WTA SPEED Framework: From Workflow Audit to Enterprise AI

Useful AI starts with understanding the work. Our five-stage delivery approach connects business priorities, Microsoft technology, and clear ownership. We assess the current workflow, test a focused pilot, and expand only when the evidence supports it. Scope and delivery dates are agreed for each engagement.

Five stages. Clear decisions at every step.

Strategy: understand the workflow and its cost

We work with the people who perform and own the process. Together, we map tasks, handoffs, exceptions, data sources, and approvals. We record current time, volumes, error rates, and operating costs before considering automation.

The output is a prioritized opportunity list and a baseline for comparison. Our AI Strategy & Governance service helps define the first pilot, accountable owners, and measurable acceptance criteria.

Planning: design the right experience and controls

We decide where an agent, a conventional workflow, or a human decision best serves the task. The plan covers access permissions, approved data, integration boundaries, and recovery paths. Chat is introduced where it improves the journey, while forms and dashboards remain where they help users compare or approve information.

Our experience design and platform modernization teams connect these decisions to your existing Microsoft environment.

Engineering: build and connect a focused pilot

We build a contained workflow using suitable Microsoft Azure services and agent tools. Engineers connect approved systems, implement permissions, and make important actions visible to users. The team reviews generated code and tests integrations throughout delivery.

Our product engineering and software delivery services turn the plan into a working pilot with documented dependencies and clear release responsibilities.

Evaluation: prove quality, safety, and business value

We test representative tasks, unusual requests, failures, and attempts to exceed permissions. Business owners review the results against the agreed baseline. Cost comparisons include model usage, infrastructure, human review, support, and rework.

A pilot moves forward when its measured quality, reliability, and operating cost justify the next step. We document remaining limitations and keep consequential actions subject to agreed approval rules.

Deployment: release carefully and monitor daily use

We release to a defined user group, train the support team, and keep recovery procedures ready. Monitoring covers successful tasks, failures, response time, access events, and cost. Findings guide subsequent improvements.

Our AI Delivery Pods & Agentic Platforms service defines the ongoing operating model. The agreed Agent Swarm control-plane view brings relevant signals together, with capabilities and integrations confirmed for your deployment.

Frequently asked questions

Do you guarantee a fixed delivery timeline?

No single timeline fits every enterprise workflow. We agree milestones after reviewing scope, data access, integrations, risk, and team availability. A focused pilot provides evidence for a realistic rollout plan.

How do you compare current costs with agent costs?

We establish the cost of the existing process, including labor, delays, errors, and support. The proposed workflow adds model usage, hosting, integration maintenance, human review, and recovery costs. We compare cost per successfully completed task at realistic volumes.

Can we improve existing systems without replacing everything?

Yes. We assess where targeted integration, better data access, or an agent-assisted step can improve the current process. Existing systems can remain in place where they provide reliable business value.

Who remains responsible after deployment?

Named business and technical owners retain responsibility. The operating plan defines approvals, monitoring, incident response, support, and when to stop automation or use a manual fallback. Ownership is agreed before release.