Work examples

What the work has produced

These seven examples cover AI strategy, governance, enablement, executive coaching and working prototypes. Most of the work was completed inside organizations and is anonymized; the coaching and prototype engagements are my own. Each example states what Bill did, what it produced and why that work matters to a company facing the same kind of decision.

Dozens

Use cases evaluated

Functions convened

100+

Professionals taught

25+

Working AI tools built

Strategy and governance

From an AI idea to a decision people can defend

Three examples of turning cross-functional uncertainty into policy, priorities and an accountable route forward.

Governance

Adoption guidance for people managers

Financial Services · regulated company

What I did

Facilitated a cross-functional group of HR, IT, Legal, Data and Compliance leaders to resolve the trust and policy questions blocking adoption, then turned the answer into language a non-technical manager could apply. The guidance became the standing enterprise reference for people leaders.

Why it matters

When a question crosses several departments, it can belong to none of them. Progress requires someone to convene the right people, settle the issue and write the answer down.

Functions at the table: 5
Employees covered: 5,000
Standing policy: 1

Strategy and portfolio

AI use-case intake, scoring and prioritization

Financial Services · business units submitting ideas faster than they could be evaluated

What I did

Served on the governance body and helped establish the enterprise AI use-case intake and review process. Supported evaluation of use cases against a framework weighing risk profile, adoption readiness and time-to-value.

Why it matters

A common set of criteria and a clear route from idea to decision turn an unranked list into a plan — and produce a defensible ‘not yet’ pile.

Use cases: Dozens
Scoring dimensions: 13
Intake process in use: 1

Strategy and executive engagement

Executive interviews turned into a prioritized AI roadmap

Financial Services · C-Suite and leadership teams

What I did

Interviewed the C-Suite and their direct reports to surface competing priorities, risk tolerance and definitions of change success, then translated those interviews into a prioritized adoption roadmap rather than a summary of opinions.

Why it matters

Leadership teams often disagree less about AI than about what success means. The roadmap puts those definitions in the same document and makes the trade-offs visible.

Executives interviewed: 30+
Interviewed directly: C-Suite
Prioritized roadmap: 1

Enablement and coaching

Changing what people do after the tools arrive

Training, adoption architecture and direct work with leaders — all grounded in the decisions and tasks already in front of them.

Enablement

AI training for people leaders

Same carrier · people leaders across a distributed workforce

What I did

Helped design and facilitate an AI enablement course embedded in the required development framework for people leaders: how the tools behave, where the guardrails sit and how to apply them day to day.

Why it matters

Training changes behavior when it uses the work people already have in front of them and is followed by reinforcement. That is the difference between enablement and a demonstration.

Professionals taught: 70+
Leaders trained: 100+
Change leadership course: C-suite

Enablement architecture

Persona-based adoption and a champion network

Same carrier · fragmented unsanctioned AI use and visible displacement anxiety

What I did

Developed a four-persona adoption framework that sequenced awareness, capability and reinforcement differently by audience.

Why it matters

Different groups need different reasons and support to change their behavior. Sending everyone the same message leaves most people without a reason to act.

Employee personas: 4
Champion network members: Dozens
Low-risk use cases mapped: 12

Coaching

One-to-one AI coaching for senior executives

Mid-market · manufacturing

What I did

Runs one-to-one coaching with C-suite leaders at mid-market companies, working through their actual decisions rather than a curriculum: what AI is useful for in their operation, where the risk sits, what to say to their teams and which ideas are worth pursuing.

Why it matters

A private session makes room for the question a leader may not ask in a group — often the question holding the next decision up.

Mid-market clients: Multiple
Who is in the room: C-suite
Format: 1:1 and Group Workshops

07 · Working prototypes

Twenty-three AI tools across seven business functions

These working examples show that I can take an operational idea far enough to test its shape and limits. They are proof of capability, not a third service or a catalogue of ready-made automations.

Two were delivered to mid-market clients. Any version used by another company would still need to be designed around its own data, risk appetite, costs and existing tools.

5 Sales: Lead generator, prospect pilot, ROI calculator
4 Finance: Finance autopilot, cashflow watcher, payments
3 Governance: Risk assessment, prioritizer, readiness chat
1 Vendor: Vendor cost and renewal check
4 Customer: Onboarding, health monitor, account scanner
3 Marketing: Ad copywriter, social agent, blog writer
3 Operations: SOP writer, quote kit, inventory

Most examples above come from work inside large organizations. The coaching and prototype engagements are my own. Employers and clients are anonymized, and every claim reflects what I can state on the record.

Bring the decision that is holding your AI work up

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