Agentic workflows at work: turning AI into measurable productivity
Executives in the UK and EU are under pressure to raise productivity even as official statistics show only modest gains. Output per hour in the UK was 2.1% above its 2019 level in Q1 2025 and fell 0.2% year‑on‑year—evidence of the persistent productivity puzzle. Agentic AI offers a practical path forward—if leaders focus on workflows, not pilots.
What do we mean by “agentic workflows”?
Agentic workflows stitch together AI “agents” that can plan, call tools and APIs, read and write to business systems, and cooperate to complete goals. They combine retrieval‑augmented generation, function calling, memory and guardrails, moving beyond a single chat into orchestrated, event‑driven work. At the plumbing layer, emerging standards such as Model Context Protocol (MCP) and the Linux Foundation’s Agent‑to‑Agent (A2A) protocol aim to make agents interoperable and auditable across enterprise systems.
The business case: where productivity lifts appear today
Evidence is strongest where tasks are repetitive, knowledge‑heavy and time‑boxed. A large field study found that a generative‑AI assistant raised customer‑support productivity by ~14–15% (issues resolved per hour), with the biggest gains for less‑experienced agents, while controlled experiments with consultants reported 12–25% faster completion on complex knowledge tasks. These are measured outcomes from live work, not hypothetical uplifts.
Adoption is racing ahead—but returns are uneven
By early 2024, 65% of organisations reported regular use of generative AI, nearly double the prior year. Macro‑modelling suggests automation could add 0.5–3.4 percentage points to annual productivity growth through 2040, with generative AI contributing 0.1–0.6 points—if time savings are reinvested.
UK lens: leaders want outcomes, not experiments
UK business leaders rank productivity as a top 2025 priority and increasingly see AI as an augmentation tool rather than a blunt instrument for headcount. That mirrors workplace evidence that AI often narrows performance gaps by lifting newer staff. For a weak trend‑productivity environment, agentic automation can release trapped capacity in customer operations, finance back offices and field maintenance.
Why many AI pilots fail—and how agentic workflows fix it
Too many pilots sit outside core processes, so nothing moves on cost‑to‑serve, cycle time or right‑first‑time. Leaders who succeed treat AI as an operational capability and embed it in a few workflows where value is measured weekly. In 2024, Boston Consulting Group found 74% of companies struggled to achieve and scale value, while “winners” focused on outcomes and built responsible‑AI controls in parallel; Harvard Business Review analysis cautions against scattered experiments.
How agentic workflows create ROI: a practical pattern
Start with a target metric. For example, “reduce average handling time by 20%” or “cut month‑end close from 10 to 6 days.”
Map the work as a graph. Identify intents, steps, systems and approvals.
Orchestrate around systems of record. Use a planner agent, tool‑use and a rules engine; keep a human‑in‑the‑loop for exceptions.
Ground every action in enterprise data. Use RAG and structured connectors, cache decisions with auditable memory.
Instrument evaluation. Track speed, quality, safety and drift.
Close the loop. Reinvest saved time into higher‑value work, not meetings.
State of the art: interoperability and guardrails
Two shifts matter. First, standardised connectors let agents act within business apps securely; Windows is adding MCP support and Azure exposes MCP‑enabled models, while A2A seeks cross‑vendor agent messaging.
Second, governance is operationalising: ISO/IEC 42001 introduces a certifiable AI management system, and NIST’s AI Risk Management Framework (RMF) plus its 2024 GenAI profile provide concrete controls for risk, testing and monitoring.
Workplace risks—and how to mitigate them
Privacy and monitoring. The UK ICO’s worker‑monitoring guidance requires transparency, necessity and proportionality, with DPIAs for intrusive approaches; enforcement includes a 2024 order halting the use of facial recognition for attendance. Build privacy by design; avoid hidden surveillance
Model error and hallucination. Ground generations in corporate sources; require dual‑agent verification for critical actions; maintain human approval thresholds. Track task‑level accuracy and quality.
Bias and fairness in people‑decisions. In the EU, AI used in recruitment and worker‑management is classed as “high‑risk” and subject to risk management, data governance and human oversight. Keep agentic automation out of high‑risk HR decisioning unless you can evidence fairness and oversight.
Security and data leakage. Enforce least‑privilege tool access; never expose raw secrets to prompts; log every tool call; isolate agent runtimes; scan for prompt‑injection.
Five high‑value workplace use cases to prioritise
Customer operations. Triage tickets, draft replies, propose resolutions and trigger fulfilment via APIs—measured on first‑contact resolution and CSAT.
Finance. Autonomous close tasks—reconciliations, variance analysis, journal suggestions—evaluated against close cycle time and error rates.
Procurement. Policy‑compliant buying with agentic approvals and supplier summarisation—optimising price‑variance and on‑time delivery.
Field maintenance. Generate work‑orders from telemetry, plan routes and update CMMS—cutting downtime and truck‑rolls.
Knowledge management. Multi‑agent RAG over policies, contracts and SOPs—speeding onboarding and reducing escalations.
Metrics that matter
Start with a three‑month baseline, then track:
Hours returned per FTE and redeployment mix.
Cycle‑time deltas by step; right‑first‑time rate.
Error‑budget burn for safety and privacy controls.
Cost‑to‑serve and working‑capital impact.
Independent benchmarks help set expectations: AI‑exposed sectors saw faster productivity growth, with revenue per employee rising roughly three times faster than less‑exposed sectors in 2024, while workers with AI skills attracted a 56% wage premium.
Implementation roadmap for UK and EU employers
Form a joint squad: operations lead + product owner + data engineer + security + change partner.
Choose two workflows with clear KPIs and low regulatory risk.
Build on enterprise data: connect ERP/CRM/ticketing via secure connectors; cache decisions with audit trails.
Put controls first: DPIA, role‑based access, model evaluation, red‑teaming.
Prove value in 8–12 weeks, then scale through a central platform team and playbooks. Align to ISO/IEC 42001 and NIST AI RMF.
Regulation in brief
EU AI Act. Most provisions apply from 2026; HR and worker‑management uses are high‑risk with obligations on risk management, human oversight and documentation.
UK. No single AI Act: regulators provide domain guidance. For worker monitoring, the ICO expects necessity tests, transparency and alternatives for intrusive tools.
Industry examples to emulate
Retail and e‑commerce. Inventory agents predict demand and automate purchasing to cut stock‑outs and markdown risk.
Manufacturing. Agents schedule maintenance and adjust parameters in near‑real time to reduce unplanned downtime.
Professional services. Multi‑agent research and drafting accelerates client deliverables while improving consistency. See manufacturing and industrial automation examples and agentic playbooks.
Chart: measured productivity uplifts from live experiments
We visualised two well‑cited experiments to help set realistic targets: MIT National Bureau of Economic Research (NBER) 2023 and Boston Consulting Group 2024.
Measured productivity uplift with AI assistance (2023-2024)
How Data Nucleus can help
Strategy and solution design. End‑to‑end discovery and KPI definition; operating‑model design for agentic workflows—Cognitive Intelligence Solutions.
Governance‑by‑design. Implementation aligned to ISO/IEC 42001 and NIST AI RMF, plus privacy‑first patterns for worker tools—Corporate Governance & Compliance.
Operations automation. Agentic orchestration for maintenance, supply chain and asset performance—Manufacturing & Industrial Automation and Energy & Asset Management.
Enterprise delivery. Secure deployment, integration and runbooks across on‑prem and cloud—Solutions Deployment.
Conclusion
Agentic workflows shift the conversation from “what model?” to “what outcome?”. The winners are building secure, interoperable systems around a few core workflows and proving impact on cost, cycle time and quality. With measured designs, guardrails and credible KPIs, UK and EU employers can turn AI from pilots into productivity.
