Unlocking ROI from Agentic AI: The Data Engineering Foundations Enterprises Need Now

AI promises to revolutionise the way we do business, but the reality for many UK enterprises is rather more complicated. According to Gartner, a staggering 85 percent of AI projects fail to deliver real business value - and the culprit is often poor data quality and integration. In this blog, we will explore how robust data engineering can unlock the true ROI of agentic AI, offering a practical roadmap to transform your business operations through intelligent automation.

Why Agentic AI Falls Short Without Strong Data Foundations

Agentic AI is a step beyond traditional chatbots - it does not simply converse, it takes action. Whether updating your CRM, processing invoices, or managing supply chain disruptions, these agents are designed to make autonomous decisions. However, when plugged into siloed legacy systems, they lack the 'ground truth' needed for accuracy.

Poor data quality becomes a ceiling for AI performance. If your organisation's data is scattered across departments, your agentic AI will inevitably deliver inconsistent results, eroding trust and undermining ROI.

• Audit your current data pipelines for silos before scaling AI agents.
• Identify gaps in data quality that could cause AI errors or 'hallucinations'.
• Ensure agents have access to real-time context, not just historical data.

Data Engineering Best Practices for Agentic AI Success

To realise the promise of agentic AI, data engineering must be seen as the cornerstone of success. Adopting a cloud-first, modular architecture allows your agents to scale seamlessly with your business. Data governance and compliance are no longer optional - especially in sectors like finance and HR, where every autonomous action must meet strict regulatory standards.

• Centralise your data sources to provide a single source of truth for agents.
• Automate cleansing pipelines to avoid 'garbage in, garbage out'.
• Prioritise cloud-first architectures for scalability and low latency.

Measuring and Maximising ROI in Agentic AI Projects

ROI from agentic AI is not simply about reducing headcount - it is about expanding your team's capacity and accelerating revenue. Take DPD, for example: by automating complex workflows with agentic AI, they unlocked over £248,000 in monthly recurring revenue. This success was built on clear KPIs - tracking lead reactivation, response times, and error reduction.

• Track cost savings and process acceleration as your main KPIs.
• Use revenue impact as a benchmark for automation success.
• Monitor agent performance continuously to optimise data inputs.

Conclusion

To succeed with agentic AI, you need to go beyond basic chatbots and build autonomous systems on unified, real-time data architectures. In 2026, it is not about whether to adopt AI - it is about making sure your foundation is strong enough to scale with confidence.

Further Reading and References

• Gartner (2025). AI Maturity and Data Quality Report: Analysing Project Failure Rates in Enterprise Automation.
• McKinsey & Company (2025). The State of AI in the Enterprise: How Unified Data Platforms Accelerate Deployment.
• Olivia AI (2025). Enterprise Case Study: How DPD Leveraged Agentic AI for Revenue Growth and Operational Efficiency.