July 25, 2025
5 min read
Stu Robarts
Agentic AI systems with multiple collaborating agents promise huge business performance gains and faster decision-making in complex tasks.
Agentic AI Systems to Revolutionize Business Performance with Collaborative Multi-Agent Models
Agentic artificial intelligence (AI) systems, which consist of multiple agents working collaboratively, are poised to drastically transform business performance in the near future. Chris Ashley, Vice President for Strategy at Peak, a company specializing in AI-driven business optimization, shared insights on this transformative potential during an episode of GlobalData’s Instant Insights podcast. Ashley predicts that agentic AI systems, involving tens of agents interacting to manage complex business processes, will deliver an "enormous uplift in business performance" within the next few years.“Rather than just leveraging general-purpose agents like ChatGPT’s very impressive agent, we’ll start to see systems that have 10, 20, 30 agents all interacting together and collaborating together to work on really, really complex tasks,” Ashley said. “I’m personally really excited about that. I think this is where we’ll see breakthroughs in science, in physics, on some of the world’s hardest problems over the coming years.”This discussion follows OpenAI’s launch of the ChatGPT agent, a consumer-facing example of agentic AI capable of independently carrying out complex tasks through its own reasoning. Agentic AI represents the next major evolution in AI technology, moving beyond deterministic workflows to systems that learn, adapt, make decisions, and perform sophisticated tasks autonomously. Ashley emphasized the growing interest not only in general-purpose AI agents but also in specialized agents tailored for business environments. These specialized agents have the potential to generate meaningful performance improvements across various industries.
Use Cases of Agentic AI in Business
Ashley highlighted several practical applications of agentic AI, including:- Quote pricing
- Markdown and promotions
- Sales and operations planning
- Scenario planning within supply chains
- Merchandising He explained that the core benefits center around accelerated decision-making, scalability, and compounded returns on investment. For example, in manufacturing and consumer packaged goods sectors, agentic AI systems are already deployed to handle complex quote pricing processes that previously took many hours.
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“These processes, a couple of years ago, would have taken seven, eight hours to complete, been really burdensome to grab all of the structured and unstructured data, analyse it, process it, package it into a quote response, send it back to a customer.
We’re now seeing agentic systems being leveraged, where you can respond within minutes, and the compound impact on that is obviously enormous. You’re returning many, many tens of thousands of hours of time back into a commercial sales team that can focus on building relationships and grabbing more market share, rather than having heads in spreadsheets analysing complex data and responding to quote requests.”
Risks and Considerations
Despite the promising benefits, Ashley cautioned that agentic AI systems introduce new risks due to their autonomous nature. Unlike traditional software, these systems are not hard-coded and deterministic, which can lead to compounded risks if not properly managed.“If you have poorly mapped processes within your business, if you have a lack of standardised data governance, if you have a lack of a governance model for the AI agents themselves to monitor the performance of those agents, then you’re exposing yourself to heightened levels of risk by deploying these systems, because they can obviously compound.”Proper governance, data standardization, and monitoring frameworks are essential to safely harness the power of agentic AI.