90-Day AI Execution Sprint
Redesign one high-value workflow. Deliver a measurable business outcome. Move from assessment to execution.
Not a proof of concept. Not a pilot for the sake of saying the company is using AI. A targeted engagement built around the way work actually happens — and the metric the business needs to move.
Built to Move From Assessment to Operating Result.
Many AI initiatives fail because they are scoped around technology instead of business execution. A team selects a tool, identifies a use case, launches a pilot, and then struggles to connect the result to margin, throughput, cycle time, cash flow, risk, or visibility.
Foundation AI Advisory takes the opposite approach. The sprint starts with a business workflow that matters. Then the data is curated. Then the workflow is redesigned. Then AI is applied where it can improve the operating result.
This is how AI becomes useful inside a mid-market company — not as a standalone capability, but as part of a governed operating system.
Improve One Workflow. Produce a Measurable Result.
The sprint is designed to answer one central question: where can the company redesign the way work gets done so the business becomes faster, cleaner, more visible, less risky, or more profitable?
The answer may involve AI. It may involve data cleanup. It may involve workflow redesign. It may involve system configuration, reporting, governance, decision rights, approvals, or exception handling. In most cases, it involves several of these elements working together.
The sprint is not built around broad transformation. It is built around targeted operational improvement.
Long Enough to Execute. Short Enough to Force Discipline.
Mid-market companies need execution momentum, but they also need discipline. Large enterprise transformation programs are often too slow, too expensive, and too abstract. Small tool pilots are often too narrow and disconnected from the way the business actually operates.
The 90-day sprint sits between those two extremes. Long enough to understand the workflow, clean up the data, redesign the process, implement practical changes, test the operating model, and measure the result. Short enough to force prioritization.
That pressure is useful. It prevents the company from trying to solve every problem at once. It requires leadership to choose one workflow where improvement matters. It keeps the team focused on execution instead of endless strategy discussion.
Strong sprint candidates:
Quote-to-cash. Order intake. Estimating. Project setup. Job costing. Invoice preparation. Purchasing approvals. Customer service triage. Document intake. Sales handoff. Financial close support. Inventory exception review. Reporting automation. Operational issue escalation.
Data First. Workflow Second. AI Third.
The sprint follows Foundation AI Advisory’s operating sequence.
Data Curation & Governance
The sprint begins by identifying the data required to run the selected workflow properly: source systems, record quality, ownership, definitions, required fields, reporting logic, access rights, document structures, and governance rules.
The goal is not to perfect every data source in the company. The goal is to curate the data required for the selected workflow to operate reliably.
Business impact: AI cannot reliably improve a workflow if the underlying data is unclear, incomplete, or unmanaged.
Workflow Optimization
Once the data requirements are clear, Foundation AI Advisory maps and redesigns the workflow: who does the work, where the work starts, what information is required, where decisions happen, where approvals occur, where delays appear, where exceptions are routed, and where the workflow ends.
We separate necessary work from avoidable friction. Some manual steps exist because the business needs control. Others exist because the current system is unclear. Some handoffs are valuable. Others are rework. The distinction matters.
Business impact: better workflow design alone can reduce cycle time, improve accountability, eliminate duplicate entry, and create better visibility — before AI enters the picture.
AI Design & Implementation
AI is applied only where it improves the redesigned workflow. That may mean using AI to classify inbound requests, extract information from documents, summarize records, identify exceptions, draft internal communications, support research, route work, compare records, detect inconsistencies, or assist human reviewers.
The key is that AI operates inside boundaries: clear inputs, clear outputs, clear controls, clear owners, and clear exception paths. Foundation AI Advisory is especially careful about human-in-the-loop design. AI can support work, accelerate review, and improve consistency, but business accountability remains with the organization.
Business impact: AI creates leverage without increasing unmanaged risk.
One Executive Owner. One Operating Owner.
The executive owner is typically the CEO, President, COO, CFO, Controller, CIO, or business unit leader. This person owns the business outcome and ensures the sprint stays tied to margin, throughput, cycle time, cash flow, risk, or visibility.
The operating owner is the person closest to the workflow — controller, operations manager, sales operations lead, project manager, IT leader, finance manager, customer service leader, or department head.
Foundation AI Advisory supports the sprint, structures the work, drives analysis, facilitates decisions, and helps design the implementation path. The business must own the operating model.
Practical, Not Performative.
- Workflow selection and success metric definition
- Data source review and data readiness analysis
- Workflow mapping and constraint identification
- Stakeholder interviews and operating review
- Data curation requirements
- Governance and ownership recommendations
- Future-state workflow design
- AI opportunity identification
- Control points, exception logic, and human-in-the-loop design
- Implementation plan and pilot or controlled rollout
- Measurement and executive review
90 Days, Five Stages.
The 90-day structure creates a clear operating cadence.
Define and Diagnose
Align on workflow, business outcome, scope, success measures, stakeholders, and data sources. Define the performance problem in operational terms.
Curate Data and Map Workflow
Review relevant data sources, record quality, definitions, reporting logic, and governance. Map the workflow in detail. Identify what must be fixed before AI can create value.
Redesign the Operating Model
Define the future-state workflow: redesigned steps, ownership, required data, governance rules, controls, exception paths, reporting, and AI support points.
Implement and Test
Implement the redesigned workflow in a controlled manner: system changes, reporting, AI-assisted steps, training, governance routines, human review checkpoints. Testing focuses on operational performance, not technical novelty.
Measure, Stabilize, Decide
Close with measurement, operating review, lessons learned, and a recommendation for next steps — refinement, broader rollout, a second sprint, or transition into Ongoing Execution & Expansion.
AI Tied to an Operating Outcome.
The expected impact depends on the workflow selected, but the operating dimensions are clear.
Improves when the company reduces leakage, improves costing, reduces rework, strengthens pricing discipline, or improves operational consistency.
Improves when work moves through the business with fewer delays, clearer ownership, and less manual friction.
Improves when handoffs, approvals, document review, reporting, and exception handling are redesigned.
Improves when billing, collections, project closeout, order processing, or financial workflows move faster and with better data.
Reduces when controls, ownership, auditability, and exception paths are designed into the workflow.
Improves when leadership can see status, constraints, performance, and exceptions without relying on informal updates or manual reporting.
90-Day AI Execution Sprint — Common Questions
- What is the 90-Day AI Execution Sprint?
- The 90-Day AI Execution Sprint is a focused engagement to redesign one high-value workflow, align the data behind it, and apply AI where it improves performance. The deliverable is a working AI capability tied to a measurable business outcome — not a pilot, not a slide deck, and not a tool installation. The Sprint typically follows the Business Systems Assessment.
- How is the Sprint different from an AI pilot?
- An AI pilot usually starts with a tool and searches for a use case. The Sprint starts with a business workflow that matters, then curates the supporting data, then redesigns the workflow, then applies AI only where it improves the operating result. The output is an operating capability, not a demonstration.
- Why 90 days?
- Ninety days is long enough to understand the workflow, clean up the data, redesign the process, implement practical changes, test the operating model, and measure the result. It is short enough to force prioritization and prevent the team from trying to solve every problem at once.
- What kinds of workflows are good Sprint candidates?
- Quote-to-cash, order intake, estimating, project setup, job costing, invoice preparation, purchasing approvals, customer service triage, document intake, sales handoff, financial close support, inventory exception review, reporting automation, and operational issue escalation are all strong candidates.
- Who owns the Sprint inside the company?
- Each Sprint has an executive owner — typically the CEO, President, COO, CFO, Controller, CIO, or business unit leader — and an operating owner closest to the workflow. Foundation AI Advisory supports the work and structures the engagement, but the business owns the operating model.
- Does the Sprint require a Business Systems Assessment first?
- Not always. The Sprint typically follows the Business Systems Assessment because the Assessment identifies the highest-value workflow to address. Companies with clear operating priorities and a defined target workflow can sometimes move directly into a Sprint.
Not Tool-First.
A tool-first approach starts with software and then searches for a use case. It often produces activity without adoption, dashboards without trust, automation without control, and pilots without measurable business value.
Foundation AI Advisory starts with the business problem. Then we define the workflow. Then we curate the data. Then we design the AI-supported operating model. Then we select or configure tools based on the work they need to support.
This sequence reduces waste. It gives leadership a stronger basis for investment decisions. It helps IT avoid being blamed for business process problems. It helps finance see where value will be measured. It helps operations understand what will actually change.
Select the Right Workflow.
A strong sprint candidate should meet five criteria.
- It matters to business performance.
- It has visible friction today.
- It depends on data and workflow quality.
- It can be improved in a focused timeframe.
- It can be measured.
The goal is not to prove that AI is interesting. The goal is to prove that the business can use AI, data, and workflow redesign to improve execution.
Move From Idea to Operating Capability.
Pick the workflow that matters. Define the metric. Run a sprint that ends with a measurable result, not a slide deck.