Despite investing in AI and data solutions, many businesses still struggle turning mountains of sometimes unstructured data into actionable decisions. There has been much headway in fine-tuning prompts for large language models, but these tend to work in request-response mode. There is tremendous potential to do more, however, which is now being realised with agentic AI.
This intelligent system of coordinated agents promises to take us to the next phase of the AI-enabled organisation with its ability to collaborate, iterate, learn and act in real-time. But doing this effectively requires the right data foundation. The issue is not access to the data – rather that businesses don’t have a system that can provide the context that makes that data usable at the right moment.
From request-response to real-time
Agentic AI promises to break organisations out of the request-response cycle that many of us are familiar with when it comes to using chatbots. As a system of many smaller, specialised agents continuously monitoring activity, it can draw from data coming from different sources simultaneously and decide whether action is needed, without requiring a prompt. Like a high performance team, it can provide up to the second updates and react accordingly.
This can help identify issues and opportunities across operations, marketing, customer experience, supply chains and IT, and create solutions. It means faster, more accurate decisions when dealing with changing customer shopping patterns, demand surges or dips, or problems in the supply chain, for example.
Success depends on the underlying architecture
The obstacle many organisations face in deploying AI agents effectively is a fragmented underlying architecture. With conflicting data or partial context, they will not be able to coordinate or act efficiently.
This situation is causing many businesses to suffer from a “customer decision gap”, where they are unable to spot signals and act on changing customer behaviour in real-time. When AI relies on an infrastructure that presents incomplete customer profiles and stale signals, it can lead to customers receiving irrelevant offers, which exacerbates contact fatigue and erodes trust in a brand.
Realising this, businesses are waking up to millions in missed revenue and wasted campaign spending. As a result, we’re seeing a market shift – away from data-led campaigns and towards real-time interactions, orchestrated by intelligent systems.
Effective agentic systems are based on three principles, each of which will help organisations to take advantage of this new iteration of the technology:
- Unified data: Unified customer identifiers are the baseline for coordinated intelligence. If systems cannot consistently recognise the same customer across channels, every downstream decision becomes less reliable. Signals and behaviours fail to resolve into a complete understanding of the individual.
- Interoperability: AI agents need connected systems that provide shared access to signals, historical context, and operational data across platforms. Consistent interpretation is essential for coordination and real-time action.
- Adaptable environments: Flexible data models and governance frameworks allow AI systems to learn from prior outcomes, refine decisions over time, and operate within clearly defined business rules.
Delivering value across several dimensions
With this architecture in place, agentic AI will be able to deliver the following benefits:
- Automated decisions: Agents can manage routine operational work such as forecasting, personalisation, and budget allocation, allowing teams to focus more on strategy, creativity, and oversight.
- Real-time adaptation: Organisations can respond to changes in customer behaviour while the opportunity still exists, rather than reacting after the fact.
- Consistency across channels: Systems operating from the same customer context reduce conflicting experiences and create more continuity across customer interactions.
- Continuous improvement: AI systems can learn from outcomes over time, improving recommendations, surfacing longer-term patterns, and refining business decisions continuously.
These benefits will strengthen human teams. Although AI agents can work autonomously, they still need human instruction. Intelligent systems will handle day-to-day decisions, while human judgement is essential to ensuring the agentic system is supporting the objectives of an organisation.
Data foundations for success
Agentic AI demands a reworking of how intelligence is embedded into an organisation’s fundamental architecture. Every decision this system makes must be based on the same understanding of the customer or operation. With connected infrastructure it can continuously sense, decide, and act. The infrastructure must be designed for ongoing learning and improvements.
The most successful organisations won’t necessarily be those with the most advanced or largest models. They will be the ones that have created a unified data foundation that provides the conditions necessary to turn data into decisions, reliably, consistently, and in real-time.
