The emergence of agentic AI looks set to elevate the technology from just an efficiency driver to a concrete, scalable lever of competitiveness for companies. This shift will allow businesses to move beyond focusing their AI efforts on identifying narrow sets of dedicated use cases to systems capable of working end-to-end on certain workflows. While earlier versions of AI have helped people solve specific issues like improving sales forecasts, agentic AI promises to transform workflows, redesign roles and unlock performance gains at scale.
The question for leaders is no longer whether AI can create value, but how to deploy it in ways that reliably lift operational performance at scale.
How did we get here?
AI is impacting nearly every part of the global economy, giving every team the chance to innovate and redesign processes. Over the past decade, organisations have explored different ways to harness AI. Today, three main approaches coexist. Each has their own distinct benefits and limits.
1) Narrow AI: specialised models for specific problems
This path involves training a model to solve a clearly defined problem better than conventional methods. Typical examples include demand forecasting models that outperform spreadsheet-based planning, or machine-learning models that optimise parameters on industrial equipment.
Narrow AI works best where the problem is stable, the data is structured, and the success metric is clear. Its limitation, though, is that it is only effective in a relatively small number of use cases.
2) Personal AI: generalist copilots for everyday tasks
The rise of generative AI made generalist assistants, internal chatbots or tools like Microsoft Copilot available to knowledge workers. They work well for cross-cutting tasks such as drafting meeting notes, summarising emails or performing generic research. But they do have two major limitations. Firstly, they lack the deep business context required for many decisions and secondly, they are usually not connected to enterprise systems. This can make them slow or impractical for operational workflows.
3) Agentic AI: job-focused agents that compound value across dozens of tasks
Agentic AI shifts the unit of design from single use cases to jobs. An agent is built around a specific function such as procurement, customer service or transport planning. It is then connected to both structured enterprise data (e.g. ERPs, WMS, TMS, CRMs and KPIs) and unstructured sources (emails, chats and meeting notes).
Most importantly, the agent is able to act within systems and communication channels. Rather than solving one big problem, it assembles 30–60 micro use cases per job. While each may seem minor on their own, they produce a step-change impact when combined.
Why agentic AI can sharpen your competitive edge
Nearly every job involves repeatable and relatively simple tasks that produce a predictable output. For example, people in support jobs (like HR or administration) spend about one-third of their working time on focused, routine tasks like data entry, scheduling or answering employee questions about policies. Agentic AI is now capable of performing these kinds of tasks quickly, accurately and at scale. For those working in functions like customer service, procurement and transport, the share of tasks that AI could perform could rise to 40% or more. Even in roles with higher variety and complexity, such as frontline management or project management, the share is still typically 20–25%. Automating these tasks doesn’t just represent a marginal potential gain – it is a systemic shift in how work gets done.
This increased efficiency is a powerful source of value that can be monetised in several ways depending on your business context. The time saved can be reinvested into higher-value activities. It can also be used to accelerate continuous improvement and transformation as well as resizing support functions to sharpen cost competitiveness without impacting service quality. When one-third of the workload of indirect operational teams can be reliably automated or augmented, the resulting capacity and performance lift becomes a durable competitive advantage rather than a one-off saving.
Agentic AI offers a strategic dual advantage for businesses. It can significantly reduce human workload, freeing up time to be reinvested in cost competitiveness or performance. By autonomously running end-to-end workflows, monitoring issues in real time and applying best practices consistently, agentic AI can potentially enhance decision quality and reduce errors. This strengthens core performance metrics such as service levels, inventory availability or customer response times. At the same time, AI extends the capabilities of teams beyond what was previously achievable, allowing organisations to improve process effectiveness and realise new levels of performance gains. This could be a powerful engine for new forms of organisational and competitive advantage.
It’s time to move beyond use cases to job redesign
Despite the obvious potential of AI, many businesses are struggling to get out of the sandbox and into deployment at scale. Many decision-makers are still thinking about AI in terms of standalone ‘AI use cases’, rather than trying to identify workflows or jobs that can be redesigned. This tends to surface a handful of obvious, large items and leaves most of the potential value untouched.
The real potential of agentic AI comes from automating the huge array of small, repetitive tasks people perform regularly as part of their jobs. As well as saving people time, this creates the potential to redesign end-to-end processes, uncover new performance opportunities and refocus people’s daily time around value-producing tasks.
Taking a job-first approach to agentic AI is a useful way of maintaining a strategic approach to agent deployment. The process involves:
- Mapping the job end-to-end, including decision rules and exception paths.
- Decomposing activities into tasks and sub-tasks
- Identifying where AI can perceive, decide and act reliably.
- Redesigning processes to be more efficient and higher performing (better service levels, fewer errors, faster cycle times).
- Quantifying impact upfront (time, quality, service, working capital), prioritise by value and complexity, and set clear implementation targets.
If we use procurement as an example, we can see how the agentic opportunity spans entire workflows:
- Contracting lifecycle checks and compliance.
- Supplier performance monitoring.
- Issue management and expediting: handling supplier production problems, delivery delays and demand changes; proposing and tracking action plans with full traceability and managerial visibility.
- Purchase order updates and confirmations, lead-time exception handling, shortage risk detection and mitigation.
- Advance shipping notice/invoice reconciliation and root-cause attribution to enable systematic cost recovery.
Individually, each micro use case looks small. Together, they transform the function’s service, cost and speed.
There are three criteria companies should use to prioritise roles for agentic AI implementation:
- Task complexity
Simpler, more structured tasks are easier for AI to execute reliably. But even in more complex roles, breaking work into smaller components helps an agent work with a higher degree of accuracy.
- Job population size
AI delivers outsized returns when applied to roles performed by many people. A job done by only one or two team members may not justify the investment.
- Repetitiveness of tasks
If a job involves tasks that follow the same pattern daily or weekly, AI is far easier to deploy and scale. Jobs with high variability may still be good candidates but will require stronger redesign and oversight frameworks.
Designing impactful AI agents
While identifying the right opportunities is an important first step, agent design is what really determines whether you reach production-grade impact.
Effective agentic AI design combines capabilities across three layers. Your agents need to be able to:
- Read and reason over unstructured inputs like emails, chats, meetings and documents in addition to structured data/KPIs from enterprise systems.
- Apply policies, analytics and optimisation with guardrails and auditability in order to make reliable decisions.
- Operate inside systems and communicate back through the same channels teams use.
Achieving this requires the right infrastructure and a coherent technical stack. The minimum requirements for businesses are:
- A data and analytics platform to store, transform, analyse and visualise data.
- An agentic platform to design, configure and orchestrate job-focused agents with usage and accuracy monitoring.
- Enterprise integration via API connectors and batch feeds. Where APIs are missing, robotic process automations (RPAs) can be used selectively. An emerging approach is AI computer use where agents safely operate graphic user interfaces (GUIs) under strict policies, observability and logging.
Managing the risks of agentic AI
Like any transformative technology, agentic AI also introduces risks that businesses must actively manage. A key consideration is accuracy. AI can sometimes provide incorrect outputs, which can create operational issues if left unchecked. To mitigate this, organisations need rigorous pre-launch testing, continuous accuracy monitoring and systematic root-cause analysis when errors occur. Techniques such as refining prompts, breaking complex tasks into simpler components and using oversight agents to verify outputs in real time all help raise performance. The most important technique, however, remains maintaining human control and oversight at every stage of the workflow.
At the same time, companies must ensure sensitive information is not stored by external providers, personal data is handled in line with regulations and models operate within secure technical environments. There are also social and organisational risks to manage. The introduction of AI can generate anxiety among employees, particularly those concerned about job security. Clear communication, transparent change management and meaningful involvement of teams throughout the transformation are essential to maintain trust and support successful adoption.
Prioritising the right skills
This is not a tool rollout – it is an enterprise transformation that changes how work is organised, measured and improved. A successful implementation of agentic AI requires buy-in and collaboration between a number of key stakeholders:
- A business process owner (BPO) to lead end-to-end redesign, define guardrails and service levels, and prototypes quickly with users.
- A data-trained engineer who will act as an AI agent builder. They will design and productionise reliable, enterprise-connected agents with monitoring, fallback strategies and quality controls.
- Data analysts and data engineers to build analytical components and data pipelines, ensure data quality, timeliness, lineage and visualisation.
- Enablers of change and internal champions who can drive prioritisation, training, communications, adoption and performance management.
Developing the right skills is essential for scaling agentic AI, but the approach varies by company size. Large enterprises may be able to focus primarily on building strong internal capabilities while partnering with external experts to accelerate early projects, train teams and design the operating models needed for long-term scale. Smaller and mid-sized organisations often prefer more flexible arrangements, relying initially on specialist partners with the option to upskill internal teams over time. Across all contexts, the most successful companies strike a balance between strengthening internal talent without slowing the pace of transformation.
Streamlining supply chain management to increase competitiveness for an under-pressure automotive supplier
Context and ambition
Facing aggressive competition from regional players, the leadership of this automotive supplier were looking to make big changes to the company’s supply chain management. Among the targets were a 20% reduction in indirect headcount, a 10% reduction in inventory and 5% decrease in direct logistics costs over four years. This level of improvement is challenging. It is roughly equivalent to the gains achieved over the past 15 years in a performance-obsessed industry, but delivered in a quarter of that time.
We implemented a three-stage process:
- Building a strategic transformation plan (one month)
- We mapped jobs across functions and ran deep dives to size both efficiency and performance potential.
- Next, we structured workstreams and built a time-phased roadmap that positioned gains and investments over time.
- A core team was mobilised through a training programme that focused on acculturation to agentic AI, concrete cross-industry examples and hands-on prototyping of first agents.
- Architecture choices were made around agentic platform selection and defining how unstructured data (emails, meetings) would be ingested and governed in line with GDPR and HR policies.
- Established a communication strategy to start conversations on the “why,” “how”, and safeguards. Its primary goals were to address AI-related concerns, equip managers with FAQs and talk tracks as well as set up listening channels and a champions network.
- Procurement planning pilot (three months)
Building on field observations and process redesign, we identified around 60 detailed micro use cases. We then delivered a three-month pilot that integrated with core systems and communication channels.
During the pilot, we provided continuous support to monitor performance, quickly fix any problems and safely roll out the system in stages, always keeping a human ready to step in if the AI made a mistake. This allowed us to maintain a reliability rate of 99.9% for critical tasks.
The first stage of the pilot delivered several clear benefits for the client:
- A 36% reduction in total workload across the procurement function, with service stability improving in parallel.
- Earlier risk detection, precise root-cause tracking and systematic re-invoicing for supplier-caused events, resulting in more proactive management and cost recovery.
A second cycle expanded coverage to second-priority use cases and refined prompts, policies and integrations based on telemetry and user feedback.
- Scaling
Following the success of the pilot, the client launched the scale-out plan to additional priority functions like in-plant logistics, master data management and factory planning. Rather than pre-defining use cases, each function begins with a short field analysis to map jobs, decompose activities and surface the micro use cases that actually drive workload and service.
The guiding principle is that whenever a repetitive activity combines structured system data with unstructured signals, there is room for agents to deliver material impact. Each stream commits to a timeboxed pilot with the same support, production monitoring and continuous-improvement loop to sustain accuracy and scale impact.
You can move from concept to value in weeks with a disciplined approach:
- Launch the strategy by running mapping exercises to prioritise jobs, align on objectives and success metrics.
- Mobilise a core team through a pragmatic training programme to build shared understanding and hands-on capability.
- Select one function with repeatable tasks and measurable KPIs.
- Run a two to three-week job mapping and redesign sprint to quantify opportunity across efficiency and performance.
- Deploy the minimum viable stack by extending the data platform, deploying an agentic platform and integrating two to three core systems. Plan for RPA or batch integration instances where APIs are missing.
- Ship a pilot agent covering 20–30 micro use cases with clear service levels, then iterate to phase two with second-priority use cases.
Engaging with agentic AI is no longer optional
Agentic AI is powerful because it industrialises dozens of small improvements across a job until they add up to a structural advantage. The winners aren’t chasing isolated use cases anymore. Now the race is on to redesign processes around AI capabilities, quantify impact up front, build the right stack and team and execute quickly.
With some companies moving faster than others, those that delay adoption risk higher costs, slower decision-making, weaker service levels and a widening productivity gap. Those that embrace agentic AI have the potential to operate faster, smarter and at significantly lower cost. By reimagining entire jobs and scaling performance gains far beyond what traditional automation could achieve, early adopters could set the competitive pace for their industries.
Getting started now positions you to capture today’s value and to compound tomorrow’s. In doing so, you turn AI from a promise into a durable, accelerating competitive edge.
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