The agentic AI advantage: Scaling isolated use cases to create the intelligent enterprise

Organisations are investing heavily, running pilots, and testing proof-of-concept projects. But here’s the catch: many of these efforts never make it past the experimentation phase. Disconnected initiatives, duplicated spending, and only a fraction of the potential value is being realised.

So, what’s holding these investments back?

It’s not the technology. It’s the mindset.

Many senior leaders still view AI as a plug-in to their existing systems, as an add-on technology or tool rather than a transformational force. To unlock AI’s potential and achieve scalable impact, leaders must now think beyond isolated use cases and move toward embedding AI at the core of their business. AI is not just a tool for optimisation or incremental process improvement; it presents an opportunity to fundamentally rethink and reimagine how business outcomes are delivered.

Agentic AI is being touted as the next frontier in managing complex workflows and coordinating strategic tasks. A strategic approach that treats AI not as a passive tool, but as an active partner – one that’s outcome-driven, context-aware, and deeply integrated into business processes. With agentic AI, organisations can move from dabbling in pilots, to fully industrialising AI to build an intelligent enterprise, an enterprise enabled and powered by AI.

Evaluating agentic AI use cases – start at the end

Before building or buying AI capabilities, leaders must start with clarity. What are the strategic outcomes they want to achieve? What are the repeatable tasks that could be eliminated? Which decisions could be enhanced – or even made – by AI agents? This isn’t about chasing the latest tech trend; it’s about aligning AI initiatives with business value. So how can leaders get started?

• Assess across your organisation to find the places where AI could make the biggest difference. Next Made Real – A business leader’s guide to generative AI offers practical frameworks to help explore, specify, and develop high impact use cases
• Prioritise areas where AI can drive measurable outcomes such as cost reduction, risk mitigation, accelerating R&D, or help transforming the customer experience.
• Engage cross-functional teams early to ensure alignment between business goals and AI capabilities.

Once the business’ needs are identified, the next decision is whether to build AI capabilities in-house or buy from external providers. This choice depends on the organisation’s maturity, talent availability, and strategic priorities. Build if organisations have strong data science teams, proprietary data, and a need for customisation. Buy if speed-to-value is critical, or if the use case is well-served by existing solutions. Alternatively, hybrid models might be a good fit, where foundational models are procured and fine-tuned internally for specific use cases. However, as AI becomes an essential capability for organisations, developing in-house expertise is more important than ever.

Create an agentic AI blueprint

To scale AI effectively, organisations will also need a blueprint that maps AI agents to business outcomes. This blueprint should define the types of agents required, their roles, and how they will interact with people and systems. Senior leaders should define the decision-making scope of each AI agent, asking what decisions they aim to support or make, before assessing how they align AI agent capabilities with business processes and desired outcomes.

A key consideration is around governance and responsible AI. Key threats for agentic AI, specifically, include risk of unauthorised access to sensitive data, carrying out unauthorised processes, cascading hallucinations, and overreliance on AI by humans. Leaders should include a risk assessment in their agentic AI blueprint to address bias, hallucinations, and cybersecurity vulnerabilities, which should follow the same approach taken for any AI risk assessment.

Scaling AI isn’t just about building more models or developing algorithms – it's about rethinking the way we build them efficiently, consistently, and sustainably. In addition to a blueprint, an agentic AI factory approach provides the governance, tools, and standards needed to industrialise AI development. This means establishing standardised development pipelines for AI agents and implementing quality assurance processes to ensure reliability and compliance. Modular architectures and repeatable components will enable reuse and rapid deployment across business units.

Creating an agentic AI factory will also help embed AI into the day-to-day operations of the business. This means training the agents on organisational context and ensuring they have access to the right data at the right time. Integrating AI agents with enterprise systems (such as ERP, CRM) will ensure seamless data flow and allow the agents to train on historical data, business rules, and contextual nuances. It’s also essential to design feedback loops so agents can learn and adapt over time.

Reimagine workflows – it’s not about automation

AI shouldn't replace people – but it is changing how they work, so the question becomes: “How can I do my job with AI?” Workforces should be encouraged to deconstruct their tasks to identify what AI can support with – and, crucially, scrutinise existing workflows. Self-evaluating workflows gives renewed ownership to the workforce and is more likely to drive genuine transformation through trust and collaboration. The focus should then be on developing training programmes that focus on AI literacy (for example prompt training), judgement, ethical use, and – most of all – creativity.

Re-training employees on how to do their job with AI as opposed to just training employees on using a new tool will drive greater value will help shift the focus from process efficiencies to value-driven decision making and help teams embrace AI as a partner. An existing culture of experimentation and continuous learning is key. If organisations don’t already have this embedded, expecting the workforce to incorporate agentic AI tools will feel like swimming against the tide.

Agentic AI represents a fundamental shift in how organisations think about and use AI. It’s moving beyond pockets of experimentation with automating tasks towards augmented decision-making and workflows that unlock new sources of value. For senior leaders, the challenge is clear: move beyond pilots and proofs of concept, and start connecting outcomes to scale the intelligent enterprise.