How a broader AI Decision Sprint creates an AI roadmap

The gap between knowing what to build and actually shipping it is where most AI programs stall. This article is about the broader, multi-candidate Sprint offered toward the upper end of the $15k–$40k range. That scope can turn several candidate opportunities into a sequenced plan a team can execute, with funding gates and business cases leadership can defend.

Every AI Decision Sprint, including a focused lower-end Sprint, ends with an evidence-backed build or no-build decision, written reasoning, business and readiness evidence, principal risks and assumptions, and a costed next step. Only the broader scope may add the portfolio-level artifacts discussed here.

What the broader roadmap package may include

• Opportunity map and scored backlog. Multiple candidates compared on value, feasibility, readiness, and risk.
• Business cases and phased roadmap. The sequence, funding gates, and measures that determine whether each phase advances.
• Architecture blueprint and evaluation plan. How components, data, controls, and tests fit together before implementation.
• Vendor assessment. Requirements-based analysis of where to build, buy, or assemble.

From opportunity map to sequenced roadmap

In a broader multi-candidate Sprint, an opportunity map can compare several workflows and support a recommendation for where to start. A strategy turns that list into an order of operations, and the order matters more than the list. Some workflows are quick wins that build organizational trust; others are foundational — a permissioned knowledge layer, a clean integration point — that later workflows depend on and that deliver little visible value on their own. Sequencing badly means shipping three flashy pilots that all rebuild the same missing plumbing. McKinsey’s 2025 State of AI research found that its highest-performing respondents were nearly three times as likely as others to have fundamentally redesigned individual workflows rather than dropping AI into an unchanged process; a roadmap that sequences by “what's easy to demo” instead of “what the workflow actually requires to change” tends to produce the pilots that never make it past the demo.

Kill criteria, defined before the build starts

Every workflow on the roadmap needs a pre-agreed measure of success and a pre-agreed threshold for stopping or redirecting, decided before the build starts, not argued about after money has been spent. This matters because intuition about AI performance is unreliable even for sophisticated users: a 2025 METR study found that experienced developers using AI coding tools on tasks in their own codebases were measured 19% slower than working without them, despite predicting beforehand that the tools would speed them up, and still believing afterward that they had been faster. If people that close to the work can be confidently wrong about the direction of the effect, a roadmap cannot rely on impressions. It needs a metric, a baseline, and a date by which the metric is checked.

Architecture before code

Most AI work fails on architectural decisions, not model choice. A good strategy settles data flows, integration points, evaluation, and failure modes before the build. That is why Celadon leads with architecture. MIT’s 2025 NANDA research found that only around 5% of organizations were seeing measurable profit-and-loss impact from custom, integrated AI initiatives, despite far broader pilot activity. The gap sits mostly at the integration and architecture layer, not the model layer. Off-the-shelf tools adopted informally showed faster, if narrower, returns than custom builds that skipped this step. See build-vs-buy and What is RAG.

Funding gates, not one lump budget

A roadmap should be funded in stages, with each stage’s budget released against evidence from the last one, not approved as a single annual number up front. The Sprint’s costed next step can define the first build funding gate. Reviews at pilot, production, and scale can then check results against the kill criteria before the next stage is funded. BCG’s widely cited research on where AI value actually comes from puts a rough split on this: about 10% from the algorithm, 20% from the underlying technology and data, and 70% from process and people change. A funding model that pays for tooling up front and treats adoption, training, and workflow redesign as an afterthought is funding the smallest part of the problem first.

How this lives inside the Decision Sprint

Firms often ask why Celadon does not sell “strategy” as its own multi-month engagement. A strategy written without specific workflows, evidence, owners, and decision gates tends to be a document people agree with and then do not use. A broader multi-candidate Sprint toward the upper end of the range may produce the opportunity map, scored backlog, architecture blueprint, evaluation plan, vendor assessment, and phased roadmap together. A focused Sprint does not promise those artifacts; it promises the core decision record and costed next step.

It starts with a clear-eyed decision process. Explore the AI Decision Sprint service or review a sample Decision Sprint deliverable to see the core decision record and how broader scopes can extend it into a roadmap.