During the recent Intelligent Automation Conference, executives from major financial and industrial firms gathered to examine why many automation projects fail to move beyond pilot stages. The event, held in a virtual format, featured speakers from NatWest Group, Air Liquide, and AXA XL, as well as Promise Akwaowo, Process Automation Analyst at Royal Mail.
Key Takeaway: Elasticity Over Bot Count
Akwaowo emphasized that the success of scaling intelligent automation depends on the underlying architecture’s ability to handle variable workloads, rather than simply increasing the number of deployed bots. He noted that systems must remain stable during peak periods such as end‑of‑quarter financial reporting or sudden supply‑chain disruptions. “If your automation engine requires constant sizing, provisioning, and babysitting, you haven’t built a scalable platform; you’ve built a fragile service,” Akwaowo said.
The analyst highlighted that integration with customer‑relationship management systems like Salesforce or low‑code vendor platforms should focus on building a cohesive platform capability, not a loose collection of scripts. He warned that moving from controlled proofs‑of‑concept to live production environments introduces inherent risk, and that large‑scale, immediate deployments often cause disruption, undermining the expected efficiency gains.
Phased Deployment and Governance
Akwaowo advocated for a disciplined, staged approach that begins with a formal statement of work and validates assumptions under real conditions. He stressed the importance of understanding system behaviour, potential failure modes, and recovery paths before scaling. For example, a financial institution using machine learning to process transactions could reduce manual review times by 40 percent, but must ensure error traceability before applying the model to higher volumes.
He also addressed a common misconception that governance frameworks slow delivery. According to Akwaowo, bypassing architectural standards allows hidden risks to accumulate, eventually stalling momentum. In regulated, high‑volume environments, governance provides the foundation for safely scaling intelligent automation, establishing trust, repeatability, and confidence necessary for company‑wide adoption.
Centre of Excellence and Standardisation
Implementing a dedicated centre of excellence can standardise deployments. A central Rapid Automation and Design function ensures that every project is assessed and aligned before reaching production. Such structures guarantee operational sustainability over time. Analysts also rely on standards such as BPMN 2.0 to separate business intent from technical execution, ensuring traceability and consistency across the organisation.
Agentic AI Integration in ERP Ecosystems
As large enterprise resource planning (ERP) providers rapidly incorporate agentic AI, smaller vendors and their customers face pressure to adapt. Embedding intelligent agents directly into smaller ERP ecosystems offers a path forward, augmenting human workers by simplifying customer management and decision support. This approach allows businesses to deliver value to existing clients rather than competing solely on infrastructure size.
Integrating agents into finance and operational workflows enhances human roles rather than replacing accountability. Agents can manage repetitive tasks such as email extraction, categorisation, and response generation. By relieving finance professionals of administrative burdens, they can focus on analysis and commercial judgement. Even when AI models generate financial forecasts, the final authority over decisions remains with human operators.
Observability and Anomaly Management
Building a resilient capability requires patience and a commitment to long‑term value over rapid deployment. Business leaders must prioritise observability, allowing engineers to intervene without disrupting active processes. Before scaling any intelligent automation initiative, decision‑makers should evaluate readiness for inevitable anomalies. Akwaowo challenged the audience: “If your automation fails, can you clearly identify where the error occurred, why it happened, and fix it with confidence?”
Implications for the Automation Landscape
The conference underscored that scaling intelligent automation without breaking live workflows demands a focus on architectural elasticity, disciplined deployment, robust governance, and observability. Companies that adopt these principles are better positioned to achieve sustainable growth and maintain operational stability as they expand automation initiatives across their organisations.
Future Outlook
Industry participants anticipate that the adoption of agentic AI within ERP ecosystems will accelerate over the next few years, driven by the need for more efficient customer management and decision support. Regulatory bodies are expected to refine governance frameworks to support safe scaling of automation solutions. Companies that invest in centre‑of‑excellence models and standardised design practices are likely to lead the transition to more resilient, scalable automation platforms.
