AI is shifting from experimental to essential
AI isn’t an experiment anymore. It’s operational, it’s serious, and it’s quickly becoming foundational. Among the different deployments, one stands out, agentic AI. This isn’t about chatbots and auto-complete scripts. This is AI that can act on its own, understand goals, make decisions, and complete tasks without waiting for you to push a button.
Right now, 48% of tech executives say they’re already deploying these types of systems. That’s nearly half. And we’re going to see that number grow fast. According to EY’s Technology Pulse Poll, about half of tech execs think over 50% of their company’s AI deployments will be autonomous within two years. What this tells us is AI agents, software that thinks and acts with minimal human input, are shifting into core operations.
Business leaders are no longer asking “What can this technology do?” They’re bundling AI into strategy, supply chains, and product delivery. The rise of search interest in terms like “agentic AI” and “AI agents” in late 2024 shows markets are paying attention. If you want better performance and scaled systems that truly adapt, then you look beyond passive tools. You go autonomous. This will take pressure off operations and give your people time to focus on things that can’t yet be automated, like thinking big.
Increased AI budgets underscore executive confidence
Money usually follows confidence. And right now, AI is pulling in major interest from tech leaders, not just in prototypes, but in full-scale, operational investments. EY reports that 92% of execs are increasing their AI budgets. That’s basically the whole group saying, “Yes, this is where we’re going.” Among that group, 43% intend to put more than half of their total AI spend into agentic AI. That’s not minor. That’s a pivot.
Why? Because this is a competitive race, and people know it. Seventy percent of those executives say they’re betting on agentic AI to keep them ahead. Not just to keep up, actually stay in front. Another 59% mention customer impact, and another 59% are using these systems to drive internal strategy. So, these agents aren’t just tools, they’re embedded into how business is designed and delivered.
What’s clear is business leaders aren’t treating AI like an add-on anymore. They’re placing agentic AI at the center and shaping capabilities around it. If you’re serious about scalability and taking cost or complexity out of the mix, autonomous systems give you the throughput you need without the maintenance overhead. You spend now to save bigger, and smarter, down the line.
Structural transformations and leadership initiatives
You can’t simply bolt AI onto outdated systems and expect meaningful results. That’s not how real innovation scales. To embed agentic AI into a company in a way that delivers results, structural transformation is necessary, this includes tech infrastructure, leadership approach, and how data flows throughout the business.
Most companies are still figuring this out. According to Accenture’s research across 2,000 global executives, only 8% have actually scaled AI effectively across their operations. These “front-runners” aren’t just experimenting, they’ve wired AI into decision-making, product cycles, and customer-facing systems. The return is measurable. Organizations that did this well and generate more than $10 billion in revenue are growing 7% faster than their peers. Shareholder returns are also 6% higher, which matters if you care about long-term valuation.
That level of performance doesn’t happen unless leadership is aligned and infrastructure is ready. One of the major roadblocks? Unstructured data and fragmented systems. Businesses are still running on old platforms that can’t handle the speed and scale that AI demands. Without robust, real-time data or the right APIs and integrations, agentic AI can’t function efficiently.
If you want to compete with companies already scaling AI, then leadership must drive stronger coordination between data strategy, enterprise architecture, and execution. This isn’t about launching side projects or testing productivity hacks. It’s about refitting the core business so autonomous systems can operate and deliver value without friction.
Executive knowledge gaps present significant risks
AI’s impact is redefining industries in real time. But there’s one consistent weakness across many companies: most executive teams aren’t AI-ready where it counts. Strategy doesn’t mean much if leadership doesn’t understand the technology they’re evaluating or deploying.
Gartner’s study of 456 senior executives confirms the concern. Only 44% of CIOs are viewed by their CEOs as AI-savvy. That figure doesn’t include similar doubts about CISOs and CDOs. Meanwhile, 77% of those same CEOs believe AI signals the start of a new business age. That’s a mismatch you can’t ignore. It’s not about being technical, it’s about understanding how to lead in an environment where AI isn’t just another software shift, but a redesign of workflows, value chains, and customer experience.
David Furlonger, VP Analyst and Fellow at Gartner, says it directly, AI isn’t a marginal upgrade. It’s a complete step change in how organizations function. If your executive bench lacks the skills to align AI with actual business needs, you won’t move fast enough, and eventually you won’t compete well enough either.
Jennifer Carter from Gartner makes another critical point, upskilling current leadership is more urgent than hiring external experts. Your existing people know your systems, your culture, and your customer expectations. When you close the skills gap internally, you don’t just improve AI readiness, you raise the floor across your organization’s ability to adapt and lead. That’s how you protect your competitiveness without overextending.
Targeted upskilling and strategic hiring
Demand for AI talent is rising across the board, not just in engineering, but in roles that translate AI into real business value. You can’t deploy agentic systems at scale without the right people. This includes product managers with AI literacy, data engineers who can handle high volumes of complex data, and MLOps teams who know how to take models from build to production without delays or failures.
EY’s Technology Pulse Poll makes this clear. Eighty-four percent of tech leaders say they expect to hire in the next six months specifically due to AI adoption. These hires aren’t cosmetic, they’re operational. From product infrastructure to customer interfaces, AI systems are reshaping how businesses run, and top executives are responding by building internal teams that can own and scale the tech.
Ken Englund, EY Americas Technology Sector Growth leader, pointed out a major shift: stronger demand for roles like AI-experienced product managers, data engineers, forward deployed engineers (FDEs), and MLOps talent. These are the functions companies need if they want live environments that are stable, scalable, and useful.
But it’s not just about full-time staff. Companies are also moving fast to plug skills gaps with external experts. Fiverr’s Spring 2025 Business Trends Index showed an 18,000% increase in businesses searching for freelance help to implement AI agents. On top of that, there was a 641% jump in requests for freelancers specializing in making AI-generated content feel more human. That’s a real indicator of what’s happening: the systems are powerful, but they still need human reinforcement, especially in customer-facing roles.
If you’re a business leader driving AI transformation, you need to be clear about two things: who you need to hire and what you need to teach internally. There’s no way around it, your talent strategy will determine how much value you actually get from AI.
Successful AI integration brings tangible benefits
Salesforce isn’t tinkering with AI, they’re deploying it across core business units. Their Einstein 1 Platform is built to scale autonomous agents into real customer experiences, sales operations, and service tools. These systems aren’t passively supporting teams, they’re actively managing tasks like summarizing case histories, drafting emails, auto-filling records, and responding to customer queries using internal data.
This kind of deployment is where most organizations want to end up. AI that’s not just productive, but precise. AI that integrates smoothly into existing workflows without adding friction. It’s a working case of how companies can go beyond generative tools and deploy agentic AI to drive revenue and streamline operations.
Salesforce CEO Marc Benioff isn’t downplaying the shift. He called this “a new tech era,” highlighting how trust, accuracy, and usefulness are converging to make autonomous systems truly operational. AI isn’t running in the background anymore, it’s becoming a front-line system in enterprise-level platforms.
For decision-makers, this is useful to watch and track. It’s not theory. It’s productized, deployed, and measurable. The performance gains are never just in the technology, they’re in speed, accuracy, and customer response. Integration at this level takes infrastructure discipline, leadership support, and a clear vision of how autonomous agents deliver business outcomes, not just technical outcomes. Acting on that is what separates serious leaders from companies still stuck in pilot phase.
Key takeaways for decision-makers
- AI is no longer experimental: Agentic AI is shifting from concept to core, with 48% of tech leaders already deploying it and half expecting most AI deployments to be autonomous within two years. Leaders should prepare to integrate AI agents into core functions, not just isolated use cases.
- Budgets follow confidence: 92% of tech executives plan to increase AI spending, with nearly half directing over 50% of their AI budget toward agentic systems. Prioritize funding models that focus on autonomous solutions to maintain a long-term competitive edge.
- Scale requires structure: Only 8% of companies are scaling AI across the business, and success depends on overhauling legacy systems and modernizing data infrastructure. Organizations must align IT architecture, strategy, and leadership to enable enterprise-wide AI impact.
- Leadership needs to catch up: Just 44% of CIOs are seen as AI-savvy by their CEOs, despite 77% agreeing AI signals a new business era. Executives must prioritize AI literacy across the C-suite to avoid stalling strategic transformation and value delivery.
- Talent is the engine for adoption: 84% of tech execs are hiring because of AI, with demand rising for roles like AI-focused product managers, data engineers, and MLOps professionals. Invest in upskilling internal teams while accelerating hiring of specialized AI talent.
- AI integration is already driving results: Salesforce’s use of agentic AI platforms like Einstein 1 shows how fully embedded, autonomous tools can streamline operations and enhance customer experience. Executives should focus on building AI systems that operate across functions to generate measurable outcomes.
