Large scale organisations are under pressure to undergo a fundamental transformation of processes, functions, and models through the integration of AI-driven capabilities.
Unlike incremental improvements, we are seeing a significant shift in how businesses operate, make decisions, and create value. This moves beyond automation and low-value uses of AI to simplify a process or increase a certain efficiency. This is a move to redefine workflows, customer experiences, and industry dynamics.
Businesses are primarily playing in the Process Reinvention stage, focussing on how to do things faster and cheaper, on opportunities that are prime for automation. Here, AI is used for specific tasks such as fraud detection, information retrieval, customer service chatbots, and optimising marketing campaigns.
A few early adopters are transitioning into Function Reinvention, where AI is orchestrating multiple processes within key business functions – and it is these organisations who are deriving huge value.
For example, we’re working with insurers who are completely reimagining the underwriting process, deploying AI to improve the efficiency and effectiveness at every stage of the value chain. It is at this stage that we’re seeing Agentic AI come to the fore – autonomous agents, working together, to provide more than just a single output and providing a new level of value across multiple complex workflows.
However, the full potential of AI-driven reinvention—enterprise-wide transformation, market evolution, and economic restructuring—is still in its early stages.
Agentic AI represents a step change in AI development, moving beyond traditional Generative AI models. The hype of the last few years has been driven by Generative AI and LLMs, but these approaches operate within a simplistic framework with a single input and a single output.
But the real-world problems and processes that enterprises are trying to optimise with AI are more nuanced and complex than this model allows. The autonomous agents that comprise Agentic AI can:
Agentic AI extends beyond Gen AI, using other data and AI capabilities and allowing the integration of different models to achieve more complex outcomes. This autonomy enables businesses to build intelligent, goal-driven systems that operate with minimal human intervention, optimising complex processes and workflows.
Currently, we know the dominant perception among our customers is that Agentic AI is a means for low-impact use cases, primarily focused on process optimisation and cost reduction in back-office functions. There are a number of considerations here:
This also requires reinventing the justification for different use cases, moving beyond the cost savings and efficiency benefits and to thinking about the doors opened to new opportunities. Agent-based systems allow you to do something differently, instead of simply automating the exact same tasks and process.
This is an opportunity for organisations to completely reinvent how they reach strategic objectives and add value, but they have to think beyond business as usual. Beyond seeking efficiencies and cost savings.
There are two considerations for the success of Agentic AI and how organisations use it in their reinvention journeys. Agent based systems will place even more emphasis on the data foundations of enterprises, specifically on the quality and accessibility of data. As with Generative AI, deploying these complex technologies on low quality data will result in the whole endeavour falling at the first hurdle, and an organisation’s appetite for the technology diminished.
A second consideration is around how solutions are built. The successful adoption of sophisticated AI capabilities extends beyond the solution itself. It relies on us devising the right strategy that aligns with business objectives and a sophisticated solution architecture that ensures the long-term success of any AI capability. This will ensure customers don’t fall into the trap of repeated POCs and endless siloed innovation.
A number of our customers have focused on using LLM for information retrieval, analysis and chat, however these solutions are likely to be commoditised. Now, we are already starting to apply agent-based systems for some of our customers, making them early adopters, who are already seeing the value.
Agentic AI is being used to generate personalised market commentaries, automating a costly and time-consuming process. Agents summarise articles, critique content, ensure accuracy, and refine outputs before human validation.
Mesh-AI is building an agent-based bid response system to enhance efficiency in bid writing, increasing speed, reducing costs, and improving content quality. The system utilises information from across the organisation and enhances bid responses, making them more efficient to produce and elevating the quality of responses.
Agentic AI is set to redefine business reinvention, moving organisations beyond process optimisation into strategic transformation. As businesses progress through the phases of reinvention, Agentic AI will unlock new levels of autonomy, intelligence, and value creation. The companies that successfully adopt and integrate Agentic AI into their operations will gain a competitive edge in an increasingly AI-driven economy.
