AI Strategy

Here is how most AI projects start: someone reads an article, pitches it to the CEO, a vendor gets hired, and six months later there is a proof-of-concept that works in a demo but nobody uses in production. The vendor moves on. The POC sits in a repo. The company concludes "AI does not work for us."

AI worked fine. The strategy did not.

The three failure modes we see repeatedly

First: starting with the technology instead of the decision. "We should use LLMs" is not a strategy. "We need to cut survey analysis from 3 weeks to 3 hours" is. The technology follows the problem.

Second: skipping the data assessment. An AI model is only as good as the data it trains on. If your customer records have 40% missing fields, a churn model will predict noise. We have walked away from AI pitches and recommended data cleanup instead. It is not what the client wanted to hear, but it saved them six figures.

Third: building without guardrails. An LLM that hallucinates in a demo is a curiosity. An LLM that hallucinates in a patient-facing system is a liability. Every AI deployment needs explicit boundaries for what it can and cannot do, how errors are caught, and who is accountable.

What an engagement looks like

We start with AI Opportunity Mapping: a structured 2-3 week assessment that evaluates your operations, data readiness, and team capabilities against a library of proven AI use cases. The output is a ranked list of opportunities — scored by impact, feasibility, and data readiness — with a recommended first pilot.

Then we build the pilot. Not a slide deck about what AI could do. A working system.

For a healthcare provider, that meant a pipeline from SurveyMonkey responses through AWS Lambda and ChatGPT API into Snowflake — turning patient surveys into structured insights in minutes instead of weeks. For eBay, it was a churn prediction system — detailed in our Customer & Marketing Insights work — where the AI strategy question was not "can we predict churn" but "can we make the prediction actionable within the existing retention workflow?" The answer required integrating the model output with CRM triggers and marketing automation, so the retention team received scored lists they could act on immediately rather than a CSV they had to interpret.

For a cloud security platform, we delivered an FY27 pricing model in 9 days using Claude Code across 28 focused sessions. Nine days. Not because we cut corners — because the right architecture (structured seed tables, temporal lookups, parallel validation) eliminated the manual work that usually stretches pricing projects to quarters.

What we leave behind

Beyond the working pilot: an AI guardrails starter document tailored to your industry and risk profile. A data readiness scorecard showing which additional use cases your current data can support and which need investment first. A handoff plan so your team can operate, monitor, and iterate on the pilot without us.

When AI is not the answer

If a SQL query and a well-designed dashboard solve the problem, AI adds complexity without value. If your data is not clean or centralized, AI will amplify the inconsistencies. We will tell you during the Opportunity Mapping phase — roughly 30% of the use cases clients bring to us are better solved with conventional analytics.

Questions worth asking before you invest

Can you describe the specific decision this AI system would improve — and how you measure that improvement today without AI? Do you have at least 6 months of clean, labeled data for the process you want to automate? If the AI system makes a mistake, what is the cost — and who catches it?