What is the Work AI Institute?

The Work AI Institute is a research centre focused on how AI delivers measurable outcomes in everyday work. It convenes academics and operators to study real implementations and publish practical guidance leaders can use immediately. Its early work highlights patterns that separate hype from durable results and documents how organisations design processes around AI—not just bolt AI onto old ways of working.

Why it matters now

• From pilots to production. Many firms are stuck in pilot purgatory. The Institute’s research codifies the practices used by organisations that have escaped it.

• Context beats raw capability. AI becomes useful when connected to an enterprise’s people, processes, systems and knowledge. That demands a deliberate operating model for AI.

• Agents are here. Autonomous and semi‑autonomous agents can take on multi‑step work with human controls. Scaling them safely requires new governance.

The AI Operating Model (practical blueprint)

Use this model to guide transformation. Each element includes actions and deliverables you can implement in weeks, not months.

1) Strategy & Value

• Define three business outcomes for the next two quarters (e.g., cycle‑time reduction, cost‑to‑serve, win‑rate).

• Prioritise 5–10 AI “jobs to be done” that underpin those outcomes.

• Produce a one‑page AI Value Hypothesis for each job (baseline, target, constraints, owner).

2) Process & Workflow Design

• Map current vs. target workflows; highlight where assistants (human‑in‑the‑loop) or agents (autonomous with guardrails) fit.

• Design control points: approval thresholds, exception queues, audit logs.

• Standardise prompts and playbooks in a shared library; version them as you would code.

3) Data & Context Readiness

• Inventory systems that supply context (knowledge bases, tickets, CRM, docs, code).

• Define golden sources and access scopes; remove stale or duplicate content.

• Establish RAG/graph patterns to ground models in trusted knowledge.

4) Governance & Risk

• Draft an AI Use Charter: permitted uses, sensitive data rules, escalation paths.

• Create an AI Review Board (Legal, Security, Data, Domain leads) with fortnightly reviews.

• Implement evaluation harnesses: quality, bias, safety, and drift checks.

5) Organisation & Skills

• Appoint domain product owners for each AI job‑to‑be‑done.

• Upskill with role‑specific clinics (analyst, support, sales, developer) and a Prompt Patterns catalogue.

• Incentivise adoption on outcomes, not usage.

6) Measurement & Value Realisation

• Track Time Saved, Quality Uplift, Risk Reduction, and Revenue Impact.

• Instrument processes end‑to‑end; compare AI vs. control cohorts.

• Publish a monthly AI Value Dashboard; stop or scale based on the data.

Practical examples (cross‑industry)

• Customer Support: An agent triages tickets, drafts responses, and resolves known issues within policy; humans handle exceptions. Metrics: first‑response time, CSAT, re‑open rate.

• Sales: An assistant composes account summaries from CRM + emails, suggests next actions, and updates opportunities. Metrics: meeting prep time, pipeline hygiene, win‑rate.

• Finance & Ops: An agent reconciles invoices and flags anomalies with links to source docs. Metrics: cycle time, error rate, write‑offs prevented.

• HR & Talent: An assistant drafts job descriptions aligned to competency frameworks and screens for minimum criteria with bias checks. Metrics: time‑to‑post, time‑to‑shortlist, diversity signals.

• Engineering: An assistant proposes PR summaries and test plans grounded in repository context. Metrics: lead time, escaped defects, DevEx surveys.

A 6‑Week Pilot‑to‑Scale Plan

Week 1 – Portfolio & guardrails. Identify 5–10 candidate use cases; score by value/feasibility/risk. Publish the AI Use Charter and access controls.

Week 2 – Design the work. Map target workflows and control points; define what the assistant/agent does vs. the human.

Week 3 – Data and connectors. Connect knowledge sources; define retrieval strategies and test sample prompts with real data.

Week 4 – Build & evaluate. Stand up the first assistant/agent. Create evaluation harnesses and success metrics.

Week 5 – Shadow production. Run with real work under supervision. Capture exceptions and improve prompts/policies.

Week 6 – Go‑live & learn. Launch to the first team. Publish the value dashboard, a runbook, and a change‑management plan. Decide to scale, iterate, or stop.

Tips from high‑performing organisations

• Ground in context. Connect AI to the systems where work lives; avoid “chat dead‑ends.”

• Design for exceptions. Most value hides in the last 10% of cases; route them well.

• Make quality visible. Define accept/reject criteria and sample regularly.

• Reward outcomes. Measure what matters (quality, risk, customer value), not tool usage.

• Keep humans in control. Start with assistive patterns; automate only where confidence and controls are strong.

How Generation Digital can help

• AI Operating Model workshop. Define outcomes, portfolio, guardrails, and metrics in a single day.

• Use‑case design sprints. Map workflows and control points; prototype assistants/agents.

• Data & context integration. Connect knowledge sources and establish retrieval/graph patterns.

• Governance toolkit. Templates for AI Charters, evaluation harnesses, and value dashboards.

• Adoption enablement. Role‑based training and prompt libraries; change‑management playbooks.

Get in touch to book an AI Transformation workshop with Generation Digital.

FAQ

What is the Work AI Institute?
A research centre focused on making AI work in real organisations. It studies live implementations and publishes guidance leaders can put into practice.

How does it help with transformation?
It distils evidence from companies already scaling AI and turns it into frameworks, checklists, and plays you can adopt quickly.

What’s the “AI Transformation 100”?
A flagship publication that captures 100 practical strategies drawn from interviews and studies of leaders who have delivered AI impact.

Where should we start?
Pick a narrow set of high‑value jobs‑to‑be‑done, design human‑in‑the‑loop workflows, establish guardrails, and measure value from day one.

How is this different from generic AI training?
The focus is on operating‑model change, not one‑off demos—connecting AI to your data, workflows, controls, and metrics.