AI Strategy & Roadmap

AI Ambition Without a Fundable Plan

The Problem

Leadership had set a mandate to become a data-driven business — but no way to act on it. Reporting was manual, consuming senior analyst capacity on undifferentiated work. Launches lagged competitors by months. And there was no prioritized, costed roadmap, so every AI proposal stalled at the funding gate because no one could defend the business case to the board.

The Solution

A prioritized digital-transformation roadmap anchored in data analytics and an enterprise AI commercialization strategy. Initiatives sequenced by value, feasibility, risk, and readiness, with a finance-grade ROI model at every gate — so each investment arrived board-ready with a defensible business case, not a tool demo.

Media & Information Services — mid-market (~$40M revenue)

~$1.48M

/yr

~$275K 2 FTE-equiv × $137K loaded

~$600K 3 launches, ~1 qtr earlier

~$600K 20% on ~$3M upsell base

50 %

IT labor ↓

3

launches/yr

20 %

upsell ↑

6–8 wk

To pilot

More Cases

Manufacturing — mid-market (~$500M revenue)

Random Acts of AI, No Prioritization.

The manufacturer had scattered AI experiments with no sequencing logic — automating low-value tasks while high-impact bottlenecks went untouched. Spend was rising without measurable EBITA impact, the pattern McKinsey found in 80%+ of adopters. Leadership couldn’t see which initiatives to fund, kill, or sequence first.

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Financial Services — enterprise (~$2B revenue)

A Sprawling AI Portfolio No One Could Govern

The bank had 20+ uncoordinated AI pilots across divisions, none with shared governance, attribution, or ROI discipline. Financial services carries the highest AI failure rate in market at 82%. With the board demanding demonstrated ROI and regulators circling autonomous decisioning, the program risked mass abandonment and a multi-million-dollar write-off.

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Media & Information Services — mid-market (~$40M revenue)

AI Ambition Without a Fundable Plan

Leadership had set a mandate to become a data-driven business — but no way to act on it. Reporting was manual, consuming senior analyst capacity on undifferentiated work. Launches lagged competitors by months. And there was no prioritized, costed roadmap, so every AI proposal stalled at the funding gate because no one could defend the business case to the board.

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Regional HospitalNetwork (~$800M revenue)

AI-Ready in Ambition, Not in Data

The network wanted AI across diagnostics and intake, but its data sat in fragmented, non-interoperable systems, the exact condition that causes 60% of AI projects to be abandoned.

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