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31 Mar 2025

Cultivating a culture of success: 7 key takeaways in AI adoption

This blog has been authored by the Innovation subgroup part of the Local Public Services Committee.

Introduction

AI adoption is no longer a distant concept—it’s happening now. From streamlining workfl ows to improving risk assessments and service delivery, AI is already reshaping how public sector organisations operate. But success with AI requires more than just technology—it takes leadership, transparency, and trust.

The Local Public Services Committee team has worked closely with public sector organisations on AI innovation for many years—across local authorities, healthcare providers, and law enforcement. Based on that experience, here are seven key lessons to help ensure AI delivers impact responsibly and sustainably.

1. Define the problem: Set vision, ambition, and governance

AI adoption isn’t just about tools—it’s about leadership. Executive teams must foster a culture of innovation and control, ensuring AI aligns with strategic goals.

Key insights (Source: Boston Consulting Group):

  • 87% of AI initiatives fail.
  • 70% of AI issues are people and process-related; only 10% stem from algorithm problems.
  • 74% of companies struggle to scale AI initiatives effectively.

To succeed, leaders must:

  • Understand AI’s capabilities today and where it’s heading in 2, 5, and 10 years.
  • Link AI directly to measurable outcomes—efficiency, quality, and public value.
  • Use relatable examples (e.g. automating permit processing) to gain initial internal support.

2. Understand current ways of working

Before deploying AI, examine existing workfl ows, decision-making gaps, and biases. Many inefficiencies can be addressed through better data integration and reporting—long before AI is needed.

Example: A local authority reduced delays in housing eligibility decisions by connecting fi ve disparate systems—without using AI. Foundational data work matters.

Lesson:

  • Don’t start with AI—start with understanding and improving what’s already in place.
  • AI is not a fix for fragmented or inefficient processes.

3. Invest in the right tools, skills, and data quality

AI implementation depends on more than having the latest tools—it requires strong data governance, digital skills, and transparent systems.

One public sector challenge is the "black box" perception. If staff don’t understand how AI systems make decisions, trust breaks down.

Practical steps:

  • Improve data governance and interoperability.
  • Build internal data literacy—not just technical skills.
  • Prioritise explainable AI that makes recommendations clear and understandable.

Example: In healthcare, AI models prioritising patient follow-ups must clearly show clinicians why certain patients were flagged.

4. Involve the organisation: Build trust through collaboration

AI is not just an IT project—it impacts workflows, culture, and service delivery. Successful adoption depends on engaging the whole organisation.

Strategies for success:

  • Involve frontline users early in planning and testing.
  • Identify and support "AI champions" across departments.
  • Run workshops to demystify AI and create shared understanding.

Insight: AI projects fail when they’re done to people, not with them. Buy-in must be earned.

5. Model Selection, Ethics, and Pilot Design

Not all AI projects are high-risk. Start with manageable, low-stakes use cases that still deliver value.

Start with:

  • Automating document summaries or triage processes.
  • Pilots in narrow, low-impact environments.
  • Iterative testing with clear metrics for success.

Example: A local council used a generative language model to help draft job descriptions—saving hours of work and building confi dence in AI’s role.

Lesson: Match your ambition to the operational risk and impact.

6. Automation vs. augmentation: Find the right balance

Not every task should be automated. In areas like social care, safeguarding, or criminal justice, human oversight is essential.

Framework:

202503-LPS-blog-pic1.jpg

Recommendation:

  • Automate low-risk tasks.
  • Use AI to support—not replace—critical decisions.

7. Governance, transparency, and ethical oversight

Strong AI governance is about more than compliance—it’s about trust, assurance, and accountability.

AI assurance checklist:

  • Define an organisational AI strategy and risk framework.
  • Create ethics and data assurance boards.
  • Conduct regular audits of AI models.
  • Engage external validators (e.g. academia, civil society).

Lesson: AI without governance is a liability. AI with transparent oversight builds public confidence.

Final Thoughts

Adopting AI in the public sector brings real opportunities—and real responsibilities. To make it work:

  • Set a clear vision tied to real outcomes
  • Fix foundational data and workflows first
  • Focus on transparency and trust
  • Involve the organisation throughout
  • Choose use cases aligned to capacity and risk appetite

The future of AI in public services isn’t about hype—it’s about practical, trusted applications that improve lives.

Let’s make it work—together.

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