Stop Selling AI Tools to the Data Team

If you’re building an AI tool for enterprise data work, the org chart will tell you to sell to the head of data. That’s the title that owns the budget, manages the team, and controls the tooling decisions. Every founder running this playbook hits the same wall.

Alex Reichenbach, CEO of Structify — the AI data team for enterprises — ran that playbook. It didn’t work. He pivoted, and the second go-to-market motion is the one funding the company.

“We tried to sell to heads of data,” Alex says, “and I realized that was a terrible sales process for us.”

The lesson isn’t subtle. The natural buyer for an AI data tool is rarely the right one. The buyer who actually closes is one or two roles downstream — the operator who’s been waiting on the data team for months and would welcome anyone, agent or otherwise, who can shorten the queue.

Why the head of data resists

Heads of data resist not because they don’t understand the technology. They resist because the technology threatens what they’re optimizing for.

Most heads of data are optimizing for two things: team growth and operational survival. Team growth means hiring more engineers, securing more headcount, expanding scope. A tool that lets a COO bypass the data team entirely doesn’t help that goal — it shrinks it. Operational survival means clearing the ticket backlog without dropping balls. A tool that introduces a new dependency, a new training requirement, and a new audit surface adds risk in the short term even if it pays back in the long term.

The exception, Alex says, is the head of data with equity in the company’s outcome. “I’ve talked to heads of data that have equity in the company. They care about how the company does. And for them, all they’re saying is, ‘I have hundreds and hundreds of dashboards that I manage. I’m thousands of tickets behind. I have ownership of this company and I know I’m costing this company the bottom line. I would love if you can help us not be critically behind. I would love that in a second.’”

That’s a real buyer. But that’s also a small slice of all heads of data — most operate without equity, and their incentives don’t align with a tool that compresses their team.

Who the actual buyer is

The buyer Alex looks for now is the operator downstream of the data team — typically a COO or a senior PM. They have two characteristics that the head of data lacks: they feel the pain of the backlog directly, and they have no career incentive to protect the data team’s scope.

“The COO sees the pain point,” Alex says. “They need their data, and the data team is usually backlogged because they have so many requests coming in.”

The COO is also the one who hears the bottom-line consequences. When the finance team can’t refresh numbers fast enough, when the M&A team can’t process documents in time, when the rev ops team can’t enrich leads — those failures compound on the COO’s plate. They have skin in the game on the outcome, not on the team structure.

The PM is similar. Modern PMs run experiments, need data, and hate waiting. A tool that lets them write their own queries — even imperfectly — is more valuable to them than a tool that requires them to file a request and queue.

The signs you’re selling to the wrong person

There are three reliable signs that the buyer in front of you is the wrong one.

First, the buyer asks about how the tool fits into the existing data team workflow. This is a survival question, not an adoption question. They’re trying to figure out how to slot a new tool into a structure that won’t change. The right buyer asks about how the tool replaces the data team workflow.

Second, the buyer wants extensive customization before piloting. “Can you integrate with our specific data warehouse? Can you respect our specific access controls? Can we add a review layer?” These are valid questions, but when they come before any usage, they’re a stalling pattern. The right buyer wants to see the tool work in production fast.

Third, the buyer escalates to procurement before showing internal champions. The head of data who isn’t sure they want this will route it through procurement to slow it down. The COO who’s hurting will introduce the founder to three internal champions in the first call.

The deeper sales lesson

The pattern Alex describes generalizes beyond data tools. Whenever a new technology threatens the role of an established function, the buyer with title authority over that function is rarely the right buyer. The right buyer is one or two levels up — close enough to feel the function’s pain, but not so embedded that they’re protecting it.

This is why enterprise sales lore says “sell to the pain.” The pain doesn’t usually live with the title that owns the function. It lives with the person who depends on the function and is held accountable for outcomes. They’re hungrier, they have authority over outcomes if not budget, and they can pull the deal through procurement faster than the function lead can stall it.

For founders building AI tools — especially tools that compress what an existing team does — the play is to find the operator downstream of that team, not the leader of it. The org chart will lie to you. The pain map won’t.

FAQ

Why does selling enterprise AI to a head of function fail?

Because the head of function is incentivized to protect team scope and operational continuity, while AI tools often compress both. The exception is leaders with equity who optimize for company outcome over team size. Alex Reichenbach found this when Structify’s go-to-market hit resistance from heads of data — the same tools the COO welcomed were a threat to the team owner.

Who is the actual buyer for AI data tools in 2026?

The COO, the head of rev ops, or a senior PM — operators downstream of the data team who feel the backlog as a personal blocker. They have authority over outcomes but no incentive to protect the data team’s scope. Alex says these buyers respond to demos that show the tool replacing rather than augmenting the existing data workflow.

How do I know if my buyer is going to stall the deal?

Three signals: they ask how the tool fits into the existing team workflow (survival question), they want customization before piloting (stalling pattern), and they route the deal through procurement before introducing internal champions. The buyer who closes asks how the tool replaces the existing workflow and brings their own internal champions early.

What’s the right time to engage with the head of data?

After you’ve earned credibility through operator-led usage. Once a COO or PM has a working pilot with a clear ROI, the head of data becomes either an ally (because the proof is in production) or a constraint to manage (security review, integration scope). Engaging too early gives them veto power before there’s evidence.

What outcomes do downstream operators report?

Significant latency reduction on data work. Alex describes M&A teams replacing manual document classification with automated pipelines, finance teams eliminating cross-Excel formula errors by keeping the full pipeline reproducible, and rev ops teams pulling enriched lead data automatically. The pattern: tasks that used to take days take minutes, and operators stop queueing work behind the data team.

How do enterprises typically run a pilot with Structify?

For regulated sectors (financial services, healthcare), the first step is a security questionnaire confirming SOC 2 and HIPAA. Then a deployment strategist runs a pilot for the operator team — proving the use case without requiring extensive learning. For smaller orgs, Alex says the path is even simpler: connect Structify to Slack and let operators interact in natural language directly.

Why is “sell to the pain, not the title” the right framing for AI tools?

Because AI tools usually compress existing functions, and the title that owns that function is structurally biased against compression. The pain — the cost of the function being slow or backlogged — lives with the operator who depends on it. They’re motivated to fix the bottleneck. The title-holder is motivated to grow the team. Different incentives produce different sales cycles.

Should I avoid heads of data entirely in my go-to-market?

Not entirely. Heads of data with equity, broad ownership, or explicit mandates from the CEO to reduce backlog can be excellent buyers. Alex’s lesson is to identify which kind you’re talking to early. If they ask “how does this help my team scale?” they’re optimizing for team. If they ask “how does this reduce our queue?” they’re optimizing for outcome — that one’s a buyer.

What does the change in buyer persona say about how AI is reshaping enterprise sales?

It says the locus of decision-making is shifting from function owners to outcome owners. As tools get more capable, the bottleneck moves from “which team can do this?” to “which person needs this answered today?” Function owners optimize for stability; outcome owners optimize for speed. AI tool sales motion has to follow that shift, or the deal cycle slows to a crawl.