Everyone wants to be AI-ready. The promise of GenAI is exciting and hard to ignore. But before you rush to plug AI into your business, it’s worth pausing for a moment and asking a simple question: Is your organisation truly ready?

Trying to deploy AI without the right data foundations is like attempting to run a marathon before you can walk, or building a skyscraper without laying the base. Even in this new era of autonomous agents, one truth hasn’t changed: garbage in still equals garbage out.

The evidence is clear. About 85% of AI projects fail, often due to poor data quality or limited relevant data. Another source estimates that just 30% is making it past the pilot stage. Organizations cite poor data quality, insufficient governance, and fragmentation as core barriers. To top it off, 42% of companies are abandoning most of their AI efforts, up from just 17% last year.

To summarize: Inconsistent, Incomplete, or Siloed Data undermines trust, slows AI adoption, and erodes ROI!

Becoming a data driven organisation is no longer a “someday” ambition. It’s a competitive necessity. Yet many companies still struggle to realise the full potential of their data. Long and complex technology rollouts, disconnected initiatives, and governance bottlenecks often stand in the way.

This is where Databricks changes the equation. By bringing data, analytics, and AI together on a single platform, it allows organisations to move from fragmented, siloed systems to a governed, AI-ready lakehouse. The result is measurable business impact delivered in weeks, not years.

I have worked with organisations that spent significant sums chasing AI hype before building the right data capabilities and watched those efforts fade. The good news is that this can be avoided. With the right approach, you can build a data foundation that not only supports AI but ensures it delivers real, lasting value.

Here are five practical steps( not exclusive!) to build a solid data foundation and accelerate your journey with Databricks:

Step 1: Align vision to outcomes

Success starts with clarity. Define the business outcomes you want to achieve before deciding what technology to implement.

• Identify two or three high-impact use cases that can be delivered within 90 days
• Link them directly to measurable KPIs such as revenue growth, cost reduction, risk mitigation, or compliance improvements
• Focus on outcomes that build momentum and win early support from stakeholders

Data maturity check: At this stage, ask yourself: Do we even have consistent, trusted data to support these use cases? If not, part of the first sprint should include profiling current data assets and identifying gaps in quality or accessibility.

Step 2: Secure executive sponsorship

Change moves as fast as leadership allows. Without senior backing, even the best-designed programme can stall.

Identify a C-level champion who understands the role data plays in achieving strategic goals

• Build data-driven decision-making into leadership scorecards
• Engage executives early so they can advocate for resources and drive adoption across the organisation

Data maturity check: Is data seen as a strategic asset in your organisation, or just an IT concern? If leadership doesn’t yet understand the business value of data, part of your role is to educate and demonstrate where it directly drives outcomes.

Step 3: Deliver fast and prove ROI

Big-bang transformations rarely work. Small, well-executed wins will prove the value of the platform and unlock buy-in for larger initiatives.

Set up your first Databricks workspace and Unity Catalog in days

• Ingest and transform a high-value dataset using Delta Live Tables (Lakrhouse Declarative Pipelines)
• Put actionable insights in the hands of business users within a single sprint

Data maturity check: Can your teams already self-serve simple insights from data, or are they dependent on IT for every report? Delivering value here isn’t just about pipelines. It is about empowering analysts and business users to access, trust, and act on the data.

Step 4: Bake in governance from day one

Governance is often seen as a blocker, but it can be the accelerator that keeps you moving quickly and safely.

Use Unity Catalog for centralised access control, data lineage, and audit

• Apply consistent tagging and classification to manage costs and meet compliance requirements
• Build governance into your delivery process rather than treating it as a separate phase

Data maturity check: Are there already data ownership roles in place (data stewards, product owners, governance councils), or does everything sit with IT? Organisations with low data maturity often have no clear accountability, so building these roles into your operating model is just as important as setting up the technology.

Step 5: Build for scale, not just today

Think beyond your first use case. Your goal is to create a repeatable framework that grows with your organisation.

Develop reusable ingestion and transformation templates

• Implement CI/CD with Databricks Asset Bundles to promote jobs and models across environments automatically
• Optimise compute usage through job clusters, serverless options, and cost-tracking tags
• Plan for multi-region or multi-cloud expansion if your strategy requires it

Data maturity check: Is your organisation ready to scale both technically and culturally? Scaling isn’t just about pipelines, it is about whether teams outside IT are adopting data into their decision-making, whether processes are repeatable, and whether governance is keeping pace with innovation.

Final thoughts

The journey to becoming a data-driven organisation isn’t about big-bang transformations. It is about delivering value early, learning quickly, and scaling effectively.

Databricks provides the technology foundation, but success depends on how you approach the strategy, governance, and delivery.