AI Strategy Consulting
Fit Beats Hype On Draft Day
It feels like every competitor just announced an AI something, and the pressure to buy 1 of your own is real. We scout first. We find where AI will actually pay off in your business and where it won't, check whether your data and systems are ready for it, and build the plan, starting from your systems and how your people actually work. Then we keep score.
Payback Not pilots launched.
How Leaders Decide About AI
Most AI decisions aren't made on a spreadsheet. They're made under pressure, with a lot of noise and very little certainty, and the same old mental shortcuts do the deciding. Here's the research we scout with.
Known Odds Win
Ambiguity aversion · Ellsberg, 1961
Ellsberg asked colleagues, leading economists among them, to make a hypothetical bet on drawing a red ball from an urn with exactly 50 red and 50 black balls, or from an urn holding 100 red and black balls in an unknown mix. Most chose the known urn, though a few deliberately took the unknown 1. To most leadership teams, AI is the unknown urn. So the assessment's job is to swap "we don't know" for known odds: what's ready, what isn't, and what each project needs first.
Trust The Number, Check The Number
Algorithm appreciation · Logg, Minson and Moore, 2019
Across 6 experiments, lay people leaned on advice more when told it came from an algorithm than from a person. Experienced forecasters leaned on it less, and their accuracy suffered for it. So your newer people may take an AI answer at face value while your veterans wave it off. We plan for both: answers that show where they came from, and a person checking wherever a wrong answer costs real money.
Hype Isn't A Scouting Report
The bandwagon effect · Leibenstein, 1950
Harvey Leibenstein wrote that demand for something grows simply because others are buying it, so people can get into "the swim of things". Plenty of AI gets bought exactly that way. We scout your business before anyone signs anything: where AI would earn its spot, and where a competitor's press release is the only reason to buy.
Start Where Nobody Loses
Loss aversion · Kahneman and Tversky, 1979
In prospect theory, people's sense of value runs steeper for losses than for gains, so a possible loss weighs more than an equal gain. A team asked to hand work to AI feels what it could lose before it sees what it could gain. So the first project we recommend is small and easy to reverse, and it takes a chore off the team's plate rather than a job they're proud of.
What AI Needs Before It Earns A Spot
The people who build AI are refreshingly blunt about what it runs on. Google's own rules for its machine learning engineers open with "machine learning is cool, but it requires data," and say most of the gains come from great features, not great algorithms. Google's guide to designing AI products adds that because AI runs on probability, nobody should trust it completely; people need to know when to trust it and when to use their own judgment. And when BCG surveyed 1,000 executives in 2024, 74% of companies had yet to show tangible value from AI. The companies leading on AI put 70% of their resources into people and processes, 20% into technology and data and 10% into algorithms. So before anyone picks a tool, we check the 3 measurables AI actually runs on: clean data, clear jobs and people who'll trust it the right amount.
Your CRM
HubSpot says its Breeze Assistant pulls from your CRM data and customer records, so duplicates and blank fields there turn into gaps in its answers.
Your Data
A model learns from what it's given. Data scattered across spreadsheets and systems that don't talk has to be gathered and cleaned before AI can use it.
Your Jobs
We look for work with a clear input and a right answer somebody can check. A fuzzy job gets a fuzzy result.
Your Team
People should know when to trust an AI answer and when to overrule it, so we design the handoffs between people and AI on purpose.
Why Your First AI Move Might Not Be AI
Google's rules for its machine learning engineers start with this: "Don't be afraid to launch a product without machine learning." The same rule says that if machine learning would give you a 100% boost, a simple heuristic will get you 50% of the way there. So when a clean field, a fixed rule or a basic automation in your CRM already does the job, that's where we start. It ships sooner, nobody has to learn to trust it, and it can start collecting the clean data a later AI project would need.
You don't spend a 1st-round pick on a job a walk-on handles.
From Scouting Report To Draft Board
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Scout The Business
A kickoff working session with your team: where time and money leak today, and which jobs repeat often enough for AI to take on, in your team's own words.
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Take The Measurables
Your systems, your data and your CRM, reviewed for whether AI can actually use them. We read them, we don't edit them, and you can revoke our access whenever you like.
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Grade The Fit
Every idea scored on what it's worth, what could go wrong and what has to be true first. Hype earns no points.
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Set The Draft Board
A walkthrough of the findings with the people who'll use them, ending in a ranked plan: what goes first, what waits, and what to skip entirely, each with the reason.
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Track The Rookies
Once the first projects go live, we measure what they return against the starting line before the next pick is made.
Judge AI By What It Gives Back
A slick demo isn't a result. We keep score on the time, accuracy and money AI hands back to your business.
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Hours Returned
Time your team gets back each week from work AI now handles.
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Live Projects
AI work in daily use, not parked as a pilot.
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Answer Accuracy
How often the AI gets it right, checked against a person who knows the job.
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Payback Time
The months until a project has returned what it cost.
Who's In The Draft Room
- Jeremy Wayne Howell Leads The Scouting
Personally decides where AI fits in your business, where it doesn't, and what goes first.
- Azra Hajdar Runs The Draft Room
Your day-to-day contact, keeping the assessment moving and every finding in front of you.
Fair Questions, Straight Answers
What Is AI Strategy Consulting?
Help deciding where AI will pay off in your business, where it won't, and what has to be true before you spend on it. With us, the result is a ranked plan: what to do first, what waits, and what to skip.
What Does An AI Readiness Assessment Check?
4 things. Your data: is it complete, current and in 1 place? Your systems: can they share it? Your jobs: are they repeatable, with a right answer you can check? Your people: will they use it, and know when to overrule it? You walk away with 4 things too: a ranked plan, a readiness scorecard, the first project scoped and a data cleanup list that pays off even if you never build any AI.
Where Does The Plan Start?
With your systems and how your people actually work, because that's what any AI will run on. Your CRM is usually part of it, and if the records are full of duplicates and blanks, anything built on top of them inherits the mess. Clean data first, and every AI project after it starts on solid ground.
What If Our Data Isn't Ready For AI?
Then that's the first finding, and it's fixable. Cleaning and connecting your data is ordinary work, and your reports get better from it long before any AI arrives.
How Will We Know AI Is Paying Off?
We set the starting line before anything launches: the hours a job takes, how often it goes wrong, how long customers wait. Then we measure the same things after. A project that can't show a return gets cut.
The Next Play
AI Agents And Automation
The scouting report names the job. Somebody still has to show up Monday and do it. Meet the roster.
Door #2Custom AI Systems
Off-the-shelf AI fits most businesses the way a rental tux fits most grooms. Here's the tailored version.
Door #3Psychology Of AI
Every algorithm has a personality, and it's borrowed from whoever built it. Here's how to read it.
Let's Scout Your Business
Tell us the work that eats your team's week. We'll tell you whether AI is a fit for it, and what we'd check first.
Get a Free Audit