Start with the right AI opportunities, the right architecture and a clear path to production. Our AI Readiness Assessment helps enterprises evaluate AI opportunities, assess readiness and build a practical roadmap, with access to the AWS AI Assessment and, where relevant, AWS Programming for Agentic Process Transformation (APT).
Most AI programs don't fail on the model. They fail on the choices made before anyone builds.
Every team wants its AI project built first. Without a way to rank them, budget spreads thin and nothing ships.
The business case looks strong, then messy data, disconnected systems and unclear ownership stall the build.
Gaps in data, cloud, security and integration stay hidden until they blow the timeline and the budget.
A familiar model gets chosen before anyone tests it, locking in cost and limits that don't fit the use case.
There is no baseline for data quality, skills or platform maturity, so every estimate is a guess.
Board decks describe AI ambition but never say what to build first, what it costs or who owns it.
Use cases get chosen by whoever's loudest, not by scoring business impact against effort and feasibility. Our Use Case Prioritization Matrix scores every candidate the same way, so the ranking comes from evidence, not whoever pitched hardest in the room.
Nobody scores the data, security posture, or team capability a use case actually depends on. We deliver a Technical Readiness Score out of 10, covering cloud maturity, team capability, data readiness, and security posture.
Models get picked because they're well known, not because they were tested against your real queries. Our Model Evaluation Report runs a head-to-head comparison on accuracy, latency, cost, and response quality.
"We'll figure it out as we go" is why most pilots stall before production. We deliver an Implementation Roadmap, phase by phase, with objectives, key activities, and deliverables named for each stage.
Fixed scope and fixed timeline, so the assessment itself doesn't become the thing that stalls.
Stakeholder interviews with technical leadership, business leadership, and the teams who run the process day to day. Current-state documentation review of what's already built, tried, and known not to work. Pain point and objective mapping grounded in what the business is actually trying to achieve.
Technical infrastructure assessment covering cloud maturity, existing AI/ML capability, and integration readiness. Data quality and availability evaluation — the single most common reason pilots stall after they're greenlit. Security and compliance review against regulatory requirements, data residency, and security posture.
Use case prioritization workshop scoring business impact, effort, and feasibility together, not in isolation. Model evaluation with head-to-head testing on accuracy, latency, cost, and quality for the highest-priority use case. Draft architecture design as a first pass at the technical approach the roadmap will formalize.
Implementation roadmap, phase by phase, with objectives, key activities, and deliverables for each stage. ROI and business case tied to the metrics that matter to the business, not vanity AI metrics. Final report and readout delivered to stakeholders with a named 30-day next step, so momentum doesn't stall after the assessment ends.
Key findings, a business impact summary, and immediate next steps, on one page leadership can act on.
Every candidate use case scored on business impact, implementation effort, and technical feasibility.
A scored baseline, out of 10, covering cloud maturity, team capability, data readiness, and security posture.
Head-to-head comparison of foundation models on accuracy, latency, cost, and response quality, tested against real queries.
A phased plan, by month, with objectives, key activities, and deliverables named for each phase.
A 30-day action plan with the single highest-priority next step named explicitly.
For teams already running GenAI in production, often on another cloud, who need a scored plan to migrate and consolidate onto AWS without losing what's already working.
For a team already running GenAI in production on another cloud, a scored plan was built to migrate and consolidate onto AWS without losing what was already working.
For teams starting from a business problem, not an existing deployment. Discovers, scores, and prioritizes candidate use cases from a blank slate.
For a team starting from a business problem rather than an existing deployment, candidate use cases were discovered, scored, and prioritized from a blank slate.
A recent assessment found the cost-optimized model matched the flagship model's quality score while costing roughly a fifth as much per session — a finding that only shows up when models are tested against real queries instead of chosen by reputation.
“We'd sat through three vendor demos and picked a GenAI use case because a director liked the pitch. Flentas' assessment scored twelve candidate use cases against effort and feasibility, found data gaps in the two we'd already told the board we'd build, and handed us a model evaluation showing our flagship-model plan would cost roughly five times more than a model that scored just as well. Four weeks, and we walked into the next board meeting with a roadmap instead of a hunch.”
Chief Data OfficerEnterprise Financial Services Firm, India
For teams already running GenAI in production, often on another cloud, who need a scored plan to migrate and consolidate onto AWS without losing what's already working.
For teams starting from a business problem, not an existing deployment. Discovers, scores, and prioritizes candidate use cases from a blank slate.
One engagement is one stage. Here is what usually comes before and after, so the next step is always clear.
A scored roadmap for which AI use case to build first and what it needs.
Build and run production agents with governance and cost under control.
Explore Agentic AI SolutionsKnow what a change will break before it ships.
Explore AI in SDLCGuardrails and evidence for the AI you are putting into production.
Explore AI Security AssessmentFour weeks from kickoff to a report your leadership team can act on: which use case to build first, what your stack actually needs, which model to use, and a phased plan to get there.