AI Direction

AI strategy for mid-size companies, starting with the pilots you already have

Your AI pilots are multiplying or stalled, and nobody can say what the plan is. The board is asking about AI strategy, and the honest answer is a list of tools. That’s the most common place we meet a leadership team, and it’s a fixable one.

What you're probably seeing

Pilots keep launching, and none of them have a finish line.
Nobody can say which experiments deserve more money next quarter.
A pilot that should have been stopped months ago is still running, because stopping it feels like admitting failure.
Teams were told to use AI, and adoption stalled because people don’t trust what it gives them.
Someone owns “AI” on paper, but they don’t have the authority or budget to make a decision stick.
AI is already showing up in places nobody planned for: vendor features, personal tools, customer channels.

A quick self-check

Answer yes or no. If you say yes to two or more, a 20-minute conversation is worth your time.

Do you have more than three AI pilots running, with no agreed rule for which ones continue?
Has any pilot run for six months or more without a decision to scale it or stop it?
Would your leadership team give different answers to “what is our top AI priority?”
Is the person accountable for AI missing the authority or the budget to act on it?
Are people quietly working around the AI tools you rolled out?
Is AI being used somewhere in the business that leadership hasn’t decided on?

How we help

We don’t start with tools. We start with the decisions your leadership team hasn’t been able to make yet.

1. See what's actually running

Every pilot, tool, and vendor feature that touches the business, who owns it, and what it was supposed to prove. Most leadership teams are surprised by the list.

2. Decide what scales and what stops

Pilots are supposed to fail cheaply and early, like good experiments. The real failure is the pilot nobody was allowed to end. We help you set the bar for what continues, then make the calls.

3. Build direction people will follow

Clear owners, sensible guardrails, and a rhythm for the next decision, so AI stops being a pile of experiments and becomes part of how the business runs. That includes the people side: adoption follows trust, and trust has to be earned.

The same problem, before it was called AI

Getting pilots out of purgatory isn’t new. Here’s how we did it inside a business IT solutions and services firm.

Getting stalled pilots out of purgatory and into production

The friction

After a long run of revenue-driven growth, engineering engagement had collapsed. Top talent was at risk of leaving, teams were heads-down finishing engagements with no time or incentive to improve, and there was no way to capture what worked or fund what should scale. Good ideas had nowhere to go.

The intervention

We started with diagnosis, not deployment, engaging the engineers directly to understand their frustrations and aspirations. Then we built the structure that turns interest into outcomes: engineer-led communities of practice, a leadership-backed hackathon, and a process to capture learnings after every engagement, with leadership funding the promising ideas through regular innovation reviews.

The outcome

Over half the engineering organization took part, three hackathon pilots became new service lines or internal tools, and the company built a lasting rhythm of continuous improvement, with morale and engagement rebounding.

Questions leaders ask about AI direction

What does an AI strategy for a mid-size company actually include?

Three things: a clear view of where AI already touches the business, a rule for which initiatives get funded and which get stopped, and named owners with the authority to act. A list of tools isn’t a strategy. A strategy tells you what to do next quarter and what to stop doing.

Why do so many AI pilots stall?

Most pilots aren’t killed. They’re never allowed to die. They start without a finish line, nobody defines what success looks like, and ending one feels like failure, so it lingers. The fix is deciding up front what a pilot has to prove and when you’ll decide.

How do we decide which AI pilots to stop?

Judge each pilot against what it was supposed to prove, not how much has been spent on it. If it hasn’t shown a path to real use by the people who would rely on it, stop it and keep what you learned. Stopping a pilot on purpose is a good outcome.

Do we need a Chief AI Officer?

Not always. What you need is someone who owns the decisions about AI and has the authority and budget to make them stick. Many mid-size companies give the title without the authority, which leaves that person stranded. Get the mandate right first, then decide on the title.

Where should a mid-size company start with AI?

Start with what’s already running, not with a new tool. Most companies have more AI in use than leadership realizes, through pilots, vendor features, and people’s own tools. Seeing that clearly is the fastest way to a plan.

Start with 20 minutes

No pitch. Tell us what’s going on with your AI efforts, and we’ll tell you honestly whether we can help.

Direction Beats Chaos^™

A Pilot & Rutter principle