The questions CEOs ask

In the vast majority of cases (≈95%), it is not the recommended first step. Before putting agents on the table — autonomous systems that pursue goals of their own — it pays to follow a sequence that lowers risk and maximises return.

1. First, the ESOAR method (getting the house in order): Eliminate → Standardize → Optimize → Automate → Robotize. We deal with the cause of the inefficiency before adding complex technology.
2. Then, AI assistants (with human supervision): once the process is clean, assistants help the users and depend on human approval.
3. Last, AI agents (autonomous, and higher risk): they bring power, but also operational complexity and governance/observability requirements. Analysts expect >40% of agentic AI projects could be cancelled if the prior step of strategy and control is skipped.

Practical conclusion: ESOAR first, assistants next, and only then consider agents — where there is a business case, reliable data and governance.

Because without a strategy, the odds of failure are high. Studies put it at ~70% of transformations missing their targets for lack of focus, metrics and governance. Our consulting puts you in the 30% that does capture the value: we prioritise by financial impact, define the KPIs and set governance from day one.

Automating “piece by piece”, with no roadmap and no business case. In RPA, firms such as EY have reported 30–50% failure rates on first attempts where the method and the right choice of cases were missing. We start from end-to-end analysis and a business case for each process to avoid it.

Before anything is built, we put a number on every initiative: investment, savings or incremental revenue, payback and risks. During delivery we measure business KPIs (margin, capacity, errors) and operational ones (times, availability). Excellent change management makes a project 7× more likely to meet its objectives.

We integrate your software (ERP/CRM, Google Workspace, APIs), prioritising by ROI. We combine orchestration and custom code only where it adds value. We guarantee solutions that scale, with no technical debt and no data fragmentation.

We minimise data, anonymise or pseudonymise it where that is appropriate, and grant access on least privilege. We are governed by the GDPR principles (lawfulness, transparency, purpose limitation, minimisation, accuracy and security) and by official guidance on AI and data protection.

Cancellations driven by unclear value, rising costs and insufficient risk controls. Gartner expects >40% of agentic AI projects to be cancelled before 2027 if these points are not managed. What we propose: a solid business case, quality data, and technical and functional governance from the outset.

Three lines of work: Strategic consulting (diagnosis and a roadmap with a business case), Custom development (phased delivery with business metrics) and Maintenance (SLAs, observability and continuous improvement). Budgets tied to scope and expected return; we avoid oversizing: we build what moves your P&L and drop what doesn't add value.

We run “day two”: we monitor the critical workflows, resolve incidents at root cause, optimise costs and prioritise enhancements by impact. That reduces the risk of post-implementation decay that many badly governed transformations suffer.