Change management for AI adaptation in public sector
Embracing AI Adaptation in the Public Sector
Welcome to the second part of our series on public sector change management!
This time, we’re tackling a subject that can be equal parts exciting and intimidating—adapting to Artificial Intelligence (AI). But that’s where we come in! From the first spark of inspiration to full-blown implementation, this guide is your roadmap to making AI work for your organisation. Along the way, we’ll jump into practical strategies, how to dodge the pitfalls, and touch on the all-important human side of AI integration.
Let’s begin!
Why AI Matters in the Public Sector
AI isn’t just a shiny new toy for tech enthusiasts; like most other areas, it’s poised to transform the public sector at its core. From streamlining service delivery to enhancing decision-making, AI’s potential to add value across operations is enormous.
By 2030, AI is expected to pump over $20 trillion into the global economy. For governments, AI can be the secret weapon behind smoother resource management, sharper efficiency, and public services that actually impress. Think of it as a toolkit that combines computing power, device connectivity, and clever data analysis.
For example: AI-enabled chatbots resolving citizen inquiries 24/7, predictive analytics helping to prevent traffic congestion, or machine learning algorithms identifying tax fraud before it happens.
Whether it’s predicting outcomes, decoding languages, or spotting patterns humans might miss, these systems are like overachievers that never take a day off. With machine learning, they’ll only get better at their jobs over time.
The public sector has been slow to adopt AI compared to its private-sector counterpart (governments and councils aren’t known for their speed), but that’s changing fast. With mature products breaking through the hype, the time to act is now. Yet, navigating this new terrain requires careful planning and a solid foundation.
Finding the Right Use Cases
AI isn’t a magical, all-encompassing solution. It works best when applied to the right problems—think laser-focused, not scattershot. For example:
• Predictive modelling for disaster response.
• AI-assisted document analysis for faster policy review.
• Enhanced fraud detection in public welfare programs.
The key is to start small. Nail a few high-impact areas, and you’ll build momentum (and confidence) for more ambitious projects.
Levels of Ease in Adopting New AI Software
Adopting AI ranges from plug-and-play simplicity to large-scale, organisation-wide transformations:
Easy:
• Work support tools:
- Examples: ChatGPT, Jasper AI, Microsoft Copilot.
- Requirements: Basic access to the tool, minimal training, and an internet connection.
- Why it’s easy: These tools are intuitive, require no complex integration, and can be up and running in minutes.
• Meeting summarisation software:
- Examples: Otter.ai, Gong.io, Fireflies.ai.
- Requirements: Integration with video conferencing platforms like Zoom or Teams and a quick setup process.
- Why it’s easy: With simple plug-ins and cloud storage, these tools seamlessly generate accurate meeting summaries, analytics, transcripts, and constructive feedback.
Medium:
• Automation for routine tasks:
- Examples: Zapier, UiPath, Microsoft Power Automate.
- Requirements: A clear understanding of workflows, some training, and configuration of automation rules.
- Why it’s medium: While these tools simplify repetitive work, they need careful setup and some team training to make sure they’re effective.
• Customer-facing chatbots:
- Examples: Intercom, Freshdesk, Ada.
- Requirements: Customisation for your organisation’s needs, training on specific datasets, and some IT support.
- Why it’s medium: Chatbots involve creating scripts and training the AI to handle a variety of queries, which takes time and effort but delivers great ROI.
Hard:
• AI in large-scale decision-making:
- Examples: IBM Watson, Palantir Foundry, SAS AI.
- Requirements: Organisation-wide integration, access to high-quality data, and collaboration between AI teams and leadership.
- Why it’s hard: Embedding AI into core decision-making processes involves heavy customisation, compliance checks, and cultural adaptation.
• Overhauling documentation workflows:
- Examples: DocuSign Insight, HyperScience, Tungsten Automation.
- Requirements: Migration from legacy systems, re-engineering workflows, and extensive staff training.
- Why it’s hard: These projects require a significant shift in processes and a high level of cross-departmental coordination.
By categorising AI tools based on ease of adoption, organisations can better plan their journey, starting with quick wins and scaling up to more transformative changes.
Step One: Build or Source AI Products?
When it comes to AI, the first decision organisations face is whether to build proprietary solutions or source them externally. Think of it as deciding between cooking up a gourmet meal from scratch or grabbing a delicious ready-made dish. Each option has its perks and pitfalls, and getting it right means weighing up costs, time, data, risks, and how it all fits with your operations.
The Case for Building
This approach lets you design AI solutions that fit your organisation like a glove. Take, for example, a public transport system whipping up its own AI tool to predict peak traffic patterns. Sure, it would be fully customised and fancy, but it’s also a serious investment in time, money, and brainpower.
Building isn’t cheap or easy, and it’s more than just the initial development; you’ve got to keep your AI baby fed and healthy with ongoing maintenance and updates. And then there’s the big question: do you have the ingredients—the data, the expertise, the infrastructure—to make it happen? Building cutting-edge AI tools can cost millions. Unless you’ve got some serious cash to work with, it might not be worth it.
Oh, and don’t forget about the rulebook. Regulations like the EU AI Act mean you’ll need to stay transparent, unbiased, and fully compliant. If you’re not ready for the extra scrutiny and potential penalties, you might end up biting off more than you can chew.
The Case for Sourcing
This approach is quicker, typically cheaper, and you get to skip the whole trial-and-error process. With the AI market exploding—and we’re talking US$305.90 billion in 2024—you’re spoiled for choice. Low-code platforms, AI-as-a-service, plug-in components, and Application Programmable Interfaces (APIs). it’s a buffet of possibilities.
But before you dig in, do your homework. Is the product mature? Has it proven itself in your kind of environment? Look for case studies, benchmarks, and success stories to make sure you’re not buying a shiny dud. And hey, sourcing can save you from regulatory headaches since vendors usually handle compliance for their products. That means fewer late nights reading up on General Data Protection Regulation (GDPR).
Of course, sourcing isn’t necessarily foolproof. What if your vendor suddenly changes the recipe? Vendor lock-in is a real risk, so you’ve got to make sure there’s a backup plan. And don’t overlook data privacy—sharing sensitive info with an external provider can get tricky, especially when legal agreements start piling up.
A Balanced Approach
So, how do you choose? Compare the total costs, consider how it fits with your current setup, and don’t be afraid to run some numbers. Maybe building is the way to go for a truly unique solution, or maybe sourcing is the smarter play for speed and simplicity.
At the end of the day, it’s all about finding the right fit for your organisation. With the right questions and a clear view of your priorities, you can make a choice that serves up success—no matter which path you take.
Preparing Your Organisation for AI
Adopting AI is not just a technical challenge—it’s a cultural one. Building organisational readiness requires a blend of technical, strategic, and human-centred approaches.
Training and Upskilling
AI tools are only as good as the people using them. Public sector employees need to be equipped with the skills to:
• Use AI tools effectively.
• Contribute to building and customising AI systems.
• Maintain and improve AI tools over time.
Investment in training programs, workshops, and partnerships with educational institutions is key. Consider adding machine learning talents or data scientists to your team to bridge skill gaps - because let’s face it, every team could use a data wizard or two.
Common AI Worries You Should Not Worry About
1. AI Tools’ Incompetency
AI tools are, at their core, just that—tools. When they don’t perform as expected, it’s often not the technology itself but the use case or the readiness of the organisation. Let’s face it, even the best hammer doesn’t work if you’re trying to use it as a screwdriver. Take chatbots, for example—what happens if they start hallucinating answers? That’s a matter of defining structured responses or setting clear parameters around their capabilities. When you’re not attempting to build a futuristic AGI (Artificial General Intelligence), rest assured, there will always be a solution. Focus on defining the use case, just like you would with any other software.
2. AI Degradation
Some worry that AI will get "dumber" as it receives poor data inputs over time. But unless you're building a language model from scratch (which, let’s be honest, isn’t a typical 2025 concern), there’s no need to stress. With proper data governance and clear use cases, the chances of AI tools "degrading" are extremely low. It's all about using good, quality data and keeping your goals well-defined. AI, after all, is just a turbocharged version of digital transformation—start small, focus on customer needs, and build from there.
Aligning Leadership with the Greater Vision
Leaders, this is your moment. AI adoption needs visionaries who can inspire their teams, champion the change, and connect the dots between AI and broader organisational goals. Clear communication and a compelling vision can turn sceptics into believers.
Tackling AI-Specific Risks
No groundbreaking technology comes without a few bumps in the road. The trick is to face these challenges head-on.
Data Usage and Policy Compliance
Where is your data stored? If you’re partnering with vendors, how do they handle sensitive information? Public sector organisations are under intense scrutiny when it comes to data privacy and regulatory compliance. Ensuring AI solutions meet these standards is non-negotiable.
Budgetary Constraints
AI might be more affordable these days, but poor planning can still sink your budget quickly. Do the math before diving in and keep an eye on the long-term costs. Develop a detailed cost-benefit analysis before committing to any project.
Calibrate and Celebrate the Human-AI Partnership
As AI takes over routine tasks, what happens to the human workforce? Ensuring that automation enhances rather than erodes human value is a delicate balancing act. Engage employees early, integrate the AI thoughtfully, and focus on meaningful work redistribution.
Legacy Systems: The Elephant in the Room
The public sector is notorious for clinging to legacy systems - the faithful old dogs of the public sector. Reliable? They can be. But innovative? Not so much. While these systems often underpin critical operations, they can also stifle innovation.
But, adopting AI doesn’t always mean starting from scratch - you can upgrade without overhauling. Incremental upgrades can extend the life of existing systems while introducing AI capabilities. For example, integrating predictive analytics into an old transportation system can yield significant benefits without a full rebuild.
Outdated systems can be inefficient, vulnerable, and at times downright useless. Take a holistic view to make sure yesterday’s tech doesn’t hamstring tomorrow’s progress.
Real-World Lessons in Public Sector AI
Success Story: Singapore’s Smart Nation Initiative
Singapore has embraced AI as a cornerstone of its Smart Nation initiative. From AI-powered traffic management systems to digital identity solutions, the government has leveraged technology to improve citizen services. Their success stems from a strong governance framework and a commitment to citizen-centric design.
Pitfall: AI Bias in Predictive Policing
In the US and parts of Asia, predictive policing programs faced backlash due to algorithmic biases that disproportionately targeted certain communities. The lesson here? Transparency, oversight, and ethics must be baked into AI projects from the start.
Building Public Trust in the Age of AI
As AI becomes more omnipresent, one thing has become crystal clear—building public trust in AI policies is paramount. There’s a fair amount of scepticism surrounding the role of AI in people's lives. And rightly so—AI can feel like a big, impersonal force in a world already full of uncertainties. So, how can public sector organisations work to stay inclusive while adopting AI technologies?
Transparency
Trust begins with being upfront. Let people in on how AI works, where it gets its data, and how it stays fair. No one likes a mystery, so make the process clear and simple to understand.
Inclusive Design and Ethics
AI should be for everyone. Make sure your data and design are diverse, and bring in voices from all corners of society. A fair AI is one that reflects the many, not just the few.
Clear Communication of AI Benefits
Let’s show the positive side! Whether it’s faster services, smarter decisions, or saving time, AI should be seen as a helpful tool, not something to fear. Let people know how it makes life better.
Accountability and Oversight
Who’s in charge? Having solid checks and balances in place will give people peace of mind. Regular audits and open feedback loops ensure AI stays on track and responds to the public’s needs.
The Bottom Line
If we want people to trust AI, it’s all about being transparent, inclusive, clear, and accountable. Get these right, and we’ll all feel more confident about welcoming AI into our lives.
The Takeaway
AI adaptation is all about making the public sector smarter, not just faster. The key isn't in jumping straight to complex systems, but in picking the right projects, testing things out, and letting AI grow with your needs. This isn’t about keeping up with tech for tech's sake, but about creating solutions that actually work in the real world.
Next up, we’ll dig into the people side of the equation. AI’s success depends just as much on the team, leadership, and culture as it does on the algorithms. So, get ready for the next chapter, where we get into the human factors that enable truly successful AI adaptation, and how to support people through the process.
Founders’ Words
At Alknoma, we don’t just talk about AI—we make it practical. Our goal is simple: to help organisations seamlessly adopt AI and enhance their decision-making processes over time. With our flexible, multi-tier system, businesses can integrate AI at their own pace and tackle real-world challenges as they evolve.
Alknoma is a collaborative, interactive experience that lets you assess your strategy, validate your planning, and continually improve—while the AI adapts to your feedback.
When you click ‘Start AI Conversation,’ you’re engaging with a dynamic knowledge base built on real experience. Use it to refine your change management, fine-tune your approach, or simply validate your thoughts.
Alknoma empowers you to make smarter decisions, faster.
