Consulting & Advisory
Enterprise AI that is planned around outcomes
Know which AI to build, what to invest in, what to fix first and how you will measure success, before you commit the budget. Then move from pilot to production through milestones tied to business value.
Every step has an exit test. Funding follows evidence.
Why so many AI programs stall between pilot and production
The models are rarely the problem. The plan around them usually is.
Use cases chosen for novelty
Pilots picked because they demo well, not because they move a business number.
Data that is not ready
Knowledge scattered, definitions inconsistent, quality unknown. The AI inherits all of it.
Investments made out of order
Tools and licences bought before the use case, data and operating model are clear.
No baseline, no proof
Without a measured starting point, nobody can show the AI made a difference.
Risk handled at the end
Security, privacy and governance reviews that block go-live instead of shaping the design.
No owner after launch
Nobody accountable for adoption, quality, cost and improvement once the project team leaves.
Five questions we answer with you
Our advisory work is organized around the decisions that determine whether AI delivers value.
Question 1
How should we approach AI solutioning?
Start from the business problem and work back to the right pattern: analytics agent, knowledge assistant, decision support, workflow automation or agents working across systems.
- Use-case discovery workshops
- Pattern and architecture selection
- Build, buy or extend decisions
Question 2
Which technology should we invest in?
The right mix of platform-native AI, foundation models, orchestration and tooling for your use cases, skills and risk appetite, without locking you in.
- Model and platform options compared
- Total cost to build and run
- Vendor-neutral recommendation
Question 3
What do we need to fix first?
The gaps between where you are and what your priority use cases need, ranked by impact: data, context, platform, security, skills and process.
- AI readiness assessment
- Foundation gaps prioritized
- Fix-first plan
Question 4
What should we measure?
Business outcomes first, with adoption, quality, cost and risk metrics underneath so you can see why value is or is not arriving.
- Baseline before build
- Layered metric framework
- Dashboards owners actually use
Question 5
What milestones prove progress?
A gated path with exit criteria at each stage, so investment grows with evidence and stalled ideas stop early.
- Gate criteria and owners
- Outcome-linked funding
- Scale-out plan
The result
An AI roadmap your CFO can back
Prioritized use cases, the investments behind them, the fixes they depend on, how value will be measured and the milestones that release each next step.
Schedule a consultChoosing where to start
We score candidate use cases on business value and feasibility with your teams, then sequence them so early wins fund and inform the bigger bets.
Foundation first
High value, but the data or process is not ready. Fix the foundation, then build.
Example: enterprise-wide forecasting on inconsistent master data
Start here
High value and feasible now. Your first production use cases.
Example: natural-language analytics on governed sales data
Park
Low value and hard. Revisit only if the business case changes.
Example: custom models where off-the-shelf works
Quick wins
Feasible and modest value. Good for building skills and trust.
Example: policy and knowledge assistants for internal teams
What to fix: a quick AI readiness check
Rate where you are on the six foundations we assess. You will see where we would start. Nothing you select here is sent anywhere.
Your readiness
0 of 12
Rate each foundation to see your result.
Where we would start
Your weakest foundation will appear here, with the first fix we would recommend.
Get a full readiness assessmentChoosing the right technology investments
We compare options at each layer against your use cases, data, skills and risk appetite. Most clients get further, faster, by building on the AI already available in their data platform.
What to measure
We agree the baseline before anything is built, and track value in layers so you can see what is driving it, or holding it back.
AI cost visibility and control
As AI scales and agents take on multi-step work, cost stops being a rounding error. One agent request can trigger many model calls, retrievals and tool actions. We help you see what every use case costs, tie it to the value it creates, and put controls in place before the bill surprises you.
Where agentic AI cost hides
- Agent loops and retriesPlanning, reflection and tool calls multiply model usage for a single task.
- Growing contextLong prompts, chat history and retrieved documents inflate tokens on every call.
- One model for everythingFrontier models used for simple steps a smaller model would handle.
- Hidden platform consumptionVector search, embeddings, compute and data movement billed in different places.
- No attributionSpend lands in a shared account, so nobody knows which use case, team or agent drove it.
- Shadow AITeams buying tools and API keys outside any governance or budget.
See how agent cost multiplies
Adjust the numbers to estimate model spend for one agentic use case.
Illustrative estimate of model tokens only. Real costs vary by model, platform, pricing and architecture, and exclude retrieval, compute and data costs.
See it
Visibility
- Tagging and attribution by use case, team and agent
- Unified view across model, platform and cloud bills
- Real-time cost dashboards
Tie it to value
Unit economics
- Cost per task, conversation or resolved case
- Cost compared with the value delivered
- Forecasts as usage grows
Control it
Guardrails
- Budgets, quotas and alerts
- Step and retry limits for agents
- Approval for new models and tools
Optimize it
Efficiency
- Routing simple steps to smaller models
- Caching and context trimming
- Batching and right-sized infrastructure
FinOps for AI, built in from the startWe set up showback or chargeback, cost ownership and review routines so cost scales with value, not ahead of it.
Talk to us about AI costMilestones for outcome-based AI implementation
Each gate has clear exit criteria. Passing a gate releases the next investment. Commercials can be linked to the same milestones.
Value hypothesis
The business outcome, owner, users and baseline are agreed, along with how success will be measured.
Data and feasibility
The approach works on your real data, inside your security and governance guardrails.
Pilot in production
A real group of users runs it in their actual workflow, with monitoring and support in place.
Value measured
Results are measured against the Gate 0 baseline and the business case is confirmed or revised.
Scale
Expand to more users, processes and domains, with an operating model that owns quality and cost.
Ways to engage
Start small and specific, then scale with the same team.
AI Strategy and Readiness Assessment
- Readiness across six foundations
- Use-case portfolio and prioritization
- Investment and fix-first roadmap
Outcome: an AI roadmap leadership can fund
Ask about the assessmentUse-Case Discovery Sprint
- Business and IT workshops
- Value and feasibility scoring
- Top use cases with baselines
Outcome: your first Gate 0 candidates
Ask about the sprintPilot to Production
- One use case through Gates 1 to 3
- Forward deployed engineers
- Measured value against baseline
Outcome: AI in production, with proof
Ask about pilot to productionAI Governance and Operating Model
- Policies, guardrails and risk controls
- AI CoE and ownership design
- Run, monitor and improve
Outcome: AI you can scale safely
Ask about governanceWhy KPI Partners
Enterprise AI advice from a team that spent 20 years building the data and analytics foundations AI depends on.
20 years of advisory and implementation
Since 2006 we have advised on and delivered data, analytics and AI programs for enterprises across North America, Europe and Asia. Our recommendations come from what we have seen work in production.
Unbiased by design
We hold partnerships across AWS, Microsoft, Google Cloud, Databricks, Snowflake, Oracle, ServiceNow and Anthropic. No single vendor decides our advice, so we recommend the mix that fits your problem, not our quota.
Award-winning forward deployed engineering talent
Named Best Firm for FDE Talent by AIM for FY26-27. Forward deployed engineers sit with your teams, inside your environment, turning the recommendation into working software and handing over the know-how as they go.
Data foundations first
Deep expertise in data platforms, modernization, master data, data quality and governance. We know that analytics and AI are only as good as the data beneath them.
Analytics for global customers long before AI
350+ customers and 1,000+ projects across ERP, CRM, supply chain, finance and HR analytics. We understand your business data, not just the technology around it.
Engineering-led, not advisory-only
The team that advises you is the team that can build it. Recommendations are tested against real delivery effort, cost and risk before they reach a slide.
Accelerators that shorten the path
Pre-built accelerators for analytics, migration and AI, including GenAI-assisted migration tooling and Enterprise Analytics Accelerators across 11 source systems.
Flexible global delivery
Blended onshore, nearshore and offshore teams in the US, Mexico, India and the UK, all KPI employees, scaled as projects, pods, staff augmentation or managed services.
Also 5x Gartner recognized and Great Place to Work® certified six years running.
Questions we often hear
We already have AI pilots running. Where do you come in?
Often right there. We review the pilots against value and feasibility, put baselines and metrics in place, and help the strongest ones pass the gates into production while stopping the rest early.
Do we need to fix all of our data before starting with AI?
No. We fix what your priority use cases need, in the order they need it. Many first use cases can start on data that is already well governed while broader foundation work continues.
Which model or platform do you recommend?
The one that fits the use case. We partner across Microsoft, AWS, Google Cloud, Databricks, Snowflake and Anthropic, and choose per use case on quality, cost, latency, security and the skills you have.
Our AI spend is growing faster than we expected. Can you help?
Yes. We start by attributing spend to use cases, teams and agents so you can see what is driving it, then compare cost with the value each use case delivers. From there we put budgets, guardrails and optimizations such as model routing and caching in place, and set up FinOps routines so cost stays visible as you scale.
What does outcome-based mean commercially?
Investment is released gate by gate, and where it suits both sides, part of our fees can be linked to the measured outcomes agreed at Gate 0.
Schedule a consult on your AI plans
Tell us where you are with AI today. You will speak with a senior advisor who has taken AI from pilot to production, and leave with a clear next step.
- We reply within one business dayA senior advisor reviews your request and confirms a time.
- A 45-minute consultWe talk through your current state, goals and constraints. No sales deck.
- A written point of viewYou get our initial recommendations and suggested next step, whether or not you work with us.
We use your details only to respond to this request.