Take AI out of pilots and into the parts of the business that change the P&L.
Pilots succeed easily. Production deployments stall. We improve the operating model around named senior experts who orchestrate AI agents on execution environments defined for each project, and turn AI strategy into a portfolio with accounted-for value: outcomes and revenue tracked by use case, visible to your CFO. From scattered experiments to operational capability, in twelve-week cycles with fees at stake based on outcomes.
DIRECT LINE
Put a number on the line this quarter.
Twenty-five minute call. A Cycle 1 sketch tied to a real P&L number in your operation, the baseline mechanics, the named operators, and how outcome-staking works. No slides, no obligation.
See the four problem domains →
No obligation. We respond.
From objective to impact
- PrioritizeUse cases and business objective.
- PrepareSources, owners, and controls.
- ImplementIntegration into the agreed workflow.
- ValidateMeasurement against the baseline.
STRTGY · Value-linked fees
We validate value. Then we bill.
We agree on a value target, a baseline, and a measurement method with your Finance team. We execute a twelve-week cycle, validate the value generated at the end, and bill after that validation. Each project contract defines the scope, amount, payment terms, and what happens if the target is not met.
The twelve-week cycle is our delivery model, not an unconditional ROI guarantee. Targets, measurement, and acceptance are agreed for your operation in each proposal.
When is payment due?
We bill after validating the value generated at the end of the cycle. The project contract specifies the amount, payment terms, and how any deviation from the agreed target is handled.
The pattern
The pilot succeeds. Production never comes.
You have models that work. A vendor demo went well. A pilot was delivered. Then the deployment schedule starts slipping. The team that built the pilot was reassigned. The integrators want a new discovery phase. Model accuracy stops mattering because no workflow consumes its output. The operating model around AI was never built to scale.
Operationalization is the missing piece. It is not about more pilots or a bigger contract with a systems integrator. It is about improving the small number of workflows where AI directly affects the P&L, with senior operators handling judgment and AI agents handling volume. Each cycle makes it possible to measure the value generated against the agreed baseline.
How we do it
Twelve weeks. Production-ready. Verified value.
We define a production scope for the twelve-week cycle and measure the value generated against the baseline agreed with Finance. Subsequent cycles can expand the scope based on priorities and evidence.
Weeks 0 to 2
Mobilize around your P&L
We define the team and its responsibilities according to scope, bring authorized context into the project environment, and establish an Approved Value Baseline with Finance. The target and measurement method are agreed in writing before execution.
Weeks 2 to 9
Build and deploy to production
Senior operators design and validate; AI agents handle the volume work (data, mapping, drafting, instrumentation, quality assurance). Everything is delivered against the week 2 baseline, in production workflows, not in a staging fiction.
Weeks 10 to 12
Verify value, then build on it
Your stakeholders approve the Value Created. Transfer and usage terms for the deliverables are defined in the contract. Any subsequent cycle and its scope are agreed in the project contract.
In operation
From scattered pilots to a single operational fabric.
How the process connects
- PrioritizeUse cases and business objective.
- PrepareSources, owners, and controls.
- ImplementIntegration into the agreed workflow.
- ValidateMeasurement against the baseline.
Where we intervene
Five engagement types within this domain.
The scope can combine the following engagement types in a twelve-week cycle. We size it according to the value baseline and project dependencies, not the number of engagement types.
AI center of excellence redesign
Build or reconfigure the internal center of excellence to deliver agent-based capability to the business instead of running a pilot factory. Governance, model catalog, evaluation pipelines, agent execution environment.
Agentic execution in existing workflows
Deploy AI agents in specific workflows (order-to-cash, demand sensing, exception management, contract intelligence) that close the loop on outcomes the operator already owns.
Decision-support deployments
Production-ready decision-support agents alongside named operators. Agents present options, evidence, and trade-offs; the operator decides. Audit trail, accuracy budget, escalation rules.
Model governance and observability
Production observability for foundation-model traffic, with cost, latency, accuracy, and drift instrumented by workflow. The CFO and security teams see the same dashboard.
Pilot-to-production rescue
Take over a stalled pilot, assess its dependencies, and define the scope needed to bring it into production. Planning is organized in twelve-week cycles with agreed acceptance criteria.
Business example
From an after-sales pilot to an operational capability.
Client identity protected
Industry: manufacturing and after-sales service. The starting point is a specific decision: how to handle a request with current information, an accountable owner, and verifiable quality criteria.
The business problem
The information needed to respond is scattered across manuals, warranty terms, service history, and commercial records. A pilot can draft a convincing response without demonstrating that it uses the correct version, recognizes what is missing, or respects authorization limits. The business problem is not generating more responses: it is turning AI into a capability the team can use and oversee in its daily work.
STRTGY’s intervention
At STRTGY, we define the types of requests, authorized sources, and decisions that remain with the after-sales owner. We connect the necessary context and configure an agent that gathers background, identifies missing information, and prepares a response with verifiable references. We define quality tests, escalation criteria, and a record of every intervention. The team reviews exceptions and approves actions that commit to a warranty, incur a cost, or affect a commercial relationship. Going live includes tracking usage, quality, and cost per request.
The value and how it is measured
The value sought is more consistent service and less work reconstructing background information. We measure preparation time, rework, accepted responses, escalation, and service cost, without confusing usage volume with economic impact. Before execution, we agree with Finance on the baseline and how to translate process changes into verifiable value. At the end of the cycle, we compare the same request categories and distinguish freed-up capacity from actual savings.
Commitments
Delivery principles Defined in each proposal.
These principles guide delivery. We agree on the target and its measurement with Finance, validate value at the end of the cycle, and bill afterward. Each contract defines the scope, amount, payment terms, and how unmet targets are handled.
01
Value-based fees
Fees tied to the value we deliver, measured against a baseline approved by your Finance organization. Not to hours spent.
02
Transparent resource plan
Senior experts with responsibilities defined according to scope. Team composition and continuity and replacement terms are agreed in the project contract.
03
Auditable by design
Every request, decision, and outcome in the record. Governed, observable execution you can hand to audit.
04
Vendor-independent architecture
Open standards, multicloud, model-independent execution environment. Tools follow the design, not a sales quota.
DIRECT LINE
Put a number on the line this quarter.
Twenty-five minute call. A Cycle 1 sketch tied to a real P&L number in your operation, the baseline mechanics, the named operators, and how outcome-staking works. No slides, no obligation.
Frequently asked questions
Frequently asked questions
Questions a buyer actually asks.
What does AI operationalization actually look like?
Operationalization is the work of taking AI from successful pilots into the parts of the business that change the P&L. We improve the operating model around named senior experts who orchestrate AI agents on execution environments defined for each project, and account for value as outcomes and revenue by use case that your CFO can see. From scattered experiments to operational capability, in twelve-week cycles with fees at stake based on outcomes.
Why do most enterprise AI pilots stall before reaching production?
A pilot can stall if the operating model, governance, or integration is not production-ready. STRTGY addresses those dependencies with senior judgment, defined responsibilities, and AI agents configured according to scope. Requirements and acceptance criteria are agreed for each project.
How do you measure ‘AI ROI’ in a way our Finance team will approve?
Before execution, we agree on a value target, a baseline, and a measurement method with your Finance team. Finance validates the value generated at the end of the twelve-week cycle. We bill after validation; the contract defines the amount, payment terms, and how unmet targets are handled.
Do you build with foundation models or with our own model stack?
We work with foundation models and existing investments according to the task’s needs. We define an architecture that supports interoperability and avoids unnecessary dependencies. Models, licenses, and code usage and transfer terms are agreed in each project.
How does this differ from hiring an AI consultancy?
We bring the work into the operational workflow: design, integration, deployment, and measurement against an Approved Value Baseline. Senior experts provide judgment and validate execution with AI agents. The team, deliverables, and continuity are defined in the project contract.
DIRECT LINE
Put a number on the line this quarter.
Twenty-five minute call. A Cycle 1 sketch tied to a real P&L number in your operation, the baseline mechanics, the named operators, and how outcome-staking works. No slides, no obligation.
Let’s talk about your operation