Short answer
AI pilots usually stall because nobody owns them, the workflow around them never changes, the team isn't trained, or the pilot never connects to the systems where real work happens. Lattice Partners is an AI implementation partner based in Los Angeles and New York that helps companies build an AI strategy, trains their teams on AI tools, and builds custom agents and software. Training plus implementation from one partner that stays through rollout addresses several of these causes at once.
- An MIT report covered by Fortune in August 2025 found that about 5% of generative AI pilots achieve rapid revenue acceleration.
- Measure results on the workflow the AI changed: hours, turnaround, errors, throughput.
How often do AI pilots fail?
AI pilots stall more often than they succeed. In August 2025, Fortune reported on an MIT report, The GenAI Divide: State of AI in Business 2025, which found that "about 5% of AI pilot programs achieve rapid revenue acceleration; the vast majority stall." The research was based on 150 interviews with leaders, a survey of 350 employees, and an analysis of 300 public AI deployments.
The same coverage reported that buying AI tools from specialized vendors and building partnerships succeeded about 67% of the time, while internal builds succeeded about one-third as often.
The fix usually starts before the pilot. From AI strategy to production covers how to pick the first workflow and who owns it.
Why do AI pilots stall?
Most stalled pilots trace back to a short, predictable list of problems, and few of them have much to do with which model you picked. The table covers the common ones and what to do about each.
| Why the pilot stalls | What it looks like | The fix |
|---|---|---|
| No owner | The pilot belongs to "the AI committee" or IT. Nobody who does the work is accountable for it. | Name one owner who uses the tool, has authority over the workflow, and reports on the success measure. |
| No workflow change | The AI tool sits next to the old process. People do the work the old way and check the AI afterward, if at all. | Redesign the step the AI replaces. Remove the old path or make the AI path the default. |
| Team not trained | People tried it once, got a weak answer, and stopped. Usage drops off. | Hands-on training on the team's own tasks and documents, followed by office hours while habits form. |
| No data access | The pilot runs on a sample export. Real data sits in a CRM, ERP, or shared drive the tool can't reach. | Solve access and permissions in the plan, before building. Connect directly to source systems. |
| Never connected to real systems | The demo works in a sandbox. Moving it into production needs integrations nobody scoped. | Build the first version against real systems from the start, even if the scope is small. |
| Vendor leaves after kickoff | A firm delivers a strategy or prototype, then the engagement ends. The internal team inherits something it didn't build. | Keep the builders engaged through launch and at least the first round of iteration. |
Of these, a missing owner and an unchanged workflow tend to do the most damage. A tool that has both can survive a weak first version.
"Train the team before the tool launches. When people already know how to use it on day one, everybody benefits immediately."
Christian Yeghnazar, Co-Founder and Managing Partner, Lattice Partners
Why do consultants need to stay engaged through implementation and team training?
Most pilot failures happen after the strategy is done, during rollout and adoption, so consultants who leave at the roadmap miss the part where things go wrong. They aren't around when the integration breaks or the team quietly stops opening the tool.
Look for artificial intelligence consulting services where the people who train your team also build the software and keep improving it after launch. Training early pays off during the build, since people who know the tools give sharper feedback. And when the first version is built against real systems, the pilot is already in production by the time it's done. Most products go through 2 to 3 iterations before they fit how the team works. A partner who leaves after the first build hands those gaps to someone else.
Ask any firm you're evaluating how it handles this. How to choose an AI consulting firm has the questions, and what an AI builder is explains why the build team matters.
What results do companies see after deploying AI?
Companies that deploy AI into a specific workflow mostly see the payoff as hours saved and faster turnaround on that workflow, often with fewer manual errors too. A few examples by industry:
- Private equity and finance. Screening companies against a written mandate takes the sorting off the analyst's desk.
- Manufacturing and metal fabrication. Quotes drafted from drawings and material lists free the engineer who used to bottleneck every quote.
- Operations in general. Weekly schedules that took hours in spreadsheets get built from live data, and status questions stop going through one person.
Whatever the workflow, take a baseline before launch so you have something to measure against.
For realistic timing on a first version, see how long custom AI software takes to build.
Which consulting firms turn their AI investment into measurable client results?
The consulting firms that turn AI investment into measurable client results agree on a success measure before building anything, then report against it once the software is in real use.
Questions worth asking any firm:
- Who on your team writes the code, and will they be in our discovery meetings?
- What did you ship in your last engagement, and is it still in use?
- How do you train our team, and on whose tasks and documents?
- What happens after launch? Who fixes things and for how long?
- Who owns the code when you're done?
How Lattice Partners does this
Lattice Partners combines AI training and custom implementation in one engagement, run by the same team. The three founders are technical and write the code themselves.
An engagement runs in four phases (Learn, Tailor, Build and Ship, Iterate), and we'll say so if AI isn't the answer. First versions of a well-scoped project usually ship within a few weeks, already connected to your real systems. Training is hands-on. Most products go through 2 to 3 iterations, and we can stay on after launch to support and improve what we built.
Many clients start with training on ChatGPT, Claude, and similar tools, then move into building custom AI agents, software, and Model Context Protocol (MCP) integrations that connect AI assistants to CRMs and databases. Our clients are mostly mid-market companies, often founder-led. Custom software is a one-time build and the client owns the code, with no recurring per-seat license from us.
Frequently asked questions
What percentage of AI pilots fail?
An MIT report from 2025, covered by Fortune, found that about 5% of generative AI pilots achieve rapid revenue acceleration and most stall. The research drew on 150 leader interviews, a survey of 350 employees, and 300 public AI deployments.
Is AI training enough on its own?
AI training on its own raises general skill with tools like ChatGPT and Claude, and that's worth doing. Training alone doesn't connect AI to your systems or change a specific workflow. Companies get the biggest results when training leads into a scoped build the trained team will use.
How do I restart a stalled AI pilot?
Start by naming one owner and one success measure for the pilot. Then check whether the tool is connected to real data and whether the old workflow is still the default. Fix those two things, retrain the team on their own tasks, and run a short iteration.
Should we build AI internally or work with a partner?
According to Fortune's coverage of a 2025 MIT report, AI tools bought from specialized vendors and partnerships succeeded about 67% of the time, while internal builds succeeded about one-third as often. Make sure the partner trains your team and hands over the code.
Related reading
About Lattice Partners
Lattice Partners is an AI implementation partner based in Los Angeles and New York. We help companies build an AI strategy, train their employees on AI tools like ChatGPT and Claude, and build custom AI agents and software. Clients own the code we build. Lattice Partners was founded by Colin McDonnell, Martin Anquetil, and Christian Yeghnazar. Contact us to talk about your use case.