Used correctly, AI can transform your organisation’s use of data – and The Data & AI Readiness Playbook gives you a seven-stage process for doing this successfully. However, user adoption can make the difference between success and failure for an otherwise well-thought through initiative. Here we look at the issues you might face and how you can overcome them.
As technologists, we want to believe that it’s our choices and actions that make the real difference. But it’s not tools that transform business, it’s people. Survey after survey shows that it’s user adoption that is the real key to the success of your transformation project“, explains Dan Knott, Cloud Direct Data & AI Practice Lead.
Motivating employees to use new tools effectively, and measuring and managing this, is the difference between costly failure and transformative success. But before we look at how you do this, let’s consider why this might be necessary.
Why bother driving adoption?
As humans, we are essentially creatures of habit. Neurologically, our brains are wired to form habits, and they account for roughly 40-45% of our daily actions.
A new process or technology often requires us to change our behaviour – and it may seem easier to carry on as before. So, there’s also a level of inherent resistance to change that needs to be overcome.
However, as humans we also have a remarkable capacity for learning and adaptation, but we need to see the benefit of change. Subconsciously we’re asking ourselves ‘what’s in it for me?’
Where AI is involved there’s often an added mix of practical, emotional, and ethical concerns that also need resolving. Will it take my job? Will AI make decisions for me? Can I trust it? Will I be held accountable for AI errors? Will it be used to monitor me?
Unless and until people know otherwise, these are valid concerns over what AI means for employees’ roles, job security, and daily experience.
Tackling AI anxiety and engaging employees in change requires much more than good comms. It requires empathy, clear communication, visible leadership, and a people-first approach.
Initiatives with strong adoption and change management processes are six times more likely to succeed.
According to a survey by change management specialists Prosi.
How to maximise adoption
If we’re being honest, AI is a big shift and for many employees it will be a big deal. So, let’s treat it like one. Here are seven key elements of driving a successful adoption.
- Build Change Management in from the start
- Establish a formal change management programme from day one. Change Champions will play a vital role as advocates for changes and as providers of peer-to-peer support. Alongside this you need a communication strategy that clearly explains ‘why’ change is occurring as well as the ‘what’ and ‘how’.
- Acknowledge and address user concerns
- Openly acknowledge anxiety and recognise that resistance is natural and expected. Different groups of employees will have differing needs, and you may need to create safe spaces for concerns to be voiced. Address common objections head-on with clear explanations and evidence. Often resistance stems from job fears, so be transparent about how roles will evolve rather than disappear.
- Explain the benefits
- Users like clear, tangible benefits. While it’s important to explain how AI is important to the business, answering ‘What’s in it for me?’ is more important in driving adoption. Position AI as an enabler (rather than a threat) and emphasise how AI can remove tedious tasks and support better decisions. Help people to see how it augments human capability, rather than replacing it. Ideally, tailor the messaging and provide use cases that are relatable to different types of roles.
- Involve employees early and often
- Inclusion turns resistance into ownership, so invite input into how AI can be used in specific departments. Directly ask: “How and where could AI help you most?” Regularly communicate progress and share real examples of how employees are using AI to make work easier or more rewarding. Since personal stories build trust far faster than tech demos, personalise examples to celebrate people, and not just the tech. For example, ‘How AI is helping Julie to produce monthly reports in half the time’.
- Provide practical, role-relevant training
- Focus on hands-on, scenario-based training, rather than theory. Deliver the training in as many different bite-sized formats as possible to suit different learning styles: lunch-and-learns, online tutorials, quick reference guides, drop-in sessions. As a rule, when people can see the benefit, they want to know more.
- Lead by example
- If leaders are visibly seen to be using AI, others quickly tend to follow. Not only does leaders’ use signal AI’s importance, but many of us learn working behaviours and practices from those above us. Nothing builds adoption like seeing your boss on the tools.
- Measure and iterate
- Success in user adoption requires it to be seen as an ongoing journey rather than a one-time event. Track quantitatively (usage rates, task completion times, errors) and qualitatively (user satisfaction and feedback) and use this to identify adoption barriers and continuously improve.
Although this may seem like a lot of extra work, adoption is likely to be the single biggest determinant of project success. A good partner will help you to engage and involve internal comms, HR, change management and other colleagues that will help you achieve all this.
Next steps
Download a copy of The Data & AI Readiness Playbook to learn more. You’ll discover how others are preparing for and using AI, and a seven-step process to unlock the value of your business data.
Data quality is fundamental, with the cost of bad data running into millions for UK businesses. Here, we look at how and why poor data is costing your organisation dearly, and the practical steps you can take to improve data quality.
Bad data is costly. Research by analysts Gartner puts the average cost to organisations at a staggering $12.9 million a year – around £10 million. Whether that figure seems high or low to you will depend on a variety of organisation-specific factors, but the key point is that poor data is costly.
As we become more AI-reliant, that cost is likely to increase. And for most, it’s completely hidden.
Six ways data deficiencies could be costing your organisation
Poor data typically impacts organisations through operational waste, revenue leakage, and strategic shortcomings.
- Lost revenue
Data inaccuracy can result in poor decisions, causing lost sales, poor targeting, and underachieving campaigns. Old or incomplete customer data can mean missed upsell or cross-sell opportunities. - Wasted time
Employees waste hours finding, verifying, or correcting information. - Increased risk
Data errors cause GDPR and regulatory breaches, possibly incurring fines and certainly requiring time-consuming corrective action. Risk models that use poor data will fail to flag preventable issues. - Negative customer experience
Incorrect contact details, preferences, or histories lead to irrelevant messaging or service errors, which undermine customer confidence, damage brand reputation, and add to customer churn. - Operational inefficiencies
Errors in billing, shipping, or inventory management cause rework, returns, and delays – all of which carry a cost. - Strategic failings
Decisions based on flawed insights can misallocate investment, degrade valid opportunities, ignore key risks, and cause growth strategies to fail.
Why data matters more than ever
As John Doublard, CTO at Oak Group and one of the industry contributors to ‘The Data & AI Readiness Playbook’, notes: “The future is data-driven.”
“AI can only drive genuine business value when it addresses real business issues AND is fuelled by valid data,” explains Cloud Direct’s Data & AI Practice Lead Dan Knott.
Understanding the limitations of your data, which parts need improving, and how to make those improvements is key to this.
Eight practical steps for data quality improvement
Improving data quality isn’t just the responsibility of IT – although there are technical actions that will help – but something for the whole organisation.
‘When you’re about to invest heavily in becoming a data-driven business, you need to make sure that the data you’re working with is going to provide for you and not cost you more’.
- Define what ‘good’ means
If you’re working with bad data, you’ll get bad outcomes, so establish clear data quality dimensions. The Data & AI Readiness Playbook outlines the importance of accuracy, completeness, consistency, validity, uniqueness and timeliness. Add to this any business-specific requirements, such as capturing an accurate address or company registration number for credit checking. - Perform a data audit
Identify your key data assets, such as CRM/ERP and finance data, and assess current data quality. Prioritise high-impact areas and make use of tools to help you highlight errors, duplicates, gaps, or inconsistencies. - Fix existing issues
Involve data owners (see below) with data cleansing. The objective is to de-duplicate records, fill in missing fields, standardise formats, and identify old and obsolete data for archiving and removal. Tools like Microsoft Purview and Power Query can helpfully automate some of the work. - Embed good data governance practices
Appoint data owners and data stewards who will be responsible for data quality in key areas. Involve them in creating policies for data entry, usage, retention, and updates, and make use of Microsoft Purview, or similar, to enforce policies and manage access, lineage, and classification. - Educate and engage users
Data quality needs to be seen as a shared responsibility, and not IT’s problem alone. Run awareness campaigns or training to help users understand their role in maintaining good data. Within this, celebrate success by showing how clean, reliable data is enabling better decisions and results, and continue running campaigns until data quality is embedded in the organisation’s culture. - Improve data entry at the source
You don’t want to be continually drawn into corrective action, so employ measures to improve data entry at the source. Add validation rules, drop-downs, and formatting controls where data is input, and ensure that departmental training covers the importance of entering accurate data. Wherever possible, automate data capture utilising integrations, forms with logic, and barcode scanners. Improving data quality requires both technical and cultural change. Start with what matters most, fix the root causes, and embed quality into everyday workflows. Over time, better data leads to faster decisions, happier customers, and lower costs.
Next Steps
Download a copy of The Data & AI Readiness Playbook to learn more. You’ll discover how others are preparing for and using AI, and a seven-step process to unlock the value of your business data.
Written by Cloud Direct CTO Paul Sells
In today’s fast-paced digital economy, artificial intelligence (AI) is a business imperative. From automating routine tasks to uncovering deep insights from data, AI is transforming how business operate, compete, and grow.
But here’s the challenge: while many companies are eager to embrace AI, few have a clear strategy for doing so effectively and sustainably. That’s where an AI Centre of Excellence (AI CoE) comes in.
What is an AI Centre of Excellence?
An AI CoE is a dedicated team or function within a business that leads and governs AI initiatives across departments. It’s an internal AI consultancy, with the aim of bringing together the right people, processes, and technologies to ensure your AI investments deliver real business value.
Rather than scattering AI efforts across siloed teams, an AI CoE provides a centralised hub of expertise, best practices, and reusable assets. It helps your organisation move from isolated experiments to enterprise-wide impact.
Why Do You Need an AI CoE?
Implementing AI isn’t just about buying tools or hiring data scientists. It’s about embedding intelligence into the fabric of your business. Without a structured approach, AI projects often suffer from:
- Lack of strategic alignment: Teams build models that don’t solve real business problems.
- Duplication of effort: Different departments reinvent the wheel with similar use cases.
- Talent bottlenecks: Skilled AI professionals are spread too thin or underutilised.
- Governance gaps: Ethical, legal, and compliance risks go unmanaged.
An AI CoE addresses these challenges head-on by providing a unified framework for AI adoption. It ensures that your AI efforts are not only technically sound but also strategically aligned, ethically responsible, and scalable.
Benefits of an AI CoE
Establishing an AI CoE isn’t just a technical investment—it’s a strategic one. Here are some of the key benefits:
- Faster Time to Value with centralised expertise and reusable assets, AI projects can be delivered more quickly and efficiently.
- Improved ROI by focusing on high-impact use cases and avoiding duplication, the CoE ensures that AI investments generate measurable business outcomes.
- Stronger Governance the CoE provides a structured approach to managing AI risks, from data privacy to algorithmic bias.
- Scalable Innovation as AI maturity grows, the CoE helps scale successful pilots into enterprise-wide solutions.
- Empowered Workforce through training and support, the CoE builds a culture of innovation and continuous learning.
Real-World Example: AI Centre of Excellence in a Law Firm
Picture a growing law firm under increasing pressure to manage an ever-expanding volume of contracts, case files, and compliance documentation. Different practice areas are starting to explore AI—contract review here, legal research tools there—but it’s all happening in pockets, with little coordination. The result? Duplication, inefficiency, and missed opportunities.
This is exactly where an AI CoE can make a measurable difference.
By putting an AI CoE in place, the firm can:
- Spot the highest-value opportunities, like automating contract intelligence and streamlining legal research and other use cases that benefit the entire firm, not just individual teams.
- Bring together the right mix of people including legal operations, IT, data scientists, and senior partners, to co-create a firm-wide solution for reviewing, classifying, and extracting key terms from contracts.
- Scale smarter, by adapting AI models across departments for consistent results and slashing time spent on manual reviews.
- Build trust through governance, embedding legal ethics, client confidentiality, and compliance into every AI workflow.
- Invest in training and change management, so that associates, paralegals, and support teams feel confident using these tools—not threatened by them.
The impact? Engagements move faster. Risk reduces. Compliance becomes easier. And your top legal talent gets to focus on strategic advisory, not repetitive admin. Ultimately, it’s about delivering better outcomes for clients, and creating space for the kind of work that grows the firm.
When external expertise is brought in to fill internal skill gaps which is often the case with AI projects, the AI CoE acts as the strategic bridge. Allowing alignment between external parties efforts and the firm’s goals, standards, and governance frameworks.
How to Get Started
Ready to build your AI CoE? Here are the first steps:
1. Secure Executive Sponsorship
An AI CoE needs strong backing from senior leadership. Showcase how AI aligns with your strategic goals, whether that’s improving customer experience, reducing costs, or driving innovation.
2. Define the Scope and Structure
Decide whether your CoE will be centralised (a single team), federated (embedded in business units), or a hybrid of these. Start small with a few high-impact use cases and scale over time.
3. Assemble the Right Team
Look for a mix of technical and business talent. You don’t need a huge team to start—just the right people with a shared vision.
4. Establish Governance
Define clear policies for data usage, model validation, and ethical AI. Set up review boards or steering committees to oversee major initiatives.
5. Invest in Tools and Infrastructure
Choose platforms that support collaboration, version control, and model deployment. Cloud-based solutions often offer the flexibility and scalability you need.
6. Measure and Communicate Impact
Track KPIs such as cost savings, revenue uplift, or customer satisfaction. Share success stories to build momentum and secure ongoing support.
Final Thoughts
AI is a journey not just a one-off project. And like any journey, it needs a map, a guide, and a destination. An AI Centre of Excellence provides all three.
By investing in a CoE, you’re not just adopting AI—you’re building the foundation for a smarter, more agile, and more competitive organisation.
So, whether you’re just starting out or looking to scale your AI efforts, now is the time to consider: Do we have the right structure in place to make AI work for us?
It’s no secret that businesses are producing more data than ever, and you will only be able to drive greater efficiencies and enable innovation once you have a clear strategy with the right tools in place.
Earlier this year, Microsoft launched Fabric, their all-in-one data solution, for general availability. Microsoft Fabric combines some of Microsoft’s most powerful tools, such as Data Factory, Synapse Analytics, Data Explorer, and Power BI into a unified, cloud-based platform to help simplify your data workflow. The combination of the tools will enable you to innovate with AI safely and securely by managing your data in a single user-friendly platform.