How to get AI agent budgets right in 2026

With the end of the year around the corner, I’ve been hearing a common refrain from enterprise IT and transformation leaders: “What budget should I allocate for AI agents in 2026?” The question comes as no surprise. While the rest of their organization might be winding down for the holidays, CIOs are gearing up for one of the most high-stakes planning cycles of the last decade.

AI agents are line items in almost every boardroom agenda. CIOs and CTOs are fielding a barrage of requests from their leadership cohort around “Where are our agents? What outcomes are we expecting? What’s the plan for next year?” According to Forrester, CIOs are set to receive more budget for AI in 2026, but it’s a case of more money, more problems. Next year, IT and tech leaders can expect continued business volatility and intensified pressure to justify every AI dollar is well spent.

As CIOs set their AI budgets, it’s worth taking a reality check: many AI projects are still struggling to make it from pilot to production, or if they do make it to production, they quickly find their use case cannot deliver the ROI they had hoped. That’s why I believe 2026 is a defining budget cycle. The organizations that select the right projects, invest in talent and capabilities and carefully consider the architecture needed to support agents at scale will build sustainable competitive advantages. As for the others? They’ll burn time and money on doomed pilots and incremental tools that offer no real business impact. To make smart bets for a critical year ahead, CIOs must start with understanding how AI agents can deliver real business outcomes instead of just excitement.

What do customers and employees actually want from AI agents? 

The reason AI agents are getting board-level attention is the promise to bridge the gap between human intent and business effect through automation. Over the course of the year, I’ve consulted with hundreds of enterprise leaders seeking to transform their business with AI agents. The ones able to turn those aspirations into transformations all possessed a similar ingredient – they identified the right use cases to start with. Any CIO and any organization can get this part right.

So what do customers and employees actually want from AI agents? Almost every high-impact AI agent project I’ve seen this year boils down to the same simple concept: a user expresses an intention, and an agent takes action on their behalf to deliver an outcome. This is the paradigm that separates agentic AI from chatbots. Agents promise to go beyond just information retrieval or recommendations. That capability is valuable, no doubt, but it’s table stakes in today’s AI world. Customers and employees don’t just want responses or insights; they want assistants that can take tangible actions. They don’t just want AI to help them navigate their supply chain orders across SAP modules; they want to say “order 10 tons of product” and have their agent deliver that outcome end to end.

The majority of successful AI agent projects I’ve seen look just like this. Agents for internal teams focus on meaningful ways to improve productivity by empowering workers to turn complex processes into simple requests. No one, especially anyone under 30, wants to spend time learning how to point-and-click their way through a needlessly complex user interface on a SaaS platform. Forward-thinkers are building their agents to ride a layer above core products to turn intention into outcomes. The ROI in these cases is driven by cost and time savings for the business, at scale.

As agentic capabilities evolve, I’m increasingly seeing this same concept also being applied to AI agents that reinvent the customer experience. Look at the mortgage industry for an example. These lenders report a high drop-off rate in online mortgage applications. The reality is that mortgage applications can be quite complicated. The average applicant is often overwhelmed by financial jargon or documents they may not have readily available. If the user gets confused and steps away, odds are they won’t come back. Now, imagine replacing that with an AI agent that interacts with the core service. It can answer questions, translate complex financial terms into plain terms in real-time, save the session if needed and securely reach out for bank documents. Just a 1-2% increase in completed applications from this represents a material impact on the bottom line. That’s high-impact for the business.

Don’t swing for the fences, just get on base 

As you’re allocating your budget for AI in 2026, here’s my advice: stop chasing moonshots. These vaguely scoped, overgeneralized agent dreams are often expensive, they rarely ship and they burn resources faster than they can create value. Instead, look for opportunities to hit singles and doubles. Keep your eye out for specific, high-value and outcome-driven projects that can deliver wins in months, not years. I’ll walk you through the coaching that I use with enterprise leaders planning AI projects.

Start with this question: What are 10 processes that are repetitive, well-documented and still being manually performed by humans? Score each one on a 1-10 scale across these three dimensions: the impact if you automated it, the risk if the project fails (where 10 is catastrophic) and the complexity to build and deploy it. The winning formula is high impact, low risk and low complexity. That’s your AI sweet spot. Aim your swings there.

One pharmaceutical company used this exact framework and landed on a sleeper hit: agent-based adverse event report processing. That’s not glamorous, but it freed up 40% of the team’s time that went back into actual drug discovery. In sales, I’m seeing teams use agents to create content production pipelines for outbounds, proposals and follow-ups, which helps speed up cycle times and frees reps to focus on closing deals. In financial services, agents are automating tedious back-office processes that are expensive and labor-intensive, cutting costs and reducing turnaround times by days.

Nailing the ROI formula for AI Agents 

So you’ve honed in on the right projects for 2026 AI agents, now comes the hard part: the investment. This is not free; you’ve got to make tough decisions about how to appropriate the budget and how to beef up the teams that will actually go build them. But first, you need to understand something critical about the ROI formula. Return and investment mean widely different things from company to company. 

At the enterprise scale, the value proposition for AI rests much more on increasing the bottom line. Enterprises have many more customers, opportunities for efficiency and additional sources of revenue. So even a one to two percent upside on a critical workflow, such as customer acquisition or cost reduction, can yield a material improvement to the bottom line. The scale at which enterprises operate changes the calculus for what automation flows should look like, due to the impact on their core business.

CIOs across the board must assemble the right team and ensure it is properly budgeted and staffed (I wrote on this topic earlier this year). On top of that, organizations must ensure they’re building AI agents the right way. Your ROI is fundamentally dependent on your ability to scale AI agents across all different teams in your business. Without security, compliance and governance built in from the start, you can’t scale at all. You need to solve for thousands of users, each with their own permissions, and be able to trace every action the agent takes on their behalf. Build these guardrails in from day one, and your agents can become force multipliers. Otherwise, they will drown under token costs to LLMs while the projects never go beyond a prototype.

If 2025 was the introduction to AI agents, then 2026 will be when the winners start to emerge. The companies that break away from the pack won’t be those that talked the loudest about agents or ran the most proof-of-cycle concepts. It will be the ones who shipped them to production and saved time, made money or created new customer experiences that resonate.  The difference won’t be luck; it will come down to those who approach agents with smart design, the right use case selection and informed bets on the teams and technology to bring automation to life. The right plan could change your company’s entire trajectory.

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