Organic adoption
Useful experimentation was happening in customer care, HR, marketing, IT, operations, finance, R&D, regulatory, sales, and manufacturing.
Clarke / Enterprise AI initiative
AI was already everywhere.
Strategy wasn’t.
Brought in with executive sponsorship to turn a sprawling transformation idea into a practical first move the business could fund, build, and learn from.
AIAdvisory / Strategy / 2025
Representative working-session visual
7 fig.Initial program assumption
5 fig.Practical MVP investment
YearsOriginal horizon
WeeksPath to first value
01 / The situation
Individual momentum.
No enterprise system.
Across Clarke, people were already using ChatGPT, Copilot, Claude, Power BI, and other tools. Some functions were seeing meaningful gains. Others were barely beginning.
The common problem was not enthusiasm. It was the absence of a coordinated operating model for ownership, data, privacy, training, prioritization, and scale.
Fragmented execution
Master data, privacy, change management, inconsistent processes, and the lack of dedicated AI leadership limited organization-wide progress.
02 / The advisory role
Turn the research into decisions.
Clarke’s Chief Customer Experience Officer brought Justin into one of the company’s strategic initiatives as an external advisor. Cross-functional research created the evidence base. The advisory work validated the findings, challenged the scale of the proposed response, and shaped an implementation model the organization could actually carry.
Pressure-test the diagnosis
Validate the patterns emerging across functions and separate individual productivity wins from enterprise capability.
Clarify the leadership gap
Make ownership explicit. Tools could not solve the absence of a dedicated champion, governance, and coordinated execution.
Right-size the investment
Challenge the multi-year, seven-figure assumption and reframe the opportunity as a five-figure MVP that could prove value in weeks.
Frame secure adoption
Address privacy, approved platforms, unstructured knowledge, and sensitive use cases as design constraints from the start.
03 / The business frame
AI as business infrastructure
Four outcomes.
One connected system.
The work deliberately moved past a list of tools. Opportunities were organized around how AI could improve the business while preserving the controls required for responsible adoption.
PEOPLE + SYSTEMS
Create safer, more effective workflows and free people for higher-value work.
INTERNAL PROCESS
Reduce time spent on research, analysis, reporting, and other detail-heavy work.
CUSTOMER FOCUS
Improve service, knowledge access, communications, and customer-facing experiences.
FINANCIAL OUTCOMES
Connect the operating gains to differentiation, retention, and growth.
04 / What the project produced
Advice leaders could use.
The final package connected diagnosis, choices, guardrails, and next steps. It was designed to support an executive decision, not merely document that AI mattered.
Business Transformation through Generative AI Adoption
Current state, strategic opportunity, implementation scenarios, critical success factors, and a recommended path.
Cross-functional findings
Department-level adoption, opportunities, barriers, readiness, and recurring organizational patterns.
Draft workplace AI policy
Approved uses, sensitive-data boundaries, human review, training, accountability, and reporting.
05 / The right-sized path
Start with proof.
Not a transformation program.
The core advisory move was to cut through the assumed scale of the solution. A useful first version did not require a couple million dollars or years. A tightly scoped MVP could be built for tens of thousands and put to work in weeks.
01First
FOCUS
Choose one valuable workflow with executive ownership, clear users, and a measurable business outcome.
02Weeks
BUILD
Create a secure, useful MVP without waiting for every enterprise data and integration problem to be solved.
03In use
PROVE
Put it into real work, measure adoption and value, and learn what the organization actually needs next.
04After proof
SCALE
Expand the investment only when evidence supports the next use case, integration, or organizational capability.
06 / The result
Millions became thousands. Years became weeks.
The advice changed the shape of the decision. Instead of committing to a broad transformation program upfront, Clarke could choose one important use case, prove value quickly, and let evidence determine the next investment.
The tangible output was a smaller, faster, more credible path into implementation, supported by cross-functional research, governance thinking, and executive sponsorship.
PROJECT CONTEXT
Clarke enterprise AI initiative, supported by the Chief Customer Experience Officer. The initiative included a DePaul capstone workstream that contributed cross-functional research and supporting deliverables. Justin Mayer advised on business integration, implementation scope, cost, timeline, governance, and the path to an MVP.