In the past decade, business institutions have steadily embraced artificial intelligence – from fraud detection systems to customer service bots. But a new phase is emerging – one that shifts from passive machine learning to active decision-making and orchestration. Welcome to the era of agentic AI, where intelligent agents not only process data but act autonomously across entire operational workflows.
Across the corporate landscape – from accounts receivable to customer support and treasury forecasting – AI agents are beginning to operate like skilled digital workers. These systems don’t merely react; they reason, plan, and collaborate. And their growing influence is rapidly redrawing the architecture of business services.
The Agentic Evolution in Action
Nowhere is this change more evident than in the world of accounts receivable. Platforms like Billtrust have pioneered multi-agent AI systems that divide responsibilities among autonomous agents. These agents communicate through a central layer of orchestration, swapping insights, adapting to new variables, and making joint decisions – all similar to a synchronized team.
This multi-agent approach is not theoretical. According to Billtrust, organisations using such systems have reported major operational gains, including 95% automation of cash applications and a 50% reduction in days sales outstanding (DSO). These numbers signal more than efficiency – they reflect a rethinking of what back-office operations can become.
HighRadius, another leader in enterprise AI, offers further evidence. Their agentic AI solutions have led to 41% faster close cycles, near-elimination of reconciliation discrepancies, and major reductions in journal entry errors. The company describes their systems as context-aware and proactive, capable of not just suggesting actions, but initiating them. This is automation with autonomy – a big leap beyond pre-programmed robots.
With treasury and cash forecasting artificial intelligence agents, today treasury and cash forecasting monitor bank flows in real-time, detect risk, and resolve reconciliations of irregularities on-the-fly. When they see irregularities, they don’t wait for some individual to tell them – they correct or escalate with situational precision. These aren’t machines; these are a business nervous system that pulsates with streaming data and self-rebalances.
More than Bots: Generative AI for Corporate Services
Whereas automation for some years was associated with cost-reduction plans, a quieter application is gaining traction: generative AI for the customer. In these contexts, companies like Synechron are deploying large language models (LLMs) to create natural, human-like dialogue for the customer service. What was once static, scripted output for the chatbot now responds on the basis of tone, context, and prior behavior – personalizing advice and boosting satisfaction.
But the combination of generative and agentic AI takes this even further. Generative models provide the voice and interface, while agentic systems deliver the reasoning and action. Imagine a customer asking for a payment extension via chatbot. A generative AI crafts the response – but it’s the agentic system that checks credit status, models repayment risk, initiates a revised schedule, and updates internal records. It’s not just conversation – it’s coordination.
Rewiring the Enterprise Structure
Behind these advances lies a more foundational challenge: restructuring the very fabric of organizational operations. A recent McKinsey report on “The AI Bank of the Future” outlines four layers that must evolve simultaneously: smarter customer engagement, AI-driven decisioning, scalable data infrastructure, and platform-based operating models.
This strategic approach closely mirrors the recommendations in Elsewhen’s recent report, From Generative AI to Generative UI. Rather than focusing solely on content generation, Elsewhen advocates for systems that dynamically adjust to user needs, provide intelligent suggestions, and support autonomous processes. The report introduces a vision of user interfaces that are not static dashboards but adaptive assistants – constantly learning and responding in real-time.
What both McKinsey and Elsewhen argue is that truly transformational AI doesn’t live in isolated tools. It thrives in ecosystems. Success will depend on stitching together generative models, intelligent agents, and UI layers into a seamless, scalable framework – one capable of evolving with the business itself.
Strategic Gains and Measurable Results
The rewards of adopting agentic systems are not abstract. For many corporate teams, they are already tangible. HighRadius customers have seen up to 95% accuracy in forecasting models, along with significant labour reductions. Billtrust clients report that autonomous agents now perform tasks once handled by entire teams. In customer-facing functions, generative AI enables scalable yet personalised service – freeing up human advisors for higher-value, emotionally complex interactions.
Such gains are more than operational. They're strategic: agility. In a volatile world – economic dislocations, fluctuation of regulations, increasing demands of customers – AI-powered, agile systems provide the foresight and flexibility that manual ones can't even approach.
The Way Forward
To get underway, however, companies must rethink implementation. Step one, suggests McKinsey, is data consolidation. Pure, consistent datasets are the base of every scalable initiative of AI. Firms can then pilot agentic workflows – AR or treasury forecasting, say, is a good starting point – before expanding to orchestration outside of department silos.
Generative UI can be the user-facing interface, so humans can converse naturally, efficiently with their AI colleagues.
With the new model, operations are not just digitalized, but redefined. The enterprise is not just automated but becoming adaptive. Not only efficient, but even intelligent. And with Elsewhen leading the charge of mapping out the architecture and the strategy, the future of business might be nearer than we expect.
