As 2024 wraps up, it’s worth pausing to reflect on what we learned. Not the vendor narratives or analyst predictions – what actually happened in organisations trying to transform digitally.

This is my perspective from a year of consulting engagements, conference conversations, and watching the industry evolve.

The Year AI Became Operational

The biggest shift: AI moved from experimental to operational.

In 2023, most AI conversations were about pilots, proofs of concept, and exploring possibilities. In 2024, the conversation shifted to production deployment, governance, and operationalisation.

This is significant. Operational deployment brings different challenges than experimentation. Questions change from “can we make it work?” to “how do we make it work reliably, at scale, with appropriate oversight?”

Many organisations discovered they weren’t ready for this shift. Technical proofs of concept don’t prepare you for production reality.

The Hype-Reality Reconciliation

2024 saw a reality check on AI expectations.

Early-year predictions promised transformational productivity gains, automated creative work, and AI agents handling complex business processes. By year-end, the mood was more tempered.

Yes, AI delivers value. But:

• Productivity gains are 10-30%, not 300%
• Creative work still needs humans
• Agents struggle with real-world complexity
• Integration and change management consume most of the effort

This isn’t failure. It’s the normal pattern of technology adoption – inflated expectations followed by realistic assessment. The technology is genuinely useful; the hype was just excessive.

Change Management Finally Got Attention

For years, change management was the thing everyone acknowledged and nobody invested in properly. 2024 felt different.

Several factors drove this:

• High-profile transformation failures blamed on poor adoption
• AI tools requiring different user behaviours, not just new systems
• Recognition that technology adoption is a human problem

The best transformations I saw this year invested 15-20% of budget in change management. They treated adoption as a first-class concern, not an afterthought.

This isn’t universal yet, but the conversation has shifted. That’s progress.

Data Foundations Matter More Than Ever

The “garbage in, garbage out” problem hit home in 2024. AI amplifies data quality issues in ways traditional systems don’t.

Organisations discovered:

• Data they thought was clean wasn’t
• Data they thought existed didn’t
• Data they could access technically they couldn’t access legally
• Integration between data sources was much harder than expected

The winners invested in data foundations before scaling AI. The losers tried to build AI on unstable data and spent months debugging problems.

Vendor Consolidation Accelerated

The AI vendor landscape thinned in 2024. Many early entrants failed or got acquired. The survivors grew stronger.

For enterprises, this created both challenge and opportunity:

• Fewer viable options means less evaluation complexity
• But also less negotiating leverage and more concentration risk
• Platform players (Microsoft, Google, AWS) strengthened their positions

The practical implication: vendor selection is somewhat easier, but dependency risk is higher. Build abstraction layers.

Security and Compliance Caught Up

Early AI adoption often happened faster than security and compliance frameworks could adapt. 2024 saw governance catching up.

Key developments:

• EU AI Act moved toward implementation
• Enterprise security teams developed AI-specific policies
• Data privacy requirements became more explicit
• Audit and explainability requirements tightened

Organisations that had been moving fast discovered they needed to slow down and address governance properly. Those who built governance from the start had easier paths.

The Talent Gap Persisted

Finding people who can do AI well remains hard. The gap between demand and supply didn’t close in 2024.

What helped:

• Improved AI tools that non-experts can use
• Better training and upskilling programs
• More realistic expectations about what skills are needed

What didn’t help:

• Universities not producing graduates fast enough
• Competition from tech giants for senior talent
• AI expertise being poorly defined (everyone claims it now)

The talent constraint will continue into 2025. Building internal capability matters more than hiring.

Integration Remained the Bottleneck

The hardest part of digital transformation isn’t the digital part. It’s making new systems work with old ones.

2024 observations:

• API quality of legacy systems is often terrible
• “Simple” integrations routinely took 3-5x expected time
• Data format inconsistencies created endless cleanup
• Enterprise architecture complexity defeated AI ambitions

Organisations with cleaner architecture had dramatically better outcomes. Those carrying technical debt struggled.

What Changes for 2025

Based on 2024 patterns, my expectations for 2025:

AI becomes table stakes: The question shifts from “should we use AI?” to “how do we use AI better than competitors?”

Governance matures: AI governance frameworks become standard. Organisations without them face increasing risk.

Multi-modal expands: AI working across text, images, audio, and video becomes more common in enterprise applications.

Agent scepticism: After over-promises in 2024, agent technology will need to prove itself. Expect more cautious adoption.

Integration focus: The companies that crack integration – making AI work with existing systems cleanly – will differentiate.

Talent development: Building internal capability becomes as important as hiring. Upskilling investments increase.

The Meta-Lesson

Looking back at 2024, the meta-lesson is familiar: technology is the easy part.

The organisations that succeeded at digital transformation:

• Had clear strategic intent
• Invested in people and change management
• Built solid foundations before scaling
• Maintained realistic expectations
• Adapted when things didn’t go as planned

None of these are technology capabilities. They’re organisational capabilities.

The technology will keep improving. The winners will be organisations that can actually adopt it – that can change their processes, upskill their people, and manage their data.

That’s the lesson of 2024. It’ll likely be the lesson of 2025 too.