Put agents in the workflows that move money.
Run agents in the workflows that carry revenue and cash: order-to-cash, procure-to-pay, S&OP, contracts and quote-to-cash, sales enablement, and agentic service resolution, connected to a governed backbone of ERP, PLM, MES, and CRM. Agents that act on revenue and cash processes, record their actions, and hand control to a named person at the moments that matter. 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
- ReceiveThe task and authorized context.
- ExecuteAgents with defined permissions.
- EscalateExceptions and human decisions.
- RecordReviewable actions and outcomes.
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
Isolated systems. Agents with nothing to act on.
You have ERP, PLM, MES, and CRM. Each operates with its own truth. Order data lives in one system, inventory in another, contracts in a third, and nothing is governed for safe machine consumption. So the AI you bought sits beside the workflow instead of inside it: a chatbot on top of a form, an RPA macro with a generative AI label, a copilot that drafts but never executes.
The answer is not another point-to-point integration or another assistant. It is a governed, AI-ready data backbone, followed by agents that execute the identified workflows moving money through the business. The agent owns the volume. A senior operator owns the judgment at the moments that matter. Each cycle makes it possible to measure the value generated in the workflow against the agreed baseline.
How we do it
Twelve weeks. Agents in production. Verified value.
We define the workflows and agents included in the twelve-week cycle and measure the value generated against the baseline agreed with Finance. Subsequent cycles can incorporate new workflows based on priorities and evidence.
Weeks 0 to 2
Map the workflow, establish the baseline
We define the team and its responsibilities according to scope, map the workflow across ERP, PLM, MES, and CRM, and establish an Approved Value Baseline with Finance. The target and measurement method are agreed in writing.
Weeks 2 to 9
Establish the backbone, deploy the agents
Senior operators build the governed data backbone and design the agent’s accuracy budget, audit trail, and escalation rules. Agents act in the production workflow while named operators take responsibility for judgment at handoff points.
Weeks 10 to 12
Verify value, then build on it
Your stakeholders approve the Value Created against the week 2 baseline. 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
Disparate systems unified in one operational layer.
How the process connects
- ReceiveThe task and authorized context.
- ExecuteAgents with defined permissions.
- EscalateExceptions and human decisions.
- RecordReviewable actions and outcomes.
Where we intervene
Five engagement types within this domain.
The scope can combine integration and one or more agentic workflows. We size each twelve-week cycle according to the value baseline and project dependencies, not the number of engagement types.
Order-to-cash agents
Agents that execute order capture, credit checks, invoicing, and collections end to end. They process clean orders at scale and route credit holds, disputes, and pricing exceptions to a named operator with the full audit trail attached.
Procure-to-pay automation
Agents that match purchase orders against receipts and invoices, flag exceptions, and schedule payment according to terms. Three-way matching at machine speed; a senior operator owns judgment calls on tolerance breaches and supplier disputes.
Decision support in S&OP
Decision-support agents that merge demand signals, supply constraints, and inventory positions for the S&OP cycle. Agents present options, evidence, and trade-offs; planners and the operator decide. Includes an accuracy budget and escalation rules.
Contract and quote-to-cash intelligence
Agents that read, compare, and draft across your contract corpus, then drive the quote-to-cash process from configuration to signature. Risky clauses and nonstandard terms are escalated to a senior operator before anything is finalized.
ERP, PLM, MES, and CRM integration backbone
The substrate beneath all of the above: a governed, AI-ready data backbone connecting your systems of record. It is designed with open standards; controls, permissions, and usage and transfer terms are agreed in each project.
Business example
From an incomplete request to a quote ready for approval.
Client identity protected
Industry: industrial distribution. The commercial need is to respond quickly without losing control over specifications, availability, price, and sales terms.
The business problem
A request arrives by email with partial descriptions, attachments, and references that do not always match the catalog. The sales team checks inventory, looks for equivalents, requests clarification, and reconstructs the proposal by hand. Every handoff between departments introduces delays and opportunities for error. Automating drafting alone leaves the bottleneck intact: gathering and validating the data needed to quote responsibly.
STRTGY’s intervention
At STRTGY, we connect incoming requests to authorized catalogs, price lists, and availability records. Agents extract line items, detect ambiguities, consult sources, and prepare a traceable draft. If a specification is missing, the task goes to the relevant owner; if a pricing or terms exception arises, approval is requested. The accountable person retains the commercial decision and authorizes sending. The workflow records sources, versions, approvals, and status so sales and operations work from the same request.
The value and how it is measured
The value sought is reducing preparation work and maintaining process continuity without sacrificing accuracy or margin. We track the time from receipt to approved proposal, corrections, requests stalled by missing information, and human intervention time. With Finance, we agree on which costs and activities form the baseline. The evaluation separates workflow efficiency from the customer’s purchase decision: preparing a quote faster does not, by itself, equal an additional sale.
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
Agreed scope and changes
Scope, amount, and change handling are agreed in the project contract.
03
Transparent resource plan
Senior experts with responsibilities defined according to scope. Team composition and continuity and replacement terms are agreed in the project contract.
04
Vendor-independent architecture
Open standards, multicloud, model-agnostic runtime. The integration backbone and agents 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 Agentic Workflow Execution mean?
It means putting AI agents into the specific workflows that move revenue and cash through your business: order-to-cash, procure-to-pay, S&OP, contracts and quote-to-cash, sales enablement, and agentic service resolution. Agents act on revenue and cash processes, log their actions, and hand control to a named person at the moments that matter. They are not chatbots layered over forms or RPA with a coat of generative AI paint. Operationalization is how you run AI as an operating model. This is what the agents do.
Why do we need to integrate ERP, PLM, MES, and CRM first?
Agents can only act on data they can access and trust. Most businesses have the systems but lack the connective tissue: order data lives in one place, inventory in another, contracts in a third, and none of it is governed for machine consumption. We unify ERP, PLM, MES, and CRM into a governed, AI-ready data backbone, then run agents on top of it. Integration is the foundation, not the deliverable.
How is this different from your AI Operationalization domain?
Operationalization (at /en-ai-operationalization) is how you run AI as an operating model: center of excellence design, governance, observability, and how the entire organization adopts AI. This domain is what the agents actually do: the defined workflows they execute. Operationalization is how you run AI. This is what the agents do. The two can be combined, but they answer different questions.
How do you keep agents safe in workflows that handle real money?
Every agent operates with an explicit accuracy budget, an audit trail, and escalation rules built into the workflow. The agent handles volume; a named senior operator is accountable for judgment at the moments that matter (credit holds, exceptions, pricing approvals, and contract terms). Human handoffs are built into the design, not added later. The same observability available to Finance and security teams covers cost, latency, accuracy, and drift for each workflow.
How do you demonstrate ROI from agentic workflow automation that our Finance team will accept?
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.
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