CIO Report 2026 series: insights from exclusive report, based on interviews with IT leaders at Cloudflare, Cursor, Lyft, GitLab, Nextdoor, Zip, Rubrik, IMC Logistics, TrendAI, Palo Alto Networks, and Zscaler.

Long evaluations make for bad decisions. Full stop. The market moves faster than the RFP cycle does, and six months of due diligence produces a choice based on data that's already old. 6 months is an eternity in 2026.

Manu Narayan, CIO at GitLab, puts the problem plainly. "If you take a six to twelve month RFP cycle to make a decision on a platform," Narayan says, "you're going to make a decision on data that's twelve months old." A structural disadvantage dressed up as caution.

The teams measuring real AI outcomes in production right now made decisions in weeks. Their first pilots weren't perfect. They ran against real volume, surfaced real failure modes, and produced the kind of data no vendor demo can replicate.

Moving fast in AI evaluation is a form of risk management, and one of the recurring findings in the CIO 2026 report. The IT leaders with measurable AI outcomes started earlier and iterated faster than their peers.

Step 1: Pick the problem based on volume

The best first AI pilot is the one closest to the highest-volume, lowest-complexity work your team already handles. The low hanging fruit.

Tier-1 ticket deflection is the canonical starting point. Password resets. Access requests. Software provisioning. Onboarding steps. These problems consume 40 to 60 percent of the IT workday. They're well-defined enough to test against, and deflection rate is a metric the whole organization understands without explanation.

The goal of the first pilot is one thing. Produce a number. A real deflection rate from real ticket volume. Everything else, ROI modeling, headcount planning, platform decisions, becomes easier once that number exists.

Resist the temptation to start with the most complex or most visible problem. The harder the problem, the longer the pilot takes and the harder it is to isolate what's working. Complexity belongs later. The first pilot needs volume and simplicity.

Step 2: Define what "done" looks like before you start

Set success criteria before you deploy. The order matters more than most teams expect.

A 30-day pilot produces a clean answer only if the question is clear in advance. What deflection rate would validate this tool? What would cause you to move on? What volume do you need to see before the result means anything?

Piru Chheang, Head of IT at Zip, described the situation his team inherited plainly. "We're more reactive than proactive at this point." He said it as an accurate read of where they were starting, and as the baseline against which he intended to measure progress. His team's deployment reached upwards of 50 percent ticket deflection. That result was legible because the measurement was simple and set before deployment ran.

A pilot without pre-defined success criteria produces a lot of data and no decision.

Step 3: Run against real volume ASAP

The most common way pilots stall is by staying in sandbox mode too long. Controlled environments. Demo data. Limited ticket categories. The result is a pilot that proves the tool works under optimal conditions, the least useful thing to know.

Deploy against a real ticket category with real inbound volume as early as possible. The first few days will surface edge cases no sandbox would have shown. That's the point. Find the failure modes early, while they're cheap to address.

Most IT platforms capable of tier-1 deflection can be configured and connected to a live ticket queue in a matter of days. The technical lift is lower than most IT leaders expect.

The organizational decisions take longer. Which category, what escalation path, who reviews the edge cases. Those conversations happen in rooms, not in the tool. Starting them before the technical setup is done saves a week.

Step 4: Measure deflection rate, not demo accuracy

At day 30, one metric matters. Of the tickets that hit the AI agent, what percentage resolved without human intervention?

Anything above 30 percent in a first pilot is a meaningful result. Chheang's 50 percent at Zip is an outlier, but it happened in production, at real scale, against a real queue. That number became the foundation for every decision his team made next.

The secondary metric is time-to-resolution for escalated tickets. When the agent handles the routine cases, do the complex ones move faster? If yes, the case for expanding scope is already made.

Step 5: Use the number to decide what's next

The 30-day pilot produces one output. A decision.

If deflection rate is above threshold, expand scope. Add a ticket category. Increase volume. Move the freed hours toward projects that have been deferred. For a closer look at where those hours go next, see From IT Admin to IT Engineer.

If deflection rate is below threshold, the question is why: tool configuration, ticket category choice, or a mismatch between the problem and the platform. That answer is worth having now, not after another year of evaluation.

The fear that keeps IT teams in evaluation mode is usually the same fear Narayan identifies across the organizations he's watched. "There's a lot of fear of the unknown," Narayan says. "There's a lot of fear of what it means for jobs in the future."

That fear resolves through the work. Running a pilot, seeing a deflection rate, experiencing what freed capacity actually feels like, that's what moves teams forward. Twelve months of evaluation produces a recommendation. Thirty days of production produces conviction.

For a deeper look at what happens after the first deflection wins, see Escaping the Reactive Trap.

The IT's Time to Build report goes deeper on every layer of this framework, with insights from IT leaders at Lyft, Cloudflare, GitLab, Zip, and more. [Download the report →]

Frequently asked questions

How long does it take to deploy an AI agent for IT ticket deflection?

Most IT platforms can be configured and connected to a live ticket queue in days. The technical setup is faster than most teams expect; the organizational decisions take longer than the tooling.

What deflection rate should I expect from a first AI pilot?

Thirty percent deflection in a first production deployment is a meaningful result. Piru Chheang at Zip reached 50 percent, but that's an outlier. Start with a clear threshold and let the data guide what comes next.

Why do AI pilots fail?

The most common failure modes are running in sandbox mode too long, skipping pre-defined success criteria, and starting with a problem that's too complex. A well-defined, high-volume, low-complexity ticket category removes all three.

What should I do after a successful AI pilot?

Expand scope, add a ticket category, increase volume, or redirect freed hours toward higher-leverage deferred work. The pilot produces the data; the decision about what to build next is yours to make before the capacity gets absorbed.

Is 30 days realistic for an enterprise IT team?

Yes. The constraint is decision velocity. IT leaders who run successful pilots in 30 days made two decisions fast: which problem to solve and what success looks like. Everything else follows.