Data & AI for Business Leaders · Track 04
Make AI decisions with confidence
Senior leaders are being asked to evaluate AI investments, approve data strategies, and set AI ambitions, without a clear understanding of what the technology can realistically deliver, what it requires to work, and where it typically fails. The result is misallocated budgets, unrealistic timelines, and data strategies that can't be executed. This programme closes that gap, not by making executives technical, but by making their AI and data decisions more accurate.
The problem
Why AI literacy matters at the leadership level.
The gap between executive AI ambition and technical AI reality is one of the most reliably expensive problems in enterprise data and AI today. It produces a predictable set of outcomes, not because organisations lack talent or resources, but because the people making the strategic decisions are working from a fundamentally inaccurate model of what the technology can do.
01
Buying solutions to problems the data doesn't support
AI initiatives require clean, well-governed, sufficient data. Most enterprise data environments aren't there yet. Leaders who don't understand this greenlight AI projects before the data foundation exists and discover the mismatch after significant spend.
02
Believing vendor demonstrations
AI capabilities shown in vendor demos are routinely not replicable in the client's environment without substantial additional work. Leaders without the ability to evaluate a demo against their own data reality are vulnerable to implementation gaps that emerge at procurement, not at delivery.
03
Misaligning data strategy with business objectives
A data strategy needs to connect platform investment to the specific business decisions it will improve. When leaders don't understand the relationship between platform architecture, data quality, and decision-making capability, they approve strategies that don't close the gap between what the business needs and what the platform delivers.
04
Setting AI ambitions the organisation can't deliver
Executive AI ambitions translate into engineering priorities. When those ambitions are set without understanding feasibility, engineering teams spend months building for objectives that weren't viable from the start or managing leadership expectations rather than building systems.
The outcome
Making better decisions
The measure of this programme is not what participants learn. It is what they decide differently. Three specific decision categories improve after a well-designed AI and data literacy programme for leadership.
AI investment decisions
Leaders who understand what AI realistically requires - data quality, infrastructure, engineering capacity, governance - are less likely to approve unviable initiatives and more likely to sequence investments so the prerequisites for AI exist before it's deployed. The difference is not less ambition. It is more accurate ambition.
Vendor & partner evaluation
AI vendor markets are crowded and the claims are aggressive. Leaders grounded enough to ask the right questionswhat does this require from our data? what does "production-ready" mean here? what's the maintenance overhead after implementation? - reach better procurement decisions faster. They don't need to be technical experts. They need to know which questions reveal the gap between a demo and a deployment.
Data strategy alignment
Most organisations have data strategies that are either too technical for leadership to engage with or too business-general to guide engineering. After this programme, leaders understand the relationship between governance, platform capability, and business outcome closely enough to hold both the strategy and the engineering plan accountable, an alignment rarer, and more valuable, than either alone.
Programme content
What a senior leader needs to know
about data and AI.
Not a technical certification course. It doesn't cover algorithms, code, or infrastructure implementation. It covers the concepts, constraints, and decision frameworks that let a senior leader make informed decisions about data and AI — to evaluate what's proposed, understand what it requires, and judge whether it's realistic.
01
Data in Business
Understand the key data applications and their use cases, with a focus on generative AI.
Data applications
Business use cases
Case studies
What AI and data mean in a business context — the applications that create tangible value, real-world case studies where data investment changed decisions, and a grounded view of generative AI's role alongside existing systems. An introduction to AI as a business tool, filtered through what actually works in practice.
02
Data & AI Mythbusting
What is myth vs. reality among popular data and AI assumptions?
Popular data and AI related assumptions
The most consequential moment in the programme. A structured walk through the claims most commonly believed by senior leaders — which are accurate, which are overstated, and which are simply false. Leaders leave able to evaluate an AI pitch or vendor claim against a calibrated view of what the technology can actually do.
03
Building a Data-Driven Organisation
How to structure the organisation and build the culture to make the most of data?
Building a data team
Hiring for data roles
Creating a data culture
Managing change
Key stakeholders
The organisational design questions that determine whether a data strategy can be executed: how to structure data teams, what good data hiring looks like from a leadership perspective, how to build the cultural conditions for data initiatives to succeed, and which stakeholders must be aligned for projects to move.
04
Building a Data Strategy
How to build a successful data strategy aligned with organisational goals?
Defining a data strategy
Aligning data strategy with organisational goals
The strategic framing for AI and data investment: what a data strategy is (and isn't), how to define one specific enough to guide engineering while remaining comprehensible to business leadership, and how to align platform investment with the outcomes the organisation is actually trying to achieve.
How it's delivered
Shaped around how senior leaders work
Executive audiences don't have a week to spend in a training room. The programme runs in formats that fit into leadership schedules — from a focused half-day briefing to a multi-session engagement that builds literacy over several weeks.
Executive Briefing
A focused, high-density session covering the essential concepts, frameworks, and decision tools a senior leader needs. Structured as a briefing, not a lecture. Interactive — participants bring real questions about their organisation's AI direction and get specific, informed answers. Suitable for a single executive team or C-suite group.
Group:
4–12 executives
Duration:
3–4 hours (half-day)
Leadership Programme
Core
The primary Datapao offering: a 6-hour interactive session covering all four modules, with facilitated group discussions built into each. Begins with a preliminary discovery call to calibrate content to the organisation's data and AI maturity — not delivered from a standard template. Ends with a dedicated Q&A and feedback session. Materials provided digitally afterwards.
Group:
5–15 participants
Duration:
6 hours + pre-session discovery call
Multi-session Series
A structured programme run over several weeks — one session per week, each covering a specific topic area. Allows deeper engagement, application between sessions, and integration with ongoing strategic decisions. Suitable for organisations running a major AI or data platform initiative who want leadership literacy to develop in parallel with the technical programme.
Group:
6–15 participants
Duration:
3–6 sessions over 3–6 weeks
