Mid-market AI transformation
Most consultants arrive with a pre-baked AI playbook. We don't. We start where you are — examining the operational reality of your business before we recommend a single tool. That rigor is what makes our work stick.
The process
We do not walk in with a pre-baked playbook. The roadmap comes from your operation, your workforce, and where you are trying to take the business — not from a template we sell to everyone.
Gartner projects forty percent of agentic AI projects will be cancelled before they reach production. The pattern behind those cancellations is familiar to anyone who has sat inside an enterprise transformation: a strong pilot, a weak transition, and a governance gap that nobody named up front.
CPP's process is designed around that pattern — to close the gap before it opens.
We start with the executive team. Not a survey. Not a vendor demo. A structured workshop that maps your operations, names your real constraints, and produces a prioritized list of AI initiatives ranked by impact and effort — not by what a software vendor would like you to buy next. The AI Opportunity Sprint is this phase, productized. One day. Specific deliverables.
We bring in the domain experts your organization needs — FinOps, AI governance, data readiness, cultural impact, agentic development — matched to what the Sprint surfaced. This is not a standing retainer. It is project-scoped engagement, calibrated to where you actually are on the maturity curve and what your people can absorb.
When the strategy is clear and the readiness work is done, we deploy purpose-built agentic solutions inside your operations — operated by CPP. Your team understands what was built and why. You get the outcomes. We sustain the technology.
The leverage of large enterprise. The overhead of mid-market. CPP operates the infrastructure — no dedicated AI team required.
Why sequencing matters
of agentic AI projects will be cancelled before they reach production
Deloitte "Agentic Enterprise 2028" (Sep 2025), citing Gartner June 2025
Who we are
Shea Long and Mike Burns founded CPP because the pattern repeated across every organization they worked inside. The technology was not the problem.
Deloitte surveyed over three thousand director-to-C-suite leaders earlier this year. Eighty-four percent of their organizations had not redesigned a single job around AI capabilities. They had bought the tools. They had run the pilots. They had not done the harder, slower, organizational work that determines whether any of it holds.
Shea and Mike have watched that same gap close — and widen — across decades of operator experience.
Has led product strategy at Centene, ModivCare, Papa, EmpowerMe Wellness, and Alivi. The consistent thread across those organizations: AI transformations do not stall because the models stop working. They stall because no one at the executive level owns the sequencing problem. Shea founded CPP to be the firm that owns it with you.
Spent over thirty years inside healthcare marketing and digital transformation — as CSO at Advocado, President of Health Services at Wunderman Thompson Health, and in senior roles across the KBM and Wunderman networks. He has seen what happens when an organization's AI strategy gets handed to IT. And what happens when it doesn't.
Client testimonials — pending verified quotes
Verified quotes — specific operational outcomes, attributed to named contacts at client organizations — will appear here once supplied. No invented quotes are shipped.
The organizational gap
of companies have not redesigned a single job around AI capabilities
Deloitte "State of AI in the Enterprise" (Jan 2026), Key Findings, p. 12–13
The products
Seventy-four percent of organizations plan to deploy agentic AI within two years. Only twenty-one percent have a mature governance model to manage it. The products CPP ships are built to close that gap.
The advisory process surfaces what your organization actually needs. In most mid-market engagements, that need falls into one of three domains. We have built products in each one.
For CFO / VP Finance / FinOps
Make AI spend visible, attributable, and shrinking.
AI spend is growing in most mid-market organizations faster than the finance team can attribute it. The Phase 1 audit produces an attributed token-and-dollar baseline, a showback and chargeback framework, anomaly and budget alert design, and a right-sizing assessment. You own the output. If the audit confirms a material gap, Phase 2 managed FinOps is available — scoped to what the audit found, not to what we would like to sell next. This is an audit and managed improvement service. It goes upstream of metering. It is not a monitoring dashboard.
For CISO / Risk / Legal / Compliance
Enforce AI policy, prove it held, and produce an audit trail a regulator can read.
Shadow AI is the new shadow IT. The governance product operates across three layers — content, logical, and physical — with a directive-enforcement firmness ladder that separates visibility (what most tools offer) from enforcement (what your CISO needs). The audit trail is immutable and timestamp-verified. The regulator export is available within forty-eight hours. The product is designed to support EU AI Act compliance and NIST AI RMF alignment — not to certify it.
For CTO / VP Engineering
Separate real engineering velocity from the vibe-coding trap.
Most engineering teams are not moving faster because of AI. They are accumulating technical debt faster. The AI Development OS surfaces utilization patterns, output-quality signals, and model-steering guidance inside the IDE where engineers already work. This product is available to a founding cohort of early-access clients. Contact us to discuss scope and timeline.
The governance gap
of organizations plan to deploy agentic AI within two years — only 21% report a mature governance model for autonomous agents
Deloitte "State of AI in the Enterprise" (Jan 2026), Key Findings, p. 5
Coming soon
Shea Long and Mike Burns' operator verdicts on which AI use cases deliver fastest value — and which ones to stop funding today.
Your AI roadmap is probably backwards.
Most organizations go after the projects that sound impressive. The ones that take eighteen months, require twelve engineers, and quietly drain the people who could have moved something forward in eight weeks.
Plot every AI initiative your team has proposed on two axes: impact and effort. Be honest about where each one actually lands. The low-impact, high-effort quadrant is where organizational momentum goes to die. Kill those projects. Do it before they absorb another quarter of your best people's attention.
The 2026 Mid-Market AI Priority Matrix applies this framework to the specific industries CPP serves — automotive, education, telecom, healthcare, hospitality, and manufacturing — with operator verdicts from Shea and Mike on which use cases belong in which quadrant. It is in progress. When it publishes, you will want to be the first to see it.
The entry point
One day. Specific deliverables. Run by Shea Long and Mike Burns.
What you get
How the day runs
Morning: discovery — process mapping, constraint identification, and an optional AI education session for your leadership team.
Afternoon: ideation and prioritization. The Impact × Effort matrix applied to your specific situation, with your specific people in the room.
The pre-work
Eight hours of structured pre-work is included. We capture the CEO's vision, frame short-term and three-year goals, and build a bespoke agenda around your actual operation — not a standard discovery template.