The Governance Gap and Why Organisations Still Struggle to Operationalise Policy

Most organisations don’t have a policy problem, they have an operationalisation problem. Policies exist, but they’re not enforced, monitored, or embedded into workflows. Governance becomes a theoretical exercise rather than a practical one. Teams know what they should do, but the mechanisms to ensure they actually do it are missing.

This gap often emerges because governance is treated as documentation rather than behaviour. Policies are written in isolation, disconnected from the systems and processes they’re meant to govern. Without automation, policies rely on human discipline, and human discipline is inconsistent at best.

Microsoft Purview helps close this gap by making policy enforcement automatic and auditable. When classification, lineage, and access controls are integrated, policies become part of the system rather than an external expectation. This shifts governance from aspiration to execution.

But technology alone isn’t enough. Organisations need stewardship, accountability, and a culture that treats governance as part of delivery, not a hurdle to clear. Operationalising policy requires alignment across teams, clarity of ownership, and a commitment to continuous improvement.

The governance gap is not inevitable. It’s a symptom of misalignment. When organisations align policy, technology, and behaviour, governance becomes a strategic enabler rather than a compliance burden.

AI Is Making Us Dumber, Part III: When Models Learn Faster Than Organisations Do

We have reached a point where models can learn faster than organisations can adapt. This creates a dangerous asymmetry: the technology evolves, but the governance, culture, and literacy lag behind. The result is a widening gap between capability and control. Organisations deploy increasingly powerful models without fully understanding their behaviour, limitations, or risks.

This gap is not caused by technology, it is caused by organisational inertia. Many teams still rely on outdated governance processes that cannot keep pace with continuous learning systems. Policies are static, reviews are infrequent, and oversight is reactive. Meanwhile, models evolve with every new dataset, every retraining cycle, and every shift in user behaviour.

The solution is not to slow the models  but it is to accelerate organisational learning. Governance must become continuous, adaptive, and embedded into operational workflows. This means real‑time monitoring, dynamic policies, and stewardship that evolves alongside the data. It also means investing in literacy so that teams understand not just how to use AI, but how to question it.

When organisations learn as fast as their models, AI becomes a strategic advantage. When they don’t, AI becomes a liability. The choice is not technological, it is cultural.

I spoke on the topic at the  Data Toboggan Winter Edition in a session entitled 'Data Literacy: The Human Advantage in an AI World.'

SQL Server’s Next Chapter: What the New Release Signals for Enterprise Data Estates

The latest SQL Server release marked a significant shift in Microsoft’s data platform strategy. Rather than positioning SQL Server as a standalone engine, the new version embraces its role within a broader ecosystem, one that includes Fabric, Purview, and Azure AI. This is not just a technical update but a strategic repositioning that acknowledges how modern data estates actually operate. SQL Server is no longer the centre of gravity. It is a critical component in a distributed, interconnected architecture.

One of the most meaningful changes is the deeper integration with governance and observability tooling. SQL Server has always been strong on performance and reliability, but governance was often something organisations had to bolt on themselves. The new release changes that. Enhanced metadata exposure, improved auditing, and richer lineage signals mean SQL Server can now participate more fully in enterprise‑wide governance frameworks.

Hybrid workloads also receive significant attention. Many organisations still run mission‑critical workloads on‑premises while exploring cloud‑native architectures. The new SQL Server release acknowledges this reality by improving consistency across environments. This reduces friction for teams managing mixed estates and makes it easier to apply governance and security policies uniformly.

For data leaders, the message is clear, SQL Server is evolving to support modern architectures rather than compete with them. It’s becoming more transparent, more governable, and more aligned with the needs of organisations navigating the AI era. SQL Server’s next chapter is one built on integration, not isolation.

Image Source: Microsoft

Data Toboggan Winter Edition 2026

It is that time of year again when Data Toboggan is running another 12 hour conference with 3 tracks with speakers from around the world. There are some amazing sessions to learn from. The conference is free to attend as usual. 

I am speaking on something of interest and topical in my lightning talk in The Chalet on Data Literacy: The Human Advantage in an AI World.

AI is accelerating decision‑making across organisations, but it’s also accelerating how quickly mistakes can scale. This session explores how data literacy keeps humans in the loop, prevents over‑reliance on AI, and strengthens judgment, context, and critical thinking. Attendees will see real examples of AI hallucinations, learn how provenance and triangulation protect against bad outputs, and understand why cognitive skills weaken when tasks are automated. They will leave with a practical checklist for questioning AI outputs, a clear view of the risks of low data literacy, and a framework for building teams that use AI responsibly, confidently, and intelligently.

We have our usual Piste Maps with the agenda.

World Economic Forum 2026 in Davos Global Council for Responsible AI

At the 56th World Economic Forum 2026 in Davos between 19–23 January 2026 , the Global Council for Responsible AI officially unveiled GRAICE™ (Global Responsible AI Compliance & Ethics). It is designed as humanity’s operating system for AI. Introduced to global leaders and policymakers, GRAICE moves Responsible AI from principle to practice, integrating ethics, governance, compliance, and human-centric design into a unified, scalable framework. 

The framework is an integrated system rather than a collection of policies that are simple and repeatable.

• Foundational values established non-negotiable ethical and human centred boundaries
• Seven pillars translate values into operational requirements
• Assurance tears verify that requirements are met with evidence
• Governance structures assign accountability and decision authority

 The six foundational grounded values are  

• Human dignity and autonomy
• Accountability and governance 
• Fairness and justice
• Transparency an explain ability
• Reliability and security
• Inclusivity and social benefits

And the seven pillars for responsible AI define what responsibly I must achieve in practise

• Ethical leadership
• purpose driven innovation
• Human centric use
• responsible implementation
• AI literacy and workforce readiness
• Data governance and integrity

AI Is Making Us Dumber, Part II: When Automation Replaces Understanding

The second wave of AI dependency is more subtle than the first. It is not about hallucinations or bias, it’ is about the erosion of organisational understanding. As AI tools become more capable, teams increasingly rely on them to summarise, interpret, and decide. Over time, this creates a dangerous dynamic: people stop interrogating the underlying data and start accepting outputs at face value.

This shift is particularly risky in environments where data quality is inconsistent or poorly governed. When teams don’t understand the lineage, context, or limitations of the data feeding their models, they lose the ability to challenge results. AI becomes a black box, and decisions become detached from reality.

Governance is the antidote. By enforcing lineage, quality checks, and human‑in‑the‑loop review, organisations ensure that automation enhances rather than replaces understanding. Governance creates the conditions for informed oversight, not blind trust.

The goal is not to reduce AI usage, it is to elevate human capability alongside it. AI should accelerate insight, not diminish expertise. When governance is strong, AI becomes a partner. When governance is weak, AI becomes a crutch.

The Governance Reset: Five Data Strategy Predictions for 2026

Every January brings a wave of predictions, but 2026 feels different. The pace of change in data and AI has outstripped the pace of organisational adaptation, and leaders are beginning to recognise that their existing strategies are no longer fit for purpose. The old model of annual planning cycles, static governance frameworks, and siloed ownership simply cannot keep up with the velocity of modern data estates. This year will force a reset.

Continuous Governance

The first major shift will be toward continuous governance. Organisations can no longer rely on periodic reviews or manual controls. Governance must operate at the speed of data creation, not the speed of committee meetings. Automated lineage, dynamic classification, and policy‑driven access will become baseline expectations rather than advanced capabilities.

Clarity in areas of data Management

Second, we’ll see a rise in data contracts as a mechanism for aligning producers and consumers. Contracts bring clarity to ownership, quality expectations, and change management. They also reduce friction between teams by making responsibilities explicit. This is governance embedded into delivery, not bolted on afterward.

AI‑driven Metadata Enrichment

Third, AI‑driven metadata enrichment will become essential. Manual documentation has never scaled, and 2026 will be the year organisations finally stop pretending it can. Automated tagging, relationship inference, and behavioural metadata will fill the gaps humans never had time to address.

Cross‑functional Stewardship will Mature

Fourth, cross‑functional stewardship will mature. Governance will no longer sit with a single team; it will be distributed across product, engineering, analytics, and compliance. This shift will require cultural change, but it’s the only sustainable model.

Embrace adaptive policies

Finally, organisations will embrace adaptive policies and rules that adjust based on context, sensitivity, and risk. Static rules cannot govern dynamic estates. Adaptive governance will become the new normal.