UMVA has learned that business leaders are wrestling with a paradox: AI dominates boardroom chatter, yet deep‑seated skepticism still grips many firms unready for autonomous AI agents.
Steve Lucas, CEO of a leading integration platform, warned that the technology’s novelty fuels doubt, but also pointed to a clear‑cut path forward—start with high‑return scenarios that instantly boost the bottom line.
He highlighted sales and marketing as the low‑hanging fruit, where generative AI can wrestle away tedious data‑crunching, planning, and preparation, freeing humans to focus on strategy and relationship‑building.
The recipe begins with deploying a generative AI tool that employees can access today, then hunting for automation opportunities that amplify productivity without sacrificing human judgment.
Lucas stressed a non‑negotiable prerequisite: data must be clean, accessible, and real‑time. “If your data is a mess, AI will sputter and fail,” he cautioned, recalling his own company’s struggle to tame data quality.
Even a data‑centric firm admits that 80 % of its sales and marketing interactions now ride on AI, underscoring how essential rigorous data governance has become.
Governance, he added, is a mountain of complexity for firms handling sensitive information across regulated sectors. “If we wrestle with privacy rules, imagine the hurdles our customers face,” he said.
Faced with a flood of AI models, Lucas observed that companies are over‑rotating, trying to pick a single “right” solution. “The answer is all of them,” he declared, urging the creation of an intelligent prompt‑routing layer to orchestrate multiple models.
His vision positions the platform not just as an integration engine, but as the backbone of an “agentic enterprise” where AI augments human talent, letting people converse, imagine, and solve problems at scale.
Michael Bachman, head of emerging technology strategy, echoed the urgency, describing a relentless pressure on firms to hop aboard the AI train before it leaves the station.
He advised against a “big bang” rollout, urging businesses to first master where their models live and ensure data integrity, then breathe easy as confidence grows.
Bachman warned against reinventing the wheel—pre‑trained models from industry leaders already outperform most in‑house attempts, making external AI the smarter choice for most enterprises.
Legacy systems, especially in banking, present a ticking time bomb as AI agents begin to hammer outdated mainframes faster than any traditional software ever could.
Transitioning away from brittle infrastructures is now critical, and the platform’s open‑source suite of agent skills offers a bridge to modern, AI‑driven operations.
These agent skills integrate seamlessly with leading AI assistants, enabling natural‑language interactions that bring AI from isolated pilots to enterprise‑wide production.
