The Road To Agentic AI in Enterprise Consulting

Are we entering another phase of AI with genuinely autonomous decision-making? 

 

We’ve already seen an increase in new chatbots to sophisticated systems that independently manage complex workflows without constant supervision. These autonomous systems, known as agentic AI, are challenging what’s possible in enterprise operations.

 

Recent research reveals a market brimming with enthusiasm in this area: 37% of organisations have already begun implementing these technologies, while an overwhelming 93% express strong interest in exploring their potential.

 

We’re at a critical juncture where technical possibility meets organisational reality. The path to successful implementation isn’t purely technological – it requires thoughtful integration with existing systems, workforce transformation, and robust governance frameworks.

 

Let’s explore what makes this technology different and how your organisation can benefit from it.

What separates true agentic systems from their predecessors is their capacity for independent action. Unlike conventional automation that merely follows predetermined paths, these systems perceive their environment, make decisions, and take action with minimal supervision.

 

It’s the difference between having an assistant who needs constant instruction and one who proactively manages entire projects.

 

This distinction isn’t lost on business leaders either. According to KPMG’s comprehensive survey, 12% of companies have already deployed these systems at scale, 37% are running pilots, and 51% are actively exploring potential applications. When asked what drives their interest, executives cite improved workflow oversight (58%), enhanced application integration (53%), and automation of complex business processes (52%).

 

The most successful implementations share a common trait: strategic focus. Rather than pursuing numerous use cases simultaneously, leading organisations concentrate on fewer, high-impact applications. BCG’s research quantifies this approach precisely – top performers average 3.5 focused use cases compared to 6.1 for less successful implementations.

 

This targeted approach yields remarkable results across industries. Consider how Danish maintenance teams now deploy autonomous drone systems to monitor bridge conditions without continuous human direction, or how Australian utility workers use AI agents to analyse weather patterns and proactively dispatch field staff to clear storm grates before flooding occurs. These aren’t theoretical applications – they represent the practical reality of agentic systems in 2025.

One of the greatest challenges you’ll face when implementing agentic AI is integration with existing infrastructure. This challenge is widespread – with 70% of enterprises still operate on legacy systems, and half of all AI projects fail specifically due to integration hurdles. From the technical perspective, 70% of developers report significant difficulties connecting these autonomous agents with existing systems.

 

The solution often lies in API-based approaches that connect AI capabilities through standardised interfaces, allowing your organisation to preserve investments in core systems while incrementally enhancing them with autonomous features. This technical foundation enables organisations to capture value without wholesale infrastructure replacement.

 

Beyond the technical considerations lies perhaps an even greater challenge – preparing your workforce. Contrary to fearful narratives about job displacement, BCG’s global AI Radar survey reveals only 7% of executives anticipate reducing headcount. Instead, 68% expect to maintain current workforce sizes while focusing on productivity improvements and skill development.

 

Nearly half of executives acknowledge their organisations lack the AI competencies necessary for effective implementation. This reality is reshaping consulting relationships, with 86% of buyers specifically seeking services that incorporate AI and technology assets. The transformation extends beyond technical specialists – 70% of senior leaders want their non-technical employees to develop automation and AI skills as well.

Balancing autonomy with appropriate oversight remains essential for responsible deployment. Security concerns top the list of executive worries, with 56% citing IT security as their primary concern, followed by implementation costs (37%) and integration challenges (35%).

 

As UiPath CEO Daniel Dines aptly notes, “As AI systems become more autonomous, enterprises must strike a balance between autonomy and human oversight to prevent unintended consequences and guarantee that AI-driven actions align with ethical, compliance and legal standards.” (Technology Magazine)

 

Interestingly, many organisations find that existing robotic process automation frameworks provide effective governance structures for more autonomous systems. Max Ioffe, who leads Wesco Distribution’s Global Intelligent Automation Center of Excellence, observes: “I expect that robotic process automation will orchestrate the agents. For larger scale processes, you need clear orchestration and governance and that means a deterministic technology like RPA.” (Technology Magazine)

 

Measurement discipline provides another critical governance lever, though 60% of companies still fail to define and monitor financial KPIs related to their AI initiatives. Organisations seeing the greatest returns typically become cash-flow positive within 4-6 months and strategically allocate their AI budgets – top performers direct 80% toward reshaping core functions rather than pursuing incremental improvements.

The question is no longer whether autonomous systems will transform business operations, but how quickly and effectively your organisation can harness their potential.

 

With 90% of IT executives identifying business processes that would benefit from agentic implementation, the opportunity landscape is vast. This recognition drives substantial investment growth, with enterprise AI spending projected to increase from £27 billion in 2023 to approximately £48.75 billion by 2026.

 

This evolution extends to consulting itself. As Bill Farrell of IBM Consulting observes, “The traditional consulting model is opinion-led and time-intensive… companies must be able to drive more certainty of outcomes and move fast on new opportunities to stay ahead.”

 

Organisations that develop clear integration strategies, focus on high-value applications, build appropriate governance frameworks, and systematically measure outcomes will capture substantial returns.

 

Those that effectively combine technological capability with human expertise will find themselves at the forefront of this transformation – not merely keeping pace with change, but actively shaping how autonomous systems redefine enterprise operations in the years ahead.