelligent chatbots, businesses can offer round-the-clock customer support, improving satisfaction and loyalty.

Through AI-driven insights, companies can bring products to market faster and create offerings that meet the evolving demands of customers. In this regard, AI fosters innovation by accelerating product development and enabling businesses to explore new business models that weren’t previously feasible.

In addition, machine intelligence allows businesses to reduce costs while scaling operations efficiently. As organizations grow, AI helps manage larger volumes of data, customers, and operations without a proportional increase in resources. AI also facilitates better risk management, from detecting fraud to predicting market risks, helping businesses minimize financial loss and mitigate potential threats. AI-powered applications can provide competitive intelligence by monitoring industry trends, competitor activities, and customer sentiment, giving businesses an edge in adjusting their strategies accordingly.

Talent management also benefits from AI tooling and automation, as businesses can leverage AI to optimize recruitment, employee performance, and career development, ensuring they have the right talent in place to meet business goals. Machine learning contributes to sustainability and compliance by automating regulatory checks and helping businesses adhere to evolving standards. In industries focused on sustainability, AI can optimize resource usage, minimize waste, and improve supply chain management, addressing both environmental concerns and operational costs.

The Future Is AI-Driven—Are You Ready?

By 2027, 80% of data and analytics governance initiatives are projected to fail due to the absence of a real or perceived crisis. This alarming statistic underscores the critical need for proactive IT modernization. 

The trajectory is clear: AI is not merely an enhancement but the foundation of future-ready enterprises. Organizations integrating AI into their core strategies are poised to gain a decisive competitive advantage, while those clinging to vintage IT systems risk obsolescence. The question is no longer if AI will reshape your industry, but how—and whether your organization will lead this transformation or struggle to catch up.

In a world where agility and innovation define success, legacy IT systems are liabilities, not assets. Is your company ready to embrace AI’s potential to redefine efficiency, strategy, and growth?

At Entefy, we are passionate about breakthrough technologies that save people time so they can live and work better. The 24/7 demand for products, services, and personalized experiences is compelling businesses to optimize and, in many cases, reinvent the way they operate to ensure resiliency and growth.

Begin your enterprise AI journey here and be sure to read our previous articles on key AI terms and the 18 skills needed to bring AI applications to life.

ABOUT ENTEFY

Entefy is an enterprise AI software and hyperautomation company. Entefy’s patented, multisensory AI technology delivers on the promise of the intelligent enterprise, at unprecedented speed and scale.

Entefy products and services help organizations transform their legacy systems and business processes—everything from knowledge management to workflows, supply chain logistics, cybersecurity, data privacy, customer engagement, quality assurance, forecasting, and more. Entefy’s customers vary in size from SMEs to large global public companies across multiple industries including financial services, healthcare, retail, and manufacturing.

To leap ahead and future proof your business with Entefy’s breakthrough AI technologies, visit www.entefy.com or contact us at contact@entefy.com.

Artificial intelligence (AI) is among the top technologies transforming the world. Businesses and governments are quickly realizing the incredible value of AI in improving workforce productivity, automating processes and tasks, reducing costs and waste, optimizing customer engagement, advancing research, and much more.

That said, implementing AI in an enterprise can be a complex and cost intensive endeavor. Successful implementations of AI require a diverse set of skills, spanning technical expertise, business acumen, and strategic thinking. That’s because creating intelligent applications and systems using machine learning takes time and expertise. This includes the skills needed to gather and prepare data, design systems, choose and train the right models, test performance, create workflows, manage databases, develop software, ensure security and data privacy, and so on.

At Entefy, we’re obsessed with machine intelligence and its potential to change lives for the better. Our deep experience in advanced AI and intelligent automation, coupled with numerous inventions in the field, has enabled us to collaborate with exceptional individuals and create systems that dramatically improve operational efficiency.

In our experience, successful AI implementations rely on 18 distinct skills. The infographic below highlights each of those skills that move AI projects from ideation to production implementation.

To begin your enterprise AI journey click here and be sure to read our previous articles on key AI terms and how to build a winning AI strategy with the 5 Vs of data.

ABOUT ENTEFY

Entefy is an enterprise AI software and hyperautomation company. Entefy’s patented, multisensory AI technology delivers on the promise of the intelligent enterprise, at unprecedented speed and scale.

Entefy products and services help organizations transform their legacy systems and business processes—everything from knowledge management to workflows, supply chain logistics, cybersecurity, data privacy, customer engagement, quality assurance, forecasting, and more. Entefy’s customers vary in size from SMEs to large global public companies across multiple industries including financial services, healthcare, retail, and manufacturing.

To leap ahead and future proof your business with Entefy’s breakthrough AI technologies, visit www.entefy.com or contact us at contact@entefy.com.

The world is witnessing a technological renaissance, fueled by rapid advancements in artificial intelligence (AI). The quickly evolving sub-disciplines of AI, namely Analytical AI, Generative AI, and Hyperautomation, are converging to create a powerful force of innovation. These technologies, especially when correctly combined, create powerful synergies and are reshaping industries across the board.

Analytical AI: The power of insights

With its focus on extracting insights from vast datasets through advanced algorithms, statistical models, and machine learning techniques, Analytical AI provides foundational capabilities for understanding data in its various forms. It allows us to identify patterns, predict outcomes, and make data-driven decisions.

Generative AI: Reimagined Creativity

As a subset of machine learning consisting of deep learning models, Generative AI can create new, high-quality content based on what it has learned from its training data. It pushes the boundaries of what’s possible by creating new content and experiences—from crafting compelling narratives to generating realistic images and even composing music. Generative AI is transforming fields like art, entertainment, software, and even scientific research.

Hyperautomation: The next wave of efficiency

By expanding the depth and breadth of the traditional, narrowly-focused automation, Hyperautomation helps organizations systematically identify and automate business and IT processes. It relies on orchestration (and intelligent automation) of multiple technologies and tools. The goal with Hyperautomation is to maximize operational efficiency, agility, and ultimately, achieve significant business outcomes. Hyperautomation is becoming increasingly sophisticated, capable of handling complex decision-making and even adapting to changing circumstances.

The infographic below highlights the key differences between Analytical AI, Generative AI, and Hyperautomation, including focus, purpose, and best practices for each.

The true power lies in the convergence of these technologies. When correctly implemented, these three AI paradigms can transform enterprises, large and small. As these technologies continue to evolve, we can expect to see increased automation of knowledge work, personalized experiences at scale, and unprecedented levels of efficiency and productivity across various sectors.

However, this rapid advancement also necessitates a responsible approach. Careful consideration must be given to ethical implications, data privacy, and the potential impact on the workforce to ensure that these technologies benefit businesses while protecting society as a whole.

To learn more, begin your enterprise AI journey here and be sure to read our previous articles on key AI terms and how to build a winning AI strategy with the 5 Vs of data.

ABOUT ENTEFY

Entefy is an enterprise AI software and hyperautomation company. Entefy’s patented, multisensory AI technology delivers on the promise of the intelligent enterprise, at unprecedented speed and scale.

Entefy products and services help organizations transform their legacy systems and business processes—everything from knowledge management to workflows, supply chain logistics, cybersecurity, data privacy, customer engagement, quality assurance, forecasting, and more. Entefy’s customers vary in size from SMEs to large global public companies across multiple industries including financial services, healthcare, retail, and manufacturing.

To leap ahead and future proof your business with Entefy’s breakthrough AI technologies, visit www.entefy.com or contact us at contact@entefy.com.

Artificial Intelligence (AI) has become a cornerstone of innovation across various industries, including financial services. AI is expected to change the future of money and finance with its ability to generate predictive insight, enhance risk management, enable true personalization, reduce bias in lending, power hyperautomation, and drive efficiency in markets. AI’s influence also extends to blockchain technology and decentralized finance (DeFi), optimizing cryptocurrency trading, improving security, and enabling smart contracts. Its ability to process vast amounts of data and learn from patterns is transforming how financial markets function, leading to more accurate predictions and faster, data-driven decision-making.

While AI solutions in trading, risk management, fraud detection, customer service, and knowledge management are experiencing rapid adoption in the financial sector, other applications in AI such as those used in wealth management and lending are being adopted at a slower pace.

Rapid Adoption of AI Applications within the Financial Sector

AI’s rapid integration into the financial sector is driven by its potential to optimize operations, reduce costs, minimize human errors, and enhance scalability. According to McKinsey, generative AI alone is estimated to add “between $2.6 trillion to $4.4 trillion annually” to the economy and could deliver an additional $200 billion to $340 billion if fully implemented in the banking industry.

The financial sector, known for its data-intensive operations, is adopting AI in several key areas, including:

Trading

AI has significantly impacted trading through enhanced analysis of historical data, market conditions, and economic indicators, all of which aid traders in making more informed decisions. Predictive models developed through machine learning can forecast future market trends and price movements. AI allows traders to crunch data like they never have, allowing them to adapt to real-time data and adjust their strategies based on market changes. Traders also use AI to better understand market sentiment. Specific machine learning models can analyze news articles and social media posts to gauge market sentiment to inform trading strategies. Algorithmic trading strategies leverage predefined rules and quantitative analysis techniques, such as regression analysis, to automatically execute trades based on market data and identified patterns. High-frequency trading and arbitrage strategies are common approaches within algorithmic trading, allowing traders to capitalize on market inefficiencies and small price movements at high speeds.

Implementations in AI have also led to the creation of trading robots and robo-advisors. These digital technologies provide automated, algorithm-driven financial planning and investment advisory services with little to no human supervision. Clients can activate their automated trading robots by answering a few questions pertaining to their current finances, financial goals, and risk tolerance. Those answers are then used to construct a portfolio made primarily of a suite of low-fee exchange-traded funds (ETFs). The technology also allows customers to plan and set up taxable brokerage accounts or tax-advantaged individual retirement accounts (IRAs). Through the power of AI and machine learning, the platforms automatically rebalance portfolios and maximize tax-loss harvesting.

Risk Management

In risk management, AI systems help companies better understand risks and how to mitigate them. These systems can evaluate structured and unstructured data as well as patterns in such data to build risk assessment models. These models use historical data to evaluate the likelihood of various outcomes and help risk managers make informed decisions, knowing the potential risks and opportunities, and developing strategies for managing them. AI can also help with stress testing, subjecting strategies to various market scenarios to see how they may perform under different conditions. By summarizing customer information, AI can accelerate lenders’ end-to-end credit process. Financial institutions are using generative AI to create credit risk reports and extract customer insights from credit memos. With machine intelligence, financial institutions can analyze credit data, gain visibility into customers’ risk profiles, and generate default and loss probability estimates through models. Utilizing these risk assessment models allows companies to be better prepared for unexpected market conditions and improve their chances of success. With AI continually learning and adapting to new risk factors, the technology is poised to stay up to date with emerging threats, offering unprecedented protection for financial institutions and their customers.

Fraud Detection

AI’s ability to detect fraud patterns is reaching new heights. Today, advanced models analyze underlying behaviors and characteristics associated with fraud, identifying abnormal patterns, such as unusually high transaction amounts, and frequent purchases in unusual locations or strange time intervals between transactions. Behavioral analytics are also being employed to immediately detect fraudulent purchases. The U.S. Department of Treasury announced the recovery of more than “$375 million as a result of its implementation of an enhanced fraud detection process” powered by AI technology. Mastercard’s AI-based fraud detection platform, “Decision Intelligence,” reviews customer spending patterns and determines the likelihood of fraud for each transaction as it occurs. This has already helped them “score and safely approve 143 billion transactions a year.” Their next generation technology, “Decision Intelligence Pro,” enhances decision intelligence scores in under 50 milliseconds. According to Mastercard’s initial modeling, the adoption of AI technology has led to an average increase in fraud detection rates by 20%, with improvements reaching up to 300% in certain cases. The rapid adoption of AI and machine learning in fraud detection has allowed financial service companies the opportunity to revolutionize their ability to protect their customers from fraudulent transactions and other scams.

Customer Service

AI-powered chatbots are designed to provide 24/7/365 customer service, streamlining customer interactions and reducing costs. As of 2023, all of the top 10 largest commercial banks had integrated chatbots into their customer service operations. Institutions such as Capital One have built their own chatbot technologies with models trained on data from real customer conversations and chat logs. Bank of America introduced its chatbot, Erica, in 2018, and by October 2022, it had engaged with almost 32 million customers across more than 1 billion interactions. In 2022, roughly 37% of the U.S. population (more than 98 million people) interacted with a bank’s chatbot and, by 2026, this figure is expected to expand to 111 million. AI chatbot’s 24/7 availability is a powerful advantage in the world of finance. Studies indicate that for financial institutions, chatbots, when used instead of human customer service agents, “deliver $8 billion per annum in cost savings, approximately $0.70 saved per customer interaction.” Over time, AI powered tools are expected to further improve customer service, pave the way for more personalized financial advice, streamline operations, and reduce costs.

Knowledge Management

One of the most important assets within an enterprise is their knowledge base. This knowledge base is a treasure trove of information but can be notoriously difficult to manage. AI can dramatically improve document and knowledge management across organizations. As employees leave a company, preserving institutional knowledge becomes crucial. AI systems like JP Morgan Chase’s DocLLM categorize financial documents, ensuring that the valuable insights and processes are not lost and instead passed on seamlessly to new generations. This continuity is essential for maintaining organizational memory and fostering ongoing success.

Nearly half of digital workers depend on information to do their job properly and, according to a Gartner survey, “47% of digital workers struggle to find the information needed to effectively perform their jobs.” By utilizing natural language processing (NLP) and other machine learning techniques, companies are now providing their employees with a more precise and efficient knowledge management solution. These advanced technologies enable cost-effective processing and categorization of vast amounts of data, providing users a better way to find information and complete tasks.  

AI Applications that are experiencing slower adoption

Despite the rapid advancements in certain areas of finance, AI adoption in wealth management and lending is lagging. These areas face unique challenges that slow down the widespread implementation of AI technologies.

The slower adoption of these AI applications is due to several factors including fast-evolving governmental regulations that require financial institutions to understand and explain each of their decisions, thereby complicating AI implementation. Further, concerns about deep fakes, data quality, and misinformation introduce new hurdles for AI integration. AI models trained on inaccurate data can produce incorrect outputs, leading to an erosion of trust. In addition, many organizations struggle with managing data privacy and security, particularly when deploying AI systems in real-world use cases.

Wealth Management

Traditionally a domain heavily reliant on human advisors, wealth management is slowly but surely integrating AI. Even with the growing popularity of robo-advisors and other automated investment tools, a significant number of clients continue to favor traditional investment advisory services. Many financial advisors utilize AI technologies in preparation for client meetings, but a complete transition to full automation has yet to take place. “The experience of the finance industry suggests that human-facing services where data is not abundant and fast-changing can remain largely intact in a world of AI.” This preference is driven by the personalized feel and service human advisors provide to their clients. High-net-worth individuals, who often seek more personalized financial advice, remain the most resistant to fully automated investment services. They favor tailored strategies and trust that comes with human advisors. Although AI technologies are valuable for analyzing data before a financial advisor meets with a client, they can fall short of providing the emotional intelligence needed to establish trust and build rapport. For now, the goal in wealth management remains to find the right balance between leveraging powerful AI capabilities and maintaining the personal touch that clients highly value.

Lending

When it comes to AI, lending is another area within the finance sector that is still catching up. AI’s role in automating lending is made more complicated by the continual need for monitoring and updating data to ensure fair lending compliance. Lending decisions made by black-box AI models, creates interpretation challenges. Those models are so complex that its decision-making or internal processes cannot be easily explained by humans, thus making it challenging to assess how the outputs were created.

Regulatory compliance and potential data biases also slow down the adoption of customer-facing AI applications in lending. As Federal Reserve Governor Lael Brainard described the problem: “Depending on what algorithms are used, it is possible that no one, including the algorithm’s creators, can easily explain why the model generated the results that it did.” Using AI in lending carries risks, as transparency is crucial for preventing discrimination, ensuring fairness, and fulfilling disclosure requirements. “Data-driven algorithms may expedite credit assessment and reduce costs, but they also carry the risk of disparate impact in credit outcomes and the potential for fair lending violations.” The lack of explainability and the need to follow fair lending guidelines have slowed down AI’s complete integration into lending. However, explainable AI (XAI), a set of tools and techniques that helps people understand and trust the output of machine learning algorithms, can help address the crucial need for decision clarity and transparency in lending.

Conclusion

As analytical AI, generative AI, and hyperautomation continue to evolve, they promise to create a more efficient, secure, and accessible financial ecosystem, redefining the role of traditional institutions and expanding opportunities for both consumers and businesses. While strict regulatory compliance and data quality concerns have slowed AI’s full implementation into the financial sector, the technology continues to offer significant benefits in back-office automation, data aggregation, trading, customer service, predictive analytics, fraud prevention, and knowledge management. Despite its challenges, AI’s transformative potential in the world of finance can’t be ignored.

Begin your enterprise AI journey here and be sure to read our previous articles on key AI terms and how to build a winning AI strategy with the 5 Vs of data.

ABOUT ENTEFY

Entefy is an enterprise AI software and hyperautomation company. Entefy’s patented, multisensory AI technology delivers on the promise of the intelligent enterprise, at unprecedented speed and scale.

Entefy products and services help organizations transform their legacy systems and business processes—everything from knowledge management to workflows, supply chain logistics, cybersecurity, data privacy, customer engagement, quality assurance, forecasting, and more. Entefy’s customers vary in size from SMEs to large global public companies across multiple industries including financial services, healthcare, retail, and manufacturing.

To leap ahead and future proof your business with Entefy’s breakthrough AI technologies, visit www.entefy.com or contact us at contact@entefy.com.

Recently, Ed Grier, Dean of Santa Clara University’s Leavey School of Business, moderated a panel featuring business leaders Nick Chong, Chief Customer Officer at Zoom, and Brienne Ghafourifar, Co-founder of Entefy. The panel session was part of AACSB’s (Association to Advance Collegiate Schools of Business) AI Conference, focused on preparing business leaders. This panel’s discussion shed light on the indispensable role of partnerships between universities and businesses (particularly technology businesses) in fostering meaningful dialogue, driving innovation, and enhancing the educational experience for students. AACSB accredits the top business schools in the world.

A key segment of the discussion centered on the strategic use of advanced technologies, such as machine learning and AI, to better prepare students for their academic and professional journeys. The panelists stressed the importance of integrating cutting-edge technology into the curriculum to equip students with the skills and knowledge required to thrive in an increasingly digital and automated world. By incorporating AI and other technological advancements into the educational framework, universities can better ensure success for their students during their academic tenure and beyond.

The conversation delved into the significance of fostering a culture of innovation within academic institutions. This involves creating an environment that encourages creative thinking, problem-solving, and interdisciplinary collaboration. The panelists highlighted the necessity of providing students with the tools and resources needed to develop as well-rounded leaders, capable of navigating and excelling in various fields. By nurturing cross-disciplinary strengths, universities can produce graduates who are versatile, adaptable, and well-equipped to meet the dynamic challenges of the modern workforce.

Overall, the panel discussion underscored the importance of business and technology partnerships in shaping the future of education. By aligning academic goals with industry needs, universities can play a pivotal role in developing the next generation of leaders who are prepared to drive innovation and success in their respective fields.

ABOUT ENTEFY

Entefy is an enterprise AI software and hyperautomation company. Entefy’s patented, multisensory AI technology delivers on the promise of the intelligent enterprise, at unprecedented speed and scale.

Entefy products and services help organizations transform their legacy systems and business processes—everything from knowledge management to workflows, supply chain logistics, cybersecurity, data privacy, customer engagement, quality assurance, forecasting, and more. Entefy’s customers vary in size from SMEs to large global public companies across multiple industries including financial services, healthcare, retail, and manufacturing.

To leap ahead and future proof your business with Entefy’s breakthrough AI technologies, visit www.entefy.com or contact us at contact@entefy.com.

The digital age has ushered in an era of unprecedented data generation and management. From social media to smart sensor networks, to AI-powered systems, information flows at an ever-increasing rate. This creates a vast ocean of data that together with technology powers modern society. To navigate the complexities of today’s data for business and harness its transformative potential, we need to better understand data’s characteristics and why they matter. This is where the 5Vs of data come in:

1. Volume: Quantity Matters

Data Volume refers to the colossal and ever-increasing amount of information generated in today’s digital world. Traditional data management systems, designed for a bygone era of smaller, meticulously organized datasets, are simply overwhelmed by the sheer scale of big data.

Imagine a filing cabinet meant for neatly organized folders being flooded with a tidal wave of documents, logs, images, and videos—communication and social media posts (from emails to shared memes), sensor readings from billions of connected Internet of Things (IoT) devices, and machine-generated logs streaming in at an unrelenting pace. For many large organizations, this data deluge is measured not in megabytes or gigabytes but in terabytes (a trillion bytes), petabytes (a quadrillion bytes), and even exabytes (a mind-boggling quintillion bytes). The challenge lies not just in storing this vast ocean of information, but also in analyzing it efficiently to extract valuable insights.

Traditional methods simply struggle to keep up with the ever-growing volume of data, necessitating the development of innovative storage solutions, high-performance computing architectures, and scalable data processing techniques.

Question to ask when assessing data Volume at your organization: Is there sufficient data to properly support the target use case and the business objective?

2. Variety: A Tapestry of Data Formats

Data Variety refers to the diversity in data in terms of formats and structures. It encompasses not only the familiar structured data found in relational databases but also semi-structured and unstructured data.  

Structured data such as financial records, contact lists, or inventory control data, refers to data that has been organized using a predetermined model, often in the form of a table with values and linked relationships. Semi-structured data occupies a middle ground between the rigidity of structured data and the free-flowing nature of unstructured data. Examples of semi-structured data include log files, emails, social media posts. Unstructured data, the largest and most challenging category, is the wild west of big data. It refers to information that lacks a predefined format or organization, including free form text, images, videos, and audio files.

The variety of data formats poses a significant challenge for traditional data analysis tools designed solely for structured data. Extracting insights from unstructured data requires specialized techniques such as natural language processing (NLP) for text analysis, computer vision for image analysis, and audio signal processing for audio files.

Question to ask when assessing data Variety at your organization: To what extent does the data contain different types of objects or formats?

3. Veracity: Ensuring Data Quality and Trustworthiness

Data Veracity underscores the importance of data quality and reliability. It refers to the accuracy, completeness, consistency, and trustworthiness of the information being processed. Just as a shaky foundation compromises the integrity of a building, poor data Veracity undermines the reliability of any insight derived from data. Veracity is crucial to avoid false conclusions and, ultimately, poor decision-making.

The key pillars of data Veracity include accuracy, completeness, consistency, and trustworthiness. Accuracy refers to the degree to which data reflects the true state of the world it represents. Inaccurate data, whether due to errors in data entry, faulty sensors, or biased sources, may lead to misleading results. Completeness in data has to do with the holistic picture. Missing values or incomplete datasets can skew analysis and limit the potential for true insights and complete data is one that has no essential information missing. Consistency ensures that data adheres to predefined standards and formats. Inconsistencies, such as variations in units of measurement or date formats, can create confusion and hinder analysis. Trustworthiness refers to the source and lineage of data that are crucial for establishing trust. Data from unreliable sources or those with unclear origins can lead to misleading or confusing results.

Organizations can implement various strategies to safeguard data veracity. These strategies include a) Data Quality Management that establishes data quality checks and procedures throughout the data lifecycle, from collection to storage and analysis, b) Data Validation and Cleaning to identify and correct errors or inconsistencies present in the data, c) Data Source Validation to scrutinize the origin and reliability of data sources in order to minimize the risk of bias or inaccurate information, and d) Data Governance Framework that establishes clear policies that promote data quality, standardization, and access control.

Question to ask when assessing data Veracity at your organization: Is the data trustworthy and reliable with reasonable levels of quality and consistency?

4. Velocity: The Need for Speed in Data Flow

Data Velocity highlights the speed at which data is generated and needs to be processed. Aside from periodically updated datasets, today’s business often relies on real-time or near-real-time data generation and analysis. For instance, stock market fluctuations, news, traffic information, and sensor data from industrial machines all benefit from timely analysis to help make informed decisions.

Imagine a manufacturing plant where sensors monitor equipment performance. Traditional data analysis might involve periodic checks, potentially leading to missed opportunities or delayed responses to equipment malfunctions. Data generated at high speed can allow for continuous monitoring and real-time analysis, enabling predictive maintenance and mitigating costly downtime.

The high Velocity of data in many organizations is now requiring the implementation of new analytics tools and techniques capable of processing information streams efficiently. This includes systems powered by AI and machine learning that allow for near-instantaneous analysis of data, enabling organizations to react to market volatility, customer sentiment changes, or operational issues in real-time.

Question to ask when assessing data Velocity at your organization: How quickly is the data generated and at what rate does it need to be analyzed?

5. Value: Extracting Insights that Drive Results

Data Value encompasses the overall measurable impact that organizations can derive from their data. For business, this is the ultimate goal of data: extracting value from available data that may be otherwise idle. In the digital age, data has become the new gold. But unlike a physical treasure chest, data’s value isn’t inherent. It lies in its potential to be transformed into actionable insights that drive informed decision-making, optimize processes, and fuel innovation. Understanding data value and unlocking its potential is a critical skill for organizations of all sizes.

New technologies such as AI and machine learning play a significant role in extracting value from data, leading to previously unimaginable opportunities. Businesses can gain a deeper understanding of their customers, identify market trends, reduce costs, and make operations more efficient. Overall, organizations that embrace a data-driven culture and prioritize data Value are better positioned to thrive in an increasingly competitive market.

Question to ask when assessing data Value at your organization: Is the data of sufficient worth to support the business objectives?

Conclusion

The 5 Vs of data provides a framework for understanding the complexities and opportunities associated with data, especially in the context of data processing and analytics. By addressing the challenges of data Volume, Variety, Veracity, Velocity, and Value, organizations can unlock the true potential of this powerful resource. And, by considering these factors, organizations can leverage the power of data to build robust, reliable, and valuable AI and machine learning models that drive real-world result.

At Entefy, we are passionate about breakthrough technologies that save people time so they can live and work better. The 24/7 demand for products, services, and personalized experiences is compelling businesses to optimize and, in many cases, reinvent the way they operate to ensure resiliency and growth.

Begin your enterprise AI journey here and be sure to read our previous articles on key AI terms and the 18 skills needed to bring AI applications to life.

ABOUT ENTEFY

Entefy is an enterprise AI software and hyperautomation company. Entefy’s patented, multisensory AI technology delivers on the promise of the intelligent enterprise, at unprecedented speed and scale.

Entefy products and services help organizations transform their legacy systems and business processes—everything from knowledge management to workflows, supply chain logistics, cybersecurity, data privacy, customer engagement, quality assurance, forecasting, and more. Entefy’s customers vary in size from SMEs to large global public companies across multiple industries including financial services, healthcare, retail, and manufacturing.

To leap ahead and future proof your business with Entefy’s breakthrough AI technologies, visit www.entefy.com or contact us at contact@entefy.com.

The landscape of corporate governance is undergoing a significant transformation. Artificial intelligence, including generative AI, is rapidly weaving its way into the fabric of business operations, and boardrooms are no exception. From strategic decision-making to risk management, AI and machine learning present a unique set of opportunities and challenges for boards of directors. 

On this topic, Entefy sibling co-founders, Alston Ghafourifar and Brienne Ghafourifar, were invited to speak at Silicon Valley Directors’ Exchange (SVDX) to explain the very nature of AI today, its potential to disrupt business and industry, and how board directors can wrap their heads around the implications. This exclusive 1-hour session focused on the intricate relationship between AI and corporate governance, exploring its impact on board composition, function, and oversight. 

An organization’s governance board has an obligation to ensure that operations are aligned with strategy and safeguarded against potential disruptions or competitive threats. This SVDX session was designed to equip board members and corporate leaders with the knowledge necessary to harness the power of AI technology and develop AI strategies for responsible and effective governance.

During the session, Alston and Brienne shared their experiences and perspectives on how AI can augment a board’s capabilities and identify potential risks associated with its implementation and use. Among other topics, they proposed best practices for navigating this complex and ever-evolving technology landscape. 

ABOUT ENTEFY

Entefy is an enterprise AI software and hyperautomation company. Entefy’s patented, multisensory AI technology delivers on the promise of the intelligent enterprise, at unprecedented speed and scale.

Entefy products and services help organizations transform their legacy systems and business processes—everything from knowledge management to workflows, supply chain logistics, cybersecurity, data privacy, customer engagement, quality assurance, forecasting, and more. Entefy’s customers vary in size from SMEs to large global public companies across multiple industries including financial services, healthcare, retail, and manufacturing.

To leap ahead and future proof your business with Entefy’s breakthrough AI technologies, visit www.entefy.com or contact us at contact@entefy.com.