My clients are always curious. Also about AI. So I designed a workshop to explore the benefits and pitfalls of AI.
Below I share extracts of their initial journey and why the foundation was the organisation’s BI – Business Intelligence. The main conclusion from the workshop was that before the organisation could adopt AI at scale, it needed to move beyond static dashboards and embrace more advanced BI capabilities.
These were the main areas:
Dynamic reporting
- Real‑time or near‑real‑time updates
- Drill‑down and interactive exploration
- Adaptable views that follow the business question, not the other way around
Simulated reporting
Just like in most of my workshops, the transformation happened on two levels:
- What if?” scenario modelling
- Forecasting under different assumptions
- Risk‑free exploration of strategic options
This not only strengthens the capabilities and the organisational muscles needed for AI:
data literacy, scenario thinking and trust in data‑driven decision making.
BI as the Foundation of Decision Making
Most of you know, that I champion the decision making as the core component of risk taking and great leadership. Hence, modern BI should and can be so much more than a reporting function – it should be the decision infrastructure of the organisation.
High‑quality BI enables:
- A single version of the truth across departments
- Transparent, traceable decision processes
- Governed data models that prevent errors and inconsistencies
- Accessible insights for non‑technical users
- A culture of evidence‑based decision making
One leader said, “We learned more than to network better. We learned to work better together.”
The latter being the most important point. Yet, whilst you might have heard it before, it is not easy to find organisations, where this is 100 % the case.
When considering the use of AI and in order to gain the benefits, data quality and robust processes and infrastructure becomes key. Without it, AI becomes a collection of disconnected pilots and untrusted outputs.
Why Some Elements of AI Are Not New
Many AI techniques, incl. predictive analytics, forecasting, optimisation, anomaly detection, etc. have existed for decades.
What is new is:
- the sheer scale of data
- the computational capacity
- accessibility of cloud computing
- user‑friendly interfaces
- the ability to integrate insights into everyday workflows
The Turning Point
Yet historically, organisations often under‑used these capabilities because:
- data quality was insufficient
- insights were not operationalised
- business users lacked trust or understanding
- governance was immature
These are the same pitfalls that threaten today’s AI ambitions.
Go Slow to Go Far: A Considered Path to AI
My client chose an AI journey is not a sprint, but sustainable: – a sequence of deliberate steps which also brings the employees on board:
1. First: Fixing the Data – Data Quality:
- Clean, integrate, and govern data
- Modernise legacy systems
- Establish data quality processes and ownership
2. Strengthen BI
- Move from static to dynamic extracts and reporting
- Introduce scenario and simulated reporting
- Review decision processes and where appropriate, embed enhanced and advanced BI into them.
3. Introduce “quiet AI”
- Automated insights
- Smarter forecasts
- Anomaly detection
- Natural‑language interfaces to BI
4. Scale only what works
- Measure impact
- Build trust
- Strengthen governance
- Expand gradually
This approach ensures that the preparation for AI becomes intentional and AI can be a trusted, strategic capability – and more than a collection of experiments.
In Summary
- Data quality and data management are the true foundations of AI.
- BI has a long history—and the lessons are clear: better data leads to better decisions.
- Dynamic and simulated reporting prepare organisations for AI by building data‑driven habits.
- Many AI techniques are not new; they were simply under‑used due to poor data foundations or lack of in-depth understanding.
- A steady, considered approach ensures AI delivers real, lasting value.
I would love to hear your thoughts. I welcome your comments, reflections and/or questions. Whether you are exploring AI opportunities, wanting to improve data quality or contemplating new leadership opportunities, I am here to help.
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