The AI maturity gap

AI agents are advancing rapidly, taking on more complex roles in customer experience, operations and digital transformation. However, despite their growing sophistication, many still remain surprisingly “childlike” in their ability to deliver meaningful impact. 

 

As enterprises deploy agentic AI systems at scale, this immaturity becomes increasingly visible – showing up in inconsistent performance, and a lack of grounding in the unpredictable context of human communication and behaviour. To remain relevant in the agentic era and deliver tangible ROI, businesses need agents that can connect real human experiences to measurable outcomes.  

 

 

The impact of immature agents

Despite worldwide AI spending reaching $1.5 trillion in 2025, most organisations are a long way from having mature, scalable agentic systems. In fact, recent research shows nearly two-thirds of companies say they haven’t begun scaling AI across the enterprise. The disconnect is clear: spending is surging, but the maturity of value-driven use cases isn’t. The core reason is that many businesses are still approaching AI in the wrong way.

 

Most AI systems have been trained on static datasets, rather than exposed to the diverse, emotionally rich and context-dependent interactions that occur in the real world. This results in immature agents that often struggle to pick up on the nuances of live scenarios. When an agent encounters unpredictable shifts in tone, intent or customer behaviour, that means these outputs often fall short. While they may deliver technically accurate responses, many agents fail to address the underlying issue or provide lengthy answers that sound confident but don’t lead to a resolution. This is a fundamental limitation of training AI for response quality instead of real-world business outcomes.

 

 

The inefficiencies of AI model training

Organisations often try to fill the maturity gap with continuous human oversight – validating responses, correcting errors, and manually reviewing agent performance. While this may work in early pilot models, the weight of enterprise scale means these processes often fail in production. Manual supervision drives up costs, limits speed, and creates an operational ceiling that current AI strategies struggle to overcome.

 

As a result, some organisations have recognised that they can’t rely on manual oversight alone and have turned to open-source large language models to help train their AI. However, this introduces a different set of challenges, as these models aren’t trained on the specifics of customer data, domain knowledge, or unique operational context. They therefore often fail to understand the nuances of an organisation’s customer journeys and behaviour patterns.

 

The consequence is a widening gap. Endless human oversight is clearly not viable, and generic LLMs can’t understand the specifics of every business. This leaves many unable to unlock the ROI they expect from AI, and points to a deeper issue. The biggest barrier to achieving ROI on AI isn’t the maturity of the tools, but the way organisations train the models that power them.  

 

 

Changing perceptions on AI agents

The debate over model complexity and type matters far less than the way AI is implemented. Without building models around how humans naturally behave, AI loses access to these first-hand insights, which limits its understanding of real-world interactions. Truly mature agents learn continuously from live conversations, integrating new behavioural patterns and mapping them to tangible outcomes.

 

When agents are trained and refined based on observed human actions, they become more accurate and efficient, providing businesses with a tangible ROI. For example, by giving their AI visibility into customer journeys across apps, websites, and services, organisations can unlock more actionable insights that help them resolve issues faster and reduce escalations.

 

To excel in the Agentic AI era, businesses should extend their monitoring practices beyond static funnels, so they can respond to customer needs in real-time and with greater impact, based on experience patterns observed during previous interactions. This enables teams to analyse specific cohorts of customers and understand the precise steps they take during their journey, so they can resolve common issues they encounter in a targeted way.

 

As a result, agents will be able to respond not just accurately, but with empathy shaped by real-world patterns and proven outcomes. AI maturity, therefore, will increasingly be defined by trust that agents behave reliably, transparently, and in alignment with business goals and the ability to deliver positive customer outcomes.

 

 

The future of mature AI

One principle will define the evolution of agentic AI: success will be measured in outcomes. Resolution, satisfaction, and trust will serve as the new maturity metrics, defining whether AI agents truly add value.

 

Organisations that embrace outcome intelligence will be best placed to turn promising, childlike AI pilots in sandbox environments into mature, enterprise-ready systems. This progression will enable organisations to transform operations, elevate consumer experience, and unlock the full potential of human-AI collaboration. 

 

---

 

Keith Zubchevich is President and CEO at Conviva

 

Main image courtesy of iStockPhoto.com and ValeryBrozhinsky