Category: AI Strategy

10 Must-Use Microsoft Copilot Capabilities in Power BI for Faster Data Insights

Businesses generate more data than ever before. The real challenge is not collecting information but understanding it quickly. Microsoft Copilot in Power BI makes this process much easier. It uses artificial intelligence to help users build reports, analyze data, write DAX formulas, and find useful insights with simple text commands.

Instead of spending hours creating dashboards or hunting for critical numbers, users can ask Copilot questions in everyday language. It understands the request and delivers useful answers within seconds. Microsoft continues to enhance Copilot with new features that make Power BI more accessible for beginners and more productive for experienced professionals.

Here are ten Microsoft Copilot features that can help you get more value from Power BI.

1. Create Reports with Simple Instructions

Building a report usually takes time because users need to select charts, arrange visuals, and organize information. Copilot makes this task much easier. You only need to type a request like, “Create a sales report that shows monthly revenue, profit, and regional performance.” Copilot understands the request and creates a report with suitable charts and layouts. This feature saves time and helps users who have little experience with Power BI. It also produces reports with a clean, professional design.

2. Write DAX Formulas Easily

Many Power BI users find DAX formulas difficult because they require special syntax and functions. Copilot removes much of this difficulty. Instead of writing formulas from scratch, users can explain what they need in plain English. For example, they can ask Copilot to calculate yearly sales growth or total profit. Copilot creates the DAX formula and also explains what it does. This speeds up report development and helps users learn DAX more easily.

3. Get Quick Data Summaries

Large reports often contain many charts and tables. Finding the most important information can take time. Copilot reads the report and creates a simple summary of the main insights. It points out important trends, top-performing products, changes in sales, and unusual results. This helps managers and business leaders understand reports without having to study every chart.

4. Ask Questions in Everyday Language

Another key selling point of Copilot is its natural language capability. Instead of drilling down through filters or creating new charts, you can type a request like, “What product has sold the most in the last quarter?” or “Show Europe revenue.” Copilot understands what you need and provides the appropriate data via the best-matched visual. This makes understanding data much easier for those less familiar with technology.

5. Explain Charts and Business Numbers

Sometimes a report shows numbers without explaining what they mean. Copilot helps by describing charts, business metrics, and key performance indicators in simple language. It explains why a value changed and what the result may mean for the business. This helps everyone understand reports more clearly and supports better business decisions.

6. Improve Semantic Models

A semantic model is the foundation of every Power BI report. A well-organized model makes reports faster and easier to manage. Copilot helps developers improve these models by suggesting better relationships between tables, recommending useful measures, organizing fields, and improving names. These improvements reduce mistakes and make future report development much easier.

7. Create Measure Descriptions Automatically

Good documentation helps teams understand reports long after they are created. However, many developers skip this step because it takes extra time. Copilot can automatically create descriptions for measures, calculated columns, and important business metrics. These descriptions explain what each item does in clear language. This makes reports easier to maintain and helps new team members understand the data model more quickly.

8. Make Existing Reports Better

Many organizations already have Power BI reports, but not all of them provide the best user experience. Copilot reviews existing reports and suggests useful improvements. It may recommend better chart types, clearer layouts, improved readability, or additional business metrics that can provide more value. Microsoft has also improved Copilot so it first studies the report before checking the data model, providing more accurate recommendations and answers.

9. Find Business Trends Faster

Business data often contains important patterns that are easy to miss. Copilot automatically looks for major changes such as sales growth, revenue decline, seasonal trends, customer behavior, and unusual values. It highlights these findings without requiring users to search through every report page. This helps businesses react faster to opportunities and potential problems.

10. Use AI to Build Better Reports

Microsoft continues to expand Copilot with AI-powered report authoring. This feature helps users throughout the report creation process. Copilot suggests report structures, recommends suitable visuals, checks report quality, and supports the publishing process. Instead of creating everything manually, users receive intelligent guidance throughout report development. This makes report creation faster, easier, and more consistent for both new and experienced Power BI users.

Latest Microsoft Copilot Updates for Power BI

Microsoft continues to add new capabilities to Copilot in Power BI and Microsoft Fabric. Recent updates make Copilot more accurate by allowing it to understand the report before it analyzes the data model. Microsoft has also expanded AI support for semantic model editing, which allows users to create measures, relationships, and model changes through natural language. It is also improving AI-powered report authoring to help users plan, validate, and publish reports more efficiently.

Final Thoughts

Copilot reshapes your experience of working in Power BI. It simplifies tasks, accelerates report creation, and makes business data easy to grasp. Whether you’re generating a report, crafting a DAX query, identifying trends, refining a semantic model, or explaining business results to a broader audience — Copilot helps you at every stage. As new capabilities are added, the power of Copilot in the context of data-driven decision-making will only increase. Organizations that invest in these AI capabilities now can boost productivity, produce higher-quality reports, and accelerate better decisions with Power BI.

July 17, 2026
Pega Infinity 26 Launches: Predictable AI Outcomes Without Token Fees
Pegasystems Inc. (NASDAQ: PEGA) has announced the general availability of Pega Infinity™ 26, the latest version of its enterprise AI suite designed to help organizations scale artificial intelligence with confidence. The release tackles two critical barriers to AI adoption: unpredictable outcomes and unpredictable costs.

Available through Pega Cloud® or in clients’ managed cloud environments, Pega Infinity 26 combines advanced agentic AI with built-in governance and a new pricing model that eliminates consumption-based AI charges. This enables enterprises to achieve both predictable AI performance and predictable expenses.

Predictable AI with Flat-Fee Pricing

At the heart of the release is the Pega Predictable AI™ architecture, which maximizes AI processing at design time to ensure runtime operations are extremely token-efficient. Clients pay a flat fee per resolved case—not per token—eliminating what Pega calls the “AI token tax.” This model allows enterprises to use as much AI as needed without worrying about runaway costs from token-based consumption.

Pega also introduced a free AI Token Cost Calculator to help organizations estimate potential savings compared to traditional token-based models.

Key Innovations in Pega Infinity 26
- Pega Infinity Studio – An AI-native development environment that integrates clients’ preferred coding agents with built-in best practices from Pega Blueprint AI™, accelerating delivery of mission-critical applications while reducing risk.
- Predictable AI agents and orchestration – New capabilities allow developers to connect and orchestrate both Pega and third-party AI agents within governed workflows, ensuring compliance and consistent outcomes.
- Pega Customer Engagement Studio – A unified workspace for marketers to create personalized 1:1 campaigns in minutes at scale, with AI agents handling ideation through execution.
- Design and runtime MCP support – Clients can use MCP (Model Context Protocol) to connect with tools like Claude Code, GitHub Copilot, and OpenAI Codex for building applications, and invoke agents from assistants like ChatGPT and Office 365 Copilot.
Market Context: The Need for Cost Transparency

Despite massive enterprise investment in AI, many organizations have struggled to realize meaningful business value. Concerns around trust, risk, and security have slowed large-scale deployment. Escalating costs and uncertain ROI threaten the viability of many AI initiatives. With Pega Infinity 26, every AI-driven interaction adheres to business rules and regulatory standards while removing pricing unpredictability.

Executive Insight

“Enterprises are seeing tremendous potential in AI and are now focused on turning that momentum into measurable business outcomes,” said Kerim Akgonul, chief product officer at Pega. “The next phase of AI adoption is about scaling results with confidence. Pega Infinity 26 removes two of the biggest barriers to success—unpredictable outcomes and unpredictable costs—by building governance into AI-driven workflows and aligning pricing with completed work.”

Pega Infinity 26 is available now. For more details, visit www.pega.com/infinity.
July 17, 2026
Top 5 AI Pitfalls Businesses Must Sidestep in 2026

Smart AI Adoption

Artificial intelligence is becoming a key part of business operations in 2026. It helps companies automate tasks, improve customer experiences, and make faster decisions. However, AI delivers the best results only when it is used wisely. Avoiding these common mistakes can save time, reduce costs, and improve long-term business success.

1. Using AI Without Clear Business Goals

Many businesses rush to adopt AI because it is popular, not because it solves a real business problem. This often leads to wasted investments and disappointing outcomes. Start with a clear objective, identify the challenge you want to solve, and measure success using meaningful business results instead of hype.

2. Ignoring Data Quality

AI is only as good as the data it receives. Poor-quality, outdated, or incomplete data can produce inaccurate recommendations and unreliable predictions. Businesses should regularly clean, organize, and verify their data while maintaining consistent standards. Good data is the foundation of every successful AI strategy.

3. Replacing People Instead of Supporting Them

AI should enhance employees’ work rather than replace them entirely. Human judgment, creativity, emotional intelligence, and problem-solving skills remain essential in many business situations. Companies achieve the best outcomes when AI handles repetitive tasks, allowing employees to focus on innovation, customer relationships, and strategic decision-making.

4. Forgetting Privacy and Security

As AI systems process large amounts of business and customer data, security becomes more important than ever. Weak safeguards can lead to data leaks, legal issues, and loss of customer trust. Businesses should implement strong cybersecurity practices, limit data access, conduct regular audits, and follow all applicable privacy regulations.

5. Skipping Employee Training

Introducing AI without preparing employees often leads to confusion, low adoption, and misuse of the technology. Staff should receive practical training on how AI tools work, their limitations, and the best ways to use them responsibly. Continuous learning helps employees stay confident as AI continues to evolve.

Bonus: Expecting Instant Results

Many companies expect AI to transform their business overnight, but real success takes time. AI models require testing, fine-tuning, and regular improvements before delivering consistent value. Businesses that stay patient, monitor performance, and refine their AI strategy continuously are more likely to achieve sustainable growth and long-term competitive advantage.

This article was originally published on Analytics Insight and has been adapted for our readers.

July 17, 2026
Marriott International’s Digital Transformation: AI, Cloud, and New Systems Reshape Hospitality

Booking a hotel room has changed dramatically in recent years. Today’s travelers expect to book in minutes, check in via smartphone, unlock doors with digital keys, and receive assistance without waiting in long queues. Meeting these expectations is a formidable challenge for a company operating thousands of hotels worldwide. Marriott International, serving millions of guests annually, has been quietly rebuilding its technology infrastructure from the ground up to keep pace with evolving travel habits.

Rather than making incremental updates, Marriott is replacing legacy systems that have been in use for years. Simultaneously, the company is embedding artificial intelligence (AI) across various business areas and migrating more services to the cloud. While most guests may not notice these changes day-to-day, Marriott expects these initiatives to significantly streamline booking, check-in, customer support, and hotel operations.

A Measured and Practical AI Journey

While many companies rushed to adopt AI as soon as it gained popularity, Marriott took a more cautious approach. The company chose to test the technology first and expand its use only after seeing real-world results. To facilitate this, Marriott established its own AI Studio in 2023. Small teams began experimenting with different ideas to identify where AI could deliver tangible benefits—some focused on supporting employees directly, while others aimed to enhance the guest experience.

Personalization is a key goal for Marriott. Every traveler is unique—some travel for business, others with family. AI enables Marriott to better interpret these preferences and recommend hotels or services that genuinely match each guest’s needs, rather than offering generic options. The company is also leveraging AI to improve its website and mobile app, making it easier for guests to find the right hotel by understanding their search queries more clearly.

Employees also benefit from these changes. Hotel staff can access guest information more quickly, reducing time spent navigating multiple systems and allowing them to assist guests promptly. Marriott has also emphasized that good AI depends on good data. If the underlying information is incomplete or outdated, AI cannot deliver useful results. This is why the company has invested considerable effort in improving its data quality before expanding AI across the business.

Importantly, Marriott has made it clear that AI is not intended to replace hotel workers. Hospitality remains fundamentally about people. Technology simply helps employees perform their jobs more efficiently.

New Systems for a Future-Ready Business

While AI garners much attention, Marriott’s most significant changes are happening behind the scenes. The company is replacing three of its oldest systems: the hotel management platform, reservation system, and Marriott Bonvoy loyalty program. These systems have supported the business for years but now require faster and more flexible technology.

Marriott is also moving more of its operations to the cloud. Instead of relying on older computer systems, cloud technology enables faster deployment of changes and upgrades. It also helps the company handle millions of reservations and customer requests without performance degradation. Another major goal is to connect different systems so they can work together seamlessly. Today, guest information, reservations, loyalty rewards, and hotel services need to share data quickly to create a smooth experience from booking to checkout.

Marriott is taking a practical approach to AI, deploying it only where it solves a real problem. This helps avoid unnecessary costs while improving daily operations. The company is also preparing for what experts call an “Agentic Mesh”—a concept where multiple AI assistants work together. One could help guests make bookings, another could support hotel employees, and a third could manage loyalty rewards or answer customer questions. These AI systems would share information while handling different tasks. Although Marriott is not fully using this model today, its new cloud systems are being built to support these technologies as they become more common.

Why This Matters

Marriott’s technology upgrades could have a ripple effect across the hospitality industry. With better AI, faster cloud systems, and connected platforms, hotels may see improved guest experiences, fewer delays, and greater modernization in how they serve customers. Travel is changing, and hotels must change with it. Guests now expect fast service, simple booking, and personalized experiences every time they travel.

Marriott understands that good technology is now as important as comfortable rooms and friendly staff. The company’s approach is notable because it didn’t rush into every new trend; instead, it focused on building a solid foundation first. AI is being added step by step, while employees remain central to the guest experience. As more hotels adopt AI and cloud technology, Marriott’s early investments could give it a strong competitive advantage and help shape the future of hospitality technology.

July 17, 2026
Data Strategy as the Cornerstone of AI Transformation: Insights from AION Tech Solutions’ Jagrit Gandotra

Organizations today generate more data than ever before, yet converting that data into measurable business value remains a major hurdle. As enterprises ramp up investments in cloud, analytics, and AI, success increasingly depends on building a robust data foundation that enables faster decision-making, innovation, and long-term growth.

In a recent episode of the Analytics Insight Podcast, host Priya Diyalani spoke with Jagrit Gandotra, Chief Business Officer at AION-Tech Solutions, about why data strategy has become a boardroom priority. The conversation covered data democratization, cloud ROI, governance, modernization, and the future of AI-powered enterprises. Below are the key takeaways.

Why Has Data Strategy Become a Business Priority Rather Than Just an IT Responsibility?

I think data has moved from being an operational resource to one of the most strategic assets a business owns. In today’s world, every major business decision is informed by data—customer experience, innovation, risk management, and growth all depend on data-driven decisions. This means that data strategy cannot be left solely in the hands of the IT team; it requires executive buy-in and organizational support. In simple terms, data strategy outlines what the business plans to do with its data, whereas data management ensures that the data is reliable and secure.

What Does Data Democratization Mean, and Why Is It So Important?

As I began working within business intelligence, one of the biggest challenges I faced was the fact that all of the data was held by the IT department. By the time the report was ready for decision-makers, the window of opportunity was gone. In my opinion, data democratization means allowing all authorized personnel the ability to have secure access to reliable data without having to rely too much on technical staff. All departments need to be able to make decisions based on data promptly. This will help them increase efficiency, foster innovation, and make faster decisions.

How Should Organizations Measure the ROI of Cloud, Data, and Analytics Investments?

I always encourage businesses to move past technology metrics in measuring ROI. After all, what matters at the end of the day is whether the investments being made are resulting in better business results. Return on investment should always be measured in terms of increasing revenues, reducing time to market for products, decreasing costs, increasing customer retention, increasing compliance, and quicker decision-making. Technologies play a major role as they enable businesses to become more agile and competitive. Successful organizations set a business metric baseline before embarking on their transformation journey.

What Should Enterprises Prioritize When Modernizing Their Data Strategy?

All companies have their own set of priorities, but in my opinion, there are five key elements of effective data modernization. First, it involves using cloud-first infrastructure for its benefits of scalability and flexibility. The second one is data governance and data quality, since low-quality data prevents every effort. The third is giving business users the power of advanced analytics and self-service. The fourth is the automation and integration of processes for quicker decisions. And finally, perhaps the most important thing is cultural transformation since technology alone does not drive modernization, and without changes in work culture and leadership-driven transformation, no data platform can deliver value.

What Will Define the Next Generation of Intelligent Enterprises?

The future intelligent enterprises will be the fusion of data, AI, automation, and human intelligence on a massive scale. It won’t be about companies having more data than others. It will be about them turning that data into business value at a much faster rate than their competition. AI will drive decision-making in real time, predictive analytics will take the place of historical reports, and analytics will become an inherent part of business operations. In addition, each member of staff should have access to tools for AI-enabled decision-making. This is the way for businesses to stay nimble and competitive during the next five years.

Listen to the full discussion on the Analytics Insight Podcast.

July 16, 2026
AI’s Quiet Cost Revolution: How Digital Production Is Becoming Nearly Free
There’s a version of the AI story that grabs headlines: frontier models, billion-parameter training runs, and the race between labs. But the version that matters to most businesses is far more consequential — and far less flashy. It’s the story of specific, boring production costs collapsing toward zero, and of the workflows, budgets, and org charts built around those costs quietly becoming obsolete.

This isn’t about intelligence. It’s about cost structure. And cost structure, historically, is what actually reorganizes industries.

The Death of the Resolution Constraint

For twenty years, image resolution dictated real decisions. A 600-pixel product photo couldn’t be used on a billboard, a hero banner, or a modern retina display. You either reshot the asset, licensed a new one, or accepted a visibly worse result. That constraint dictated photography budgets, campaign timelines, and asset management policy — and created a graveyard of unusable assets.

Machine learning didn’t remove that constraint by making cameras better. It removed it by making missing information reconstructable. Modern neural upscaling doesn’t just stretch pixels; it infers what detail would have been there, based on training on millions of high-resolution originals. The result: a low-resolution asset from a decade ago can be brought to usable modern size without a reshoot or compromise.

The economic consequence is not that images look nicer. It’s that an entire category of work stopped needing to happen. Every archived asset a company owns just became potentially reusable. The reshoot budget line item became optional. And the capability has already commoditized — free browser-based implementations put it in anyone’s hands.

The Death of the Build Constraint

The same compression is happening to software production. Building a native mobile app once required platform-specific engineers, build pipelines, store submission expertise, and ongoing maintenance — a gate that filtered out all but well-funded organizations. That gate is now being dismantled from two directions: no-code platforms abstracted away the mechanics, and generative models absorbed parts that required judgment.

The marginal cost of putting a functioning native app in front of customers has fallen roughly 100x in under a decade. Of course, the output doesn’t match a bespoke engineering team — but that’s the wrong comparison. The real counterfactual for most businesses was no app at all. When price drops 100x while quality drops only 20%, the market floods with new entrants who were previously priced out.

The Pattern Beneath Both

Strip away the specifics, and a four-step structure appears:
1. A production capability was expensive because it required scarce human expertise.
2. A model learned the statistical structure of that expertise well enough to approximate it.
3. The capability became a commodity at near-zero marginal cost.
4. The bottleneck moved upstream — into judgment.
When image upscaling was hard, owning good source assets was a competitive advantage. Now upscaling is free, it’s not. When app development was expensive, having an app signaled seriousness. Now anyone can ship one in an afternoon, it signals nothing.

What This Means in Practice

Capabilities that become universal stop being advantages. If a tool is free and browser-based, your competitor has it too. ‘We use AI’ is not a strategy — it’s like saying ‘we use electricity.’ The window where AI adoption itself feels like a differentiator is closing fast.

Value is captured downstream of the tools. The operator who identifies a category that nobody served because it cost $60,000 — and serves it for $200 — captures the value. That identification is genuinely hard work.

Quality thresholds are contextual. A 20% quality degradation is catastrophic for a medical device, irrelevant for a neighborhood gym’s booking app. Most evaluations compare AI output against the best human alternative, but the real counterfactual is usually nothing at all.

The compression is not finished. Every production task with large training data and a tolerant quality threshold is on the list. Copywriting went first. Design is going. Video is going now. Voice has largely gone.

For businesses, the strategic move is not to adopt AI tools faster — everyone will have them soon. The move is to notice which constraint just disappeared, and to reorganize around a world where it’s gone, before the rest of the market updates its assumptions. The tools are already free. Working out what’s now possible that was impossible 18 months ago — and acting on it — is the part that still requires a human.
July 16, 2026
IBM Stock Plunges 25% After CEO Krishna Admits Company Was Too Slow on AI

IBM shares posted their steepest one-day drop in decades after the company reported disappointing preliminary second-quarter results, with CEO Arvind Krishna acknowledging that the company failed to adapt quickly enough to the artificial intelligence spending boom. The selloff erased billions of dollars in market value and sent shockwaves through the technology sector.

Revenue Miss and Profit Warning

The technology giant reported revenue of $17.2 billion for the quarter ended June, up just 1% from the same period last year but below analysts’ expectations. Adjusted earnings per share also missed estimates, triggering a sharp market reaction. IBM shares fell about 25% in a single trading session, marking the steepest stock decline for the company in decades.

CEO Admits Execution Failures

In a letter to investors, Krishna said IBM underestimated how rapidly enterprise customers would redirect technology budgets toward AI infrastructure such as servers, storage systems, and memory hardware.

“These conditions require our teams to execute perfectly, and this quarter we faltered. We did not adapt and move quickly enough,” Krishna wrote.

He added that several large deals also failed to close within the expected timeline, further weighing on the company’s performance. IBM had anticipated supply-chain constraints would limit demand, but instead customers accelerated purchases of AI infrastructure, reducing spending on software and mainframe-related products.

Spending Shift Hurts Software Business

IBM’s infrastructure division posted a 7% decline in revenue, while software revenue grew 5%, slower than market expectations. Consulting revenue remained largely flat, reflecting cautious enterprise spending.

The results highlight a broader industry shift: businesses are prioritizing investments in AI hardware and data center infrastructure over traditional software deployments. Analysts warn the trend could pressure other enterprise software companies in the coming quarters.

Bright Spots in the Quarter

Despite the overall disappointment, certain parts of IBM’s business performed well. Red Hat recorded 11% revenue growth, while recently acquired companies HashiCorp and Confluent delivered strong results. IBM also noted that its Distributed Infrastructure business achieved record performance, supported by demand for Power servers and storage systems, with a backlog of approximately $500 million exiting the quarter.

IBM maintained that its long-term AI strategy remains intact, but the latest results underscore how rapidly changing enterprise priorities are reshaping the technology industry as companies race to build AI infrastructure.

July 15, 2026
Master AI for Business: Harvard’s Online Course on Machine Learning, Generative AI & Ethics
Harvard Business School has launched an online course titled AI Essentials for Business, designed to equip professionals with a practical understanding of how artificial intelligence is reshaping modern organizations. The program covers machine learning, data science, predictive modeling, and generative AI, with a strong emphasis on real-world business applications, ethical considerations, and risk management.

What You’ll Learn in This Course
- Grasp the evolving role of AI in contemporary business operations.
- Explore how machine learning, predictive modeling, and data science solutions can be applied to drive decision-making.
- Identify opportunities to leverage AI for improving operational processes and strategic planning.
- Examine ethical issues and risk management challenges associated with AI implementation.
- Understand how generative AI impacts organizational structures and business workflows.
Accessibility and Value

This 100% online course runs for four weeks, requiring 5–7 hours of study per week, making it manageable for busy professionals. Delivered through Harvard Business School Online, the program allows students to learn at their own pace from anywhere. Upon completion, participants receive a Certificate of Completion, validating their knowledge of AI fundamentals and strategic business applications.

Comprehensive Curriculum
- AI Landscape Overview: Understand the growth and evolution of AI technologies.
- Machine Learning and Data Science: Learn the basics of predictive analytics, modeling, and data-driven decision making.
- AI in Business Strategy: Discover how organizations integrate AI to drive innovation and operational efficiency.
- Generative AI in Organizations: Explore how generative AI tools impact business operations and digital transformation.
- Ethical AI and Risk Management: Implement AI responsibly while addressing ethical and governance challenges.
- Business Applications of AI: Identify opportunities to apply AI in business development, innovation, and strategic management.
Eligibility Criteria

The program is open to professionals, managers, and business leaders who want to learn about AI. No advanced technical skills or programming expertise are required. It is designed for beginners seeking to understand the business impact of artificial intelligence.

What Makes This Course Stand Out?

The course emphasizes the practical business value of AI, teaching students how to leverage AI for tangible business outcomes. Faculty from Harvard Business School provide training focused on AI-driven business innovation and digital transformation.

Final Thoughts

The AI Essentials for Business program delivers foundational concepts that illustrate how AI and machine learning transform modern organizations. Professionals gain practical skills to use AI strategically while navigating ethical and operational challenges.
July 15, 2026
Masayoshi Son Bets on Nuclear Fusion to Power AI’s Growing Energy Demands

SoftBank founder Masayoshi Son has identified nuclear fusion as a potential long-term solution to one of artificial intelligence’s most pressing challenges: energy consumption. Speaking about the future of AI infrastructure, Son argued that fusion power could supply the massive electricity demands of next-generation data centers.

Son emphasized that as AI adoption accelerates, electricity consumption will rise sharply. While some may view natural gas as a quick fix, he believes nuclear fusion is the superior endgame for sustainable AI growth.

Demand for Power Grows with the Rise of AI

As corporations race to advance AI systems and expand data center capacity, the need for electrical power will intensify. Son projected that the world will require approximately 3 terawatts of additional data center capacity by 2040 to support future AI developments. Traditional energy sources, he warned, may fall short of meeting this escalating demand.

Fusion Might Be the Solution in the Long Term

Son described fusion—the process that powers the Sun—as the most promising energy source for AI data centers. Although commercial fusion is not yet viable due to ongoing technical hurdles, he believes it has the potential to transform AI infrastructure economics over the long haul.

SoftBank Doubles Down on AI

These comments align with SoftBank’s aggressive AI strategy. The Japanese conglomerate has invested heavily across the AI ecosystem, backing companies like Arm and OpenAI while expanding into chips, robotics, and data center infrastructure. Son has repeatedly compared AI’s transformative potential to—or beyond—the internet revolution, urging businesses to embrace AI rather than hesitate.

The Bigger Picture

The AI revolution is creating not just a demand for computing power but also for adequate energy supplies. Major tech firms are exploring a mix of natural gas, nuclear power, and renewables to meet their needs. For Son, fusion represents the ultimate endpoint—a way to power the next stage of AI without energy constraints, even though its commercial application remains a work in progress.

July 15, 2026
AI Trends CEOs Must Track Before 2030: Agentic AI, Governance, and Strategy

Artificial intelligence has entered a new phase. From being an emerging technology to the very core of modern companies’ operations, the transition is happening rapidly. In any industry, discussions within the boardrooms have progressed from ‘Should we use AI?’ to ‘How should we implement AI and what business value do we see in it?’

It’s not a matter of implementing chatbots or automating tasks anymore. Companies are now looking to invest in technologies capable of making decisions, predicting, optimizing processes, and transforming customer experiences. The next several years will show which companies manage to remain leaders in their fields amid growing competition and AI regulations.

If you want to stay at the top and understand the technologies shaping your company’s future over the next decade, here are the AI trends to watch.

AI Trends Transforming Business Operations

These technologies are changing how organizations operate day to day, enabling faster decision-making, greater efficiency, and smarter execution across core business functions.

Agentic AI Will Automate Complex Workflows

The next generation of AI is moving beyond just answering prompts. Agentic AI systems can independently plan, execute, and monitor tasks across departments with minimal human involvement. Instead of waiting around, they can juggle procurement workflows, coordinate customer support, and handle financial reporting too, all in one flow. Autonomous AI agents are expected to become digital co-workers that handle increasingly sophisticated business functions, even when people are not actively watching or checking every step.

AI-First Enterprises Will Gain a Competitive Edge

AI is no longer seen as just another software application; it is now being used to rethink business processes. AI is now becoming a key component in decision-making, consumer engagement, and innovation practices. Businesses that adopt AI throughout their value chain will be better equipped to cope with changes in the market environment.

Decision Intelligence Will Transform Leadership

Corporate executives today increasingly rely on AI-driven analytics to bypass the usual reporting systems. Advanced AI platforms today can analyze large datasets, spot emerging threats, forecast demand, and recommend actions in real time. Using those technologies can help leadership make faster, evidence-based decisions and boost resilience in wobbly, uncertain business conditions.

Hyperautomation Will Be Adopted in All Enterprises

Automation processes aren’t only for small, trivial tasks anymore. With artificial intelligence, robotic process automation, and other intelligent process approaches, companies can automate things like finance, human resources, manufacturing, and customer support functions. It is expected that hyperautomation will bring costs down and tighten consistency so employees can shift attention to adding more value, rather than being stuck on repetitive routines.

AI Trends Shaping Long-Term Business Strategy

Beyond the day-to-day operational wins, these emerging trends should affect how businesses manage risk, build trust, cultivate talent, and keep a real competitive edge in the AI-driven economy.

Workplaces of Future Will See Humans and AI Working Together

It is expected that artificial intelligence will help people sharpen their job performance. Intelligent machines would take on repetitive chores, while humans can focus on creativity, critical thinking, and more human-to-human interpersonal relations. In this setup, the emphasis would swing toward employee reskilling, AI literacy, and broader organizational change, so adoption doesn’t feel like a sudden wall.

Responsible AI Will Become an Imperative for Businesses

With governments tightening rules about the use of AI in business and with consumers demanding greater transparency from businesses, responsible AI has emerged as an imperative. CEOs will have to develop governance models that address data privacy, bias mitigation, accountability, and ethical deployment. Good governance not only helps avoid regulatory risk but also improves customer and investor confidence.

Businesses Will Move to Enterprise-Owned AI Infrastructure

Many organizations are reconsidering the extent of their reliance on publicly available AI systems for running important business processes. There is a trend towards investing in private AI models, secure cloud infrastructure, and proprietary databases to safeguard intellectual property rights and improve cybersecurity.

Measuring AI Returns Will Separate Leaders from Followers

In the coming wave of AI adoption, there will be much less emphasis on experimentation and much more on implementation outcomes. The shareholders and the board of directors are demanding evidence of growth in revenue, productivity, client satisfaction, and efficiency resulting from the investment in artificial intelligence. Corporations that can measure performance metrics and evaluate the value of AI implementation within their companies will have a strong chance of remaining profitable in the future.

Nowadays, artificial intelligence is not just another technology fad, it is the core of the company’s strategy. Corporations that can create autonomy and accountability, restructure their workforce, and make decisions based on the analysis are expected to lead the following decade. For CEOs, implementing AI technology is not enough; what really matters is building a company ready to adapt to this innovation. Those who act before 2030 will have an excellent chance of survival amid disruption.

July 14, 2026