Solutions Overview · Two Practices
Two practices for leadership teams: fractional AI strategy advisory — a roadmap tied to real decisions — and customer experience design and innovation — a journey customers would choose again. Each with the governance, sequencing, and investment narrative to survive contact with the board.
The situation
Most enterprises are not short on AI activity. They are short on AI decisions — the small number of consequential choices that would turn scattered experimentation into operating advantage. Customer experience fails one level over, for the same reason: plenty of channel activity, no agreement on the experience the business intends to deliver. Four patterns show up almost every time, in both.
The path from pilot or prototype to operating advantage — and the four places it breaks
AI initiatives are named after models, tools, and vendors; experience initiatives are named after channels and releases. Nobody can point to the business decision that gets clearer, or the customer moment that gets better, if the work succeeds.
Promising proofs of concept and well-tested design concepts never acquire an owner, a budget line, or an operating model. The demo lands; the transition to something that runs the business never gets designed.
Data, risk, and responsible-adoption questions surface after commitments are made — and so does the question of who actually owns the journey end to end. Both stop the work at the most expensive possible moment.
The board hears capability, the operators hear disruption, finance hears cost, and the customer is described five different ways in the same meeting. Teams are deciding against different pictures of the same program.
The leadership imperative isn't to start with technology. It's to start with the decisions that move the business and the experiences customers actually feel — and then build the AI and design capability that makes both faster, clearer, and worth defending.Skip Vanderburg
The portfolio maps to a short list of business decisions and customer moments, each with an owner.
Work is ordered by value, feasibility, and risk — with an investment narrative attached.
Data, risk, responsible-use, and experience-ownership guardrails are designed in, not retrofitted at review.
Where I add value
Advisory grounded in building the thing and designing the thing, not just recommending either — across the operator, consultant, and founder seats. Most engagements start in one practice; the useful ones end up drawing on both.
Practice one — Fractional AI Strategy Advisor
For leadership teams with AI activity everywhere and AI decisions nowhere — where the constraint is the portfolio, the operating model, and the investment case, not the technology.
Connect AI investments to business decisions, portfolio priorities, measurable outcomes, and responsible adoption — sequenced so each phase funds the next.
Design multi-agent approaches that synthesize evidence, compare scenarios, and improve executive decision velocity rather than adding another dashboard.
Stand up guardrails that survive contact with risk and legal, and translate complex AI choices into clear options and an investment narrative the board can act on.
Practice two — Customer Experience Design & Innovation
For leadership teams whose experience is the sum of what each channel happened to build — where the constraint is intent, ownership, and evidence, not effort.
Set the experience the business intends to deliver, then design the ownership, measures, and operating rhythms that make delivering it somebody’s actual job.
Research the journey customers really take, name the breakpoints that cost revenue and trust, and redesign the service across every channel and team that touches it.
Apply human-centered design and emerging technology to create differentiated products and services — prototyped and tested with customers before they are funded at scale.
Opportunities are ranked with established frameworks rather than intuition — RICE, MoSCoW, Eisenhower, scenario planning, risk assessment, and resource optimization — and, on the experience side, against evidence from customers rather than internal conviction. Either way the roadmap can be defended line by line to finance, risk, and the board.
How we work together
Scoped to the decision — or the experience — in front of you, from a single executive session to ongoing advisory alongside your leadership team. Each format runs as AI strategy work, as customer experience design work, or as one engagement covering both.
A working session with your leadership team: a shared, non-hype view of what AI can and cannot do in your operating context, an honest read on the experience customers get today — and where the real constraint sits.
A defined engagement in one of two shapes — an AI Strategy Sprint producing a roadmap tied to business decisions, or a CX Design Sprint producing a reinvented journey with prototypes customers have actually seen. Both prioritized with established frameworks rather than intuition, and sequenced against real capacity.
Retained advisory alongside your executive team as Fractional AI Strategy Advisor, as Fractional CX & Innovation Leader, or as one seat covering both — the senior perspective in the room, without adding a full-time role or a consulting bench.
Engagements are typically sized after a short scoping conversation; the briefing is often the fastest way to establish which practice the real problem sits in, and whether a sprint or a retained relationship is the right next step. Travel and expenses billed at cost.
Focus sectors
The advisory approach is industry-agnostic, but five sectors carry the combination of decision complexity, regulatory weight, data fragmentation, and customer-experience stakes where getting past the pilot — and past the disjointed journey — matters most.
Underwriting, claims, and distribution each run their own experiments. The portfolio view — what to fund, in what order, against which loss-ratio outcome — is missing, and the policyholder feels every seam between them at exactly the wrong moment.
Opening move · A single prioritized roadmap across underwriting, claims, and service, with a governance layer above it — and the claim journey redesigned end to end, because that is where trust is won or lost.
Clinical and administrative AI advance on separate tracks, and governance questions — privacy, clinical accountability, model oversight — arrive after the pilot has already been promised. Meanwhile the patient navigates access, scheduling, and billing as three unrelated companies.
Opening move · Separate the decisions that carry clinical risk from the ones that don't, sequence accordingly — and treat access-to-outcome as one designed journey rather than three handoffs.
Model risk management and regulatory expectation set the pace, and ambitious AI programs stall waiting for a defensible control story. On the front end, onboarding and servicing experiences are still assembled from products rather than designed around customers.
Opening move · Build the governance and evidence trail into the roadmap rather than bolting it on at review — and redesign onboarding around the customer’s goal, not the product catalog.
No shortage of capability — the constraint is prioritization. Too many credible AI bets, too little agreement on which ones earn engineering capacity this year, and a product experience that has accumulated more surface than intent.
Opening move · Rank the portfolio with explicit frameworks so the trade-offs are visible and arguable — then prototype the two or three experience changes that would move adoption before writing the rest of the plan.
Operational data is abundant and fragmented; AI initiatives cluster at the plant level and rarely aggregate into an enterprise capability. The dealer, distributor, and end-customer experience is usually somebody’s side responsibility.
Opening move · Identify the recurring enterprise decisions the plant data should be feeding, build toward those — and give the aftermarket and service experience an owner and a design.
Sector framing is a starting point, not a template. Every engagement begins with your operating context, the AI work already in flight, the experience your customers get today, and the decisions your leadership team is actually trying to get right.
Background
Thirty-five years translating emerging technology and human-centered design into enterprise strategy, products, customer experiences, and operating advantage — at Fortune 500 companies, global consultancies, enterprise software firms, and as a founder of both a digital experience agency and an enterprise AI company. Two decades of it spent leading customer experience design and innovation, which is why the two practices come from one career rather than two business cards.
Years across enterprise technology, customer experience design, and AI
Beta customers validating agentic decision intelligence at Prioriti AI
Published books on generative AI, decision intelligence, and prioritization
Years leading customer experience design, innovation, and service reinvention
Let's talk
If your team is weighing where to invest in AI, trying to move from pilots to something that actually runs the business, or looking hard at a customer experience that no longer holds up — a short conversation is usually the fastest way to find out whether I can help. Either practice, or both.
