You’ll have heard it said before that ‘there are two types of businesses: those that get ahead and those that get left behind.’ It’s a prediction that’s starting to look increasingly accurate, particularly when it comes to Financial Services. Here we consider how and where AI is being used in the industry, its broader cultural implications, and what you should do to prepare.

“I think there are three main drivers in our industry right now: one is scale, one is complexity, and one is the risk of becoming irrelevant,” explains Kim Sgarlata, CEO of fiduciary services provider Oak Group.

AI has the potential to assist financial services firms with all three of those challenges.

When we talk about AI in financial services, we’re referring to two overlapping revolutions:

• The increasingly established use of data-driven AI, such as data analytics, machine learning, and task automation, along with
• The emerging use of ‘agentic AI’, to autonomously and intelligently execute workflows.

However, don’t think of AI as just a tool for productivity. It is increasingly central to competitiveness, compliance, and customer trust, within a highly regulated and reputation-sensitive environment. To understand why, and how, let’s take a deeper dive into these two areas.

Data-driven AI

Good data is fundamental to the success of data-driven AI initiatives, and for many its existence is far from a given.

“While storing data has become easier, ensuring it’s clean, governed, and consistent across the organisation is the real challenge today,” explained Jonathan Ball in his role as a Data Architect at 7IM.

But, underpinned by reliable data, AI can deliver improvements in:

• Operational resilience and efficiency
  Using AI to pre-emptively identify and fix IT vulnerabilities, before service outages. For real-time anomaly detection and predictive analytics for financial crime prevention, and for automating manual processes.
• Customer insight and experience
  Using a customer’s profile, goals, and risk appetite to provide personalised financial advice, or sentiment and behavioural analytics to understand customer needs and risk behaviours from transaction and communication data.
• Decision intelligence and risk modelling
  Identification of emerging trends, liquidity risks, or portfolio optimisation opportunities. For improved credit risk modelling and underwriting, and automation of regulatory compliance, such as ESG reporting, or early detection of conduct breaches.
• Innovation and product development
  Personalising insurance and investment products to an individual customer’s needs or drawing new insights from customer data to create new services or partnerships.

The role of Agentic AI

Rather than merely generating an output, Agentic AI can plan, reason, and act autonomously to achieve a goal – theoretically without human oversight. However, Financial Services’ governance requirements mean many are cautious about adoption. So, it’s likely that we’ll see a form of ‘bounded’ autonomy in which systems act within well-defined compliance and ethical frameworks.

Industry watchers are already talking of:

• Compliance monitoring, with AI that identifies potential issues and initiates remediation
• Autonomous trading agents, that operate within tightly governed limits
• Customer service agents that manage whole case journeys (eg from claim to resolution), albeit with human oversight
• Portfolio management copilots that propose and rebalance strategies, in line with ethical and regulatory constraints.

Over time, it’s likely that many more use cases will emerge.

Cultural considerations

Perhaps more than anywhere else, cultural and ethical considerations are as important as the technology in financial services. Trust, regulation and human judgement are integral to how the sector operates.

Here are five key areas for consideration.

• Trust and transparency
  Your organisation’s credibility will be eroded if either customers or regulators mistrust your use of AI. The technology must be understood, and teams must be able to explain why a model makes a decision, such as a loan approval or a risk flag.
• Human accountability
  AI shouldn’t be seen as an ultimate decision maker, but rather as something that can make decisions that humans remain accountable for – just like a well-trained, more junior member of staff. This means having a human oversight mindset, clear ownership, and encouraging people to challenge AI output.
• Data ethics and fairness
  You’ll want your use of AI to reflect your organisation’s stance on bias, discrimination, and data misuse. Certainly, those involved in developing your AI uses must be acutely aware that the end product will reflect any biases in its training data – but you should go further than this. Embed data ethics reviews into model design and discuss what’s fair, rather than merely meeting legal requirements.
• Involve subject matter experts
  AI success is heavily dependent on combining technology expertise with the knowledge of business and regulatory specialists. Bring data scientists, compliance officers, product managers, and customer advocates together in cross-functional teams. Encourage mutual learning and a shared language so these don’t degenerate into us and them, ‘tech versus business’, silos.
• The human touch
  Consideration of AI use can easily become very process oriented, but it can also trigger fear and uncertainty for staff. This needs to be sympathetically addressed. It’s also important to recognise that AI isn’t static and that culture needs to evolve with it. This may include ongoing training on responsible AI, bias awareness, and data handling,  as well as changing your organisation’s measures of staff performance to promote explainability and critical thinking around new AI capabilities.    

“The cost of AI is an investment, but one that pays off in saved time, better decision-making, and automation of repetitive tasks,” notes Matthew Ebo, Assistant Strategic Insights Manager, at Lloyds Banking Group.

Moving forwards

If you don’t yet feel fully ready to take advantage of AI, you’re not alone. EY’s ‘State of Financial Services AI transformation 2025’ survey revealed that only 9 per cent feel they are ahead of their peers in AI adoption. Yet, in the same survey, 61 per cent of UK and European executives said they expect AI to have ‘significant impact’.

So, what should you do if you want to improve your organisation’s AI preparedness?

Register to attend a free 1:1 consultancy with one of our Data and AI experts. In this session we can discuss the individual challenges your organisation is facing and help you discover the ‘Art of the possible’ with the right AI tools in place.

In 2025, the UK’s cyber resilience has been tested like never before. Major brands have made headlines after suffering disruptive cyberattacks, forcing them to halt operations and exposing sensitive customer data.

These incidents are not isolated. The UK government’s latest Cyber Security Breaches Survey reveals that 43 per cent of UK businesses experienced a cyber breach or attack in the past year, rising to 74 per cent among large organisations. Phishing remains the most prevalent and disruptive threat, and the financial and reputational costs are mounting. 

For IT decision makers, the message is clear: robust device management is no longer optional, it’s a strategic imperative. 

The evolving threat landscape

• Identity is the new perimeter: With traditional network boundaries dissolving, user identities have become the frontline of defence. Almost all (97 per cent) identity hacks are password spray or brute force attacks. Despite headlines proclaiming more sophisticated attacks, the majority of identity-based attacks still target weak or reused passwords.

• Ransomware on the rise: Human-operated ransomware attacks have surged, with 90 per cent of successful breaches originating from unmanaged devices outside the visibility and control of IT. 

• The AI impact: AI-driven phishing is now three times more effective than traditional methods. The increasing use of AI by attackers poses new challenges for detection and response, although AI can equally be used to defend against attacks such as by detecting anomalous sign-in patterns.

Why Traditional Approaches Fall Short 

Legacy Mobile Device Management (MDM) is no longer sufficient. The modern enterprise requires Unified Endpoint Management (UEM) and Unified Endpoint Security (UES) – these integrate antivirus, encryption, detection, and response into a single platform, ensuring consistent security across all devices and operating systems. 

How enhanced device management protects your organisation 

1. Limit identity breaches by adopting… 

• Mandatory Multi-Factor Authentication (MFA): Enforce phishing resistant MFA across all devices to drastically reduce the risk of unauthorised access, even if passwords are compromised. 

• Adaptive Access Policies: Integrate with Identity and Access Management (IAM) systems to trigger additional authentication or restrict access based on risk factors like device health, location, or user behaviour. 

• Continuous Monitoring & Zero Trust: Leverage AI and machine learning to monitor for anomalies, enforce “never trust, always verify” principles, and detect compromised credentials before they’re exploited. 

2. Prevent data breaches with… 

• Robust Encryption: Ensure data is encrypted both in transit and at rest, including full-disk encryption and protection for removable media. 

• Data Loss Prevention (DLP): Flag, track, and control sensitive data to prevent unauthorised sharing or exposure. 

• Remote Device Control: Instantly lock or wipe lost or stolen devices to prevent data leaks. 

Turning theory into practice 

Addressing Unmanaged Devices 

• Device Discovery: Use tools like Microsoft Defender for Endpoint to identify all devices (managed and unmanaged) on your network. 

• Onboarding: Bring unmanaged endpoints under management to close visibility gaps and reduce vulnerabilities. 

Leveraging Microsoft’s Ecosystem 

• Microsoft 365 & Defender Suite: Deploy built-in MDM, DLP, and Conditional Access Policies for consistent, integrated security. 

• Intune Security Baselines: Rapidly deploy recommended security configurations to all managed devices, addressing the root cause of most breaches – poor configuration. 

Navigating the Age of AI 

• BYOAI Risks: With four in five AI users bringing their own tools to work, device management is essential for controlling application use and preventing data leakage using tools like Microsoft Defender for Cloud Apps.  

• AI-Driven Security: Modern device management platforms use AI to predict threats, automate policy updates, and shift security from reactive to proactive. 

What next?

1. Assess your current device management posture: Identify unmanaged devices, poor configurations, and BYOAI risks. 
2. Adopt a unified, AI-powered device management strategy: Leverage Microsoft’s ecosystem and you’re existing M365 investment for comprehensive protection. 
3. Don’t wait for a breach: Proactive action today is the best defence for tomorrow’s threats. 

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The growing adoption of artificial intelligence and increasingly sophisticated cyber threats have collectively redefined what it means to maintain a secure and performant IT environment. Changes no longer happen in months or even weeks – the IT landscape is seemingly altered daily.

So, how do businesses deal with this increasing complexity? That’s where expert Managed Service Providers come into their own.

Modern environment demand modern expertise

The traditional boundaries of IT management have dissolved. Cloud platforms, remote working, multi-device ecosystems and AI-driven automation are no longer emerging trends but established standards. For many businesses, the sheer pace of change can be overwhelming. Internal IT teams, while expertly skilled, are frequently stretched to their limits by balancing multiple priorities including end-user support, cybersecurity, and strategic planning, without the additional consideration of constant, daily upskilling.

An MSP, however, brings specialised expertise, industry certifications and cross-sector experience to the table. Their teams are immersed in the latest technologies and best practices, enabling them to deliver future-focused solutions that are tailored to your business needs. From optimising your cloud footprint to ensuring seamless device management, MSPs keep your environment current and competitive while your teams can focus on what matters most to them.

Security: Proactive protection in a dynamic threat landscape

Cyber security is no longer a siloed departmental concern; it’s foundational to every part of the business – especially as AI and automation become integral to daily operations. However, with this, the risk profile expands.

Threat actors now use AI-powered attacks, targeting vulnerabilities at a pace and sophistication never seen before. To counter this, your businesses security must be proactive, adaptive, and relentless.

MSPs leverage advanced security tools to defend against evolving risks. Importantly, specialist partners can offer continuous monitoring and rapid incident response capabilities that are often out of reach for in-house teams alone. Compliance, too, can become more streamlined, as providers are familiar with navigating complex regulatory requirements. This ensures that your business meets standards such as GDPR and ISO, and is always audit-ready.

Performance: Delivering seamless user experience

Slow systems, downtime, or poorly integrated applications can erode productivity. MSPs can design and maintain environments and ensure they are fully optimised. They implement best-in-class monitoring tools, fine-tune workloads, and ensure that applications, infrastructure, and data pipelines work in harmony.

This focus on performance extends to business continuity. MSPs develop and regularly test disaster recovery and backup strategies, minimising the business impact of unexpected events. With their comprehensive oversight, potential issues are identified and remediated before they disrupt operations, allowing your teams to focus on innovation rather than firefighting.

Future-focused: Navigating the rapid evolution of AI

The transformational impact of AI in the workplace cannot be overstated. But with opportunity comes complexity (as you may have already experienced). Staying abreast of these developments is a daunting task for many businesses.

Gaining a strategic partner in this journey is critical to stay ahead of the curve. They provide a range of benefits, including:

• Ongoing education to upskill employees across your business
• Access to AI specialists to provide insights on emerging technologies
• Guidance on responsible AI use and ensure compliance with regulations.

Strategic growth: A value-driven partnership

Perhaps the greatest value an MSP offers is the ability for your in-house teams to refocus their energies on what matters most – driving the business forward. Routine maintenance, patch management, license optimisation, and regulatory reporting are handled efficiently. This partnership model transforms IT from a cost centre into an engine of value creation, enabling you to seize new opportunities.

The era of AI and rapid technology transformation has raised the stakes for businesses. MSPs act as trusted custodians of your digital environment, combining technical excellence with strategic foresight. They offer peace of mind ensuring you remain secure, compliant, and always ready to embrace the next wave of technological advancement.

If you’re ready to explore how an MSP can support your business goals, then we’re here with more than 20 years of experience in doing just that. As an Azure Expert MSP, we want all businesses to thrive on Azure, and that’s why we are offering an initial, free-of-charge 90 minute consultancy call to help workshop your challenges.

Sound interesting? Book a call.

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. 

1. 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’.  
2. 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. 
3. 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.  
4. 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’. 
5. 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.  
6. 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. 
7. 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.  

Cloud Direct’s Data & AI Practice Lead Dan Knott explains how you can strike a workable balance between speed of delivery, cost, and effectiveness with Microsoft Fabric.  

I spend a lot of my time talking to executives and technologists. I understand the time and cost constraints; I understand the pressure to implement fast; I understand that many don’t have the appetite for lengthy assessments and strategising. But I also know that without some consideration of four key factors outside of, but directly impacting, Microsoft Fabric you’ll probably fail.    

But first, a quick reminder.  

Microsoft Fabric in a nutshell

You probably already know of Microsoft Fabric. It is a one-stop shop for data: a unified data platform that can ingest, process, analyse, and visualise your data. It centralises data storage in OneLake – a single, integrated data lake that supports structured, semi-structured, and unstructured data – and combines capabilities from Power BI, Azure Synapse, and Data Factory into a seamless experience.  

Since its November 2023 launch into General Availability, Microsoft has continued to add functionality and, if you’re not already, you should now be looking at it.

Is Fabric a quick win?

Many in IT are going to look at this as a relatively easy implementation. But is it a quick win?  

In one sense, yes. Fairly quickly, you can get to the point where it’s installed, providing some nice dashboards, and offering an incremental improvement over Power BI.   

But in terms of delivering genuine business value, it won’t.  

You’re going to hit obstacles. I know this because those that have gone before you are consistently telling me: “we tried to implement Fabric”, “we hit some bottlenecks”, and “the adoption wasn’t quite there”. 

Navigating the pitfalls and driving real value from Fabric

While Fabric will happily ingest data from anywhere, it won’t fix fundamental data issues and it relies on users asking the right questions.  

So, consider how the business is going to benefit from Fabric. There are valid analytical, AI, and machine learning use cases. If your use case is analytical, for example, and your interest is in sales, are you looking forwards or backwards? If you’re looking back, what lessons are you trying to take from this? If your focus is the future, how does this need to align with your growth or business strategy?  

Regardless of your objectives, if people don’t trust the data then they’ll soon stop using Fabric. This, in itself, raises questions around the data, like its reliability and accuracy (realistically some areas will be better than others), who owns it, and security and governance considerations around who can access what.   

Given the chance, I’ll always argue passionately for a strategic consideration of what I call your four key pillars: innovation, platform and technology, process and tools, and people and culture. It’ll help you to understand where you currently have gaps, where you can reliably use Fabric now, any priority areas for action, and enable you to make longer term plans. In short, it’ll enable you to ensure that your organisation can derive real business value from Fabric straight away.

Reality bites

Set against this, there are time and budget pressures: “we need to get this in”, “let’s do it and find out”, “what’s the worst that could happen?” 

But from what I’m seeing and hearing, without a bit of thought and planning your implementation won’t get much beyond a tick in the box.  

The adoption of Fabric is far wider than just putting the tech in, and if you’re familiar with project management’s ‘Iron Triangle’ you’ll know that when it comes to cheap, fast and good, and can only have two of them.

Striking the right balance

With a little planning and thought, a lot of the pitfalls can be managed and, to an extent, avoided.  

Your journey probably won’t be the same as everybody else’s, but if we think in terms of the four pillars I mentioned, you’ll already know that there are some gaps.  

What do you want to gain from your data? It needs to be grounded in purpose.  

Are there data quality issues? Who’s accountable for this data? Are there governance considerations, perhaps around compliance and who can see which data? Do users have the skills to use the data well?     

This will quickly tell you if ‘just do it’ feels rash or even scary, and whether or not you’re setting up Fabric to ultimately fail.     

So, why not incorporate a bit of planning up front? Make sure that we’ve got the whole picture and have given some thought to those other areas which will impact the wider implementation of Fabric. 

There’s often a lot of value to be gained from a thorough Data Strategy Assessment, but much depends on where you already are and, of course, time and budget pressures. This is where one of our Maturity Assessments will help you quickly create solid foundations for your Fabric implementation.    

Microsoft Fabric really can show the value, purpose, and reliability of your data – but please, please, please put a little time into ensuring that your project can deliver business value, and ultimately succeed, before you get started.   

If you’d like an informal chat about how you can best approach your use of Fabric, you can get in touch, using the form below.  

In the final part of this three-part blog series on Agentic AI, we outline 6 Key considerations before you get started. If you missed them, part one provided an introduction to Agentic AI, while part two looked at how Azure AI Foundry Agent Service can help you get started.

In one respect, getting started with Agentic AI couldn’t be easier. Create an Azure AI Foundry project in your Azure subscription, and off you go.

But we see this time and time again with new technologies. A lot of time and effort goes into exploration and experimentation, and its largely wasted. That’s because the experimental uses aren’t aligned to driving business value – and since the experiments don’t deliver business value, things don’t go much further.  

So, before diving head-first into Agentic AI, it’s wise to take a step back and consider some of the bigger issues. Agentic AI is a fundamental shift in how decisions are made, tasks are executed, and systems evolve.

These six key areas of consideration will help you to embark on your use of Agentic AI responsibly and effectively.

1: Strategy first

Define clear goals: Understand your organisation’s business challenges – whether it’s automating customer service, optimising logistics, or improving sales processes. Most departmental heads will share their workflow and process pains with you and Agentic AI works best with a well-scoped mission.

Prioritise pragmatically: Start with smaller problems – larger more ambitious ones can follow – that also enable tangible and measurable outcomes to be realised. This will also enable you to build trust.

Autonomy versus control: How does your organisation (more specifically, its leadership) feel about agents making decisions? How much oversight will they want? Azure AI Foundry Agent Service allows for both, but it’s important to define requirements and boundaries before you start work.

2: Technical readiness

Microsoft Azure: To use Azure AI Foundry Agent Service, you’ll need an Azure subscription with the right roles set up to create projects and agents. These will include Azure AI Account Owner, Contributor, and User.

Check infrastructure compatibility: Ensure that your systems can support agentic workflows. Consider which APIs, databases, or systems your agents will need to interact with and check that they can. Azure AI Foundry Agent Service supports over 1,400 Logic Apps, and has native integration with SharePoint, Fabric, and Azure Storage so this may not be an issue.

Evaluate data quality: AI agents need clean, structured, and relevant data to reason effectively: Garbage in, garbage out. In the short-term data quality may determine the type of workflow automations you can pursue. It may also dictate future initiatives around data quality.  

Model selection: Agents rely on an underlying large language model and there’s a wide choice available withinAzure AI Foundry Agent Service. Your choices should be based on task complexity, latency tolerance, and cost profile – you can vary model choice by project based to suit these requirements.  

3: Secure by design

Shift-left security: Integrate security from the word go. Consider the reputation of your provider, their security, how well it integrates with your security model, as well as data access, and tool permissions.

Set boundaries: Be clear which data agents can access, share, and retain. Build in constraints to prevent bias, misuse, or unintended consequences. There are many frameworks, like KPMG’s Trusted AI model, which can help.

4: The human touch

Design for collaboration: Your AI agents should be complementing and not replacing human judgment, so build interfaces that allow human guidance and intervention.

Training: As with any new technology, good training is important. Make sure that stakeholders and users understand how agents work, what they can and can’t do, and how to interact with them responsibly.

5: Governance

Set AI use policies: As well as setting boundaries, you should agree acceptable use cases – and since these will likely change over time, a review process – and escalation protocols. This should involve legal and operations as well as IT.

Accountability: Although Agentic AI can act, humans should remain accountable for outcomes, so decide who’s responsible for agent decisions.

Auditability and transparency: Ensure that agents’ actions and decisions are logged and that people know how to monitor, debug, and, if needed, intervene.

6: Skills development

Skills: Before you start playing with Agentic AI make sure that your dev team have the right skills. Although the Azure AI Foundry Agent Service makes experimentation relatively easy, they’ll need skills in Python, C#, TypeScript, Java, or REST.

Prompt engineering: To work well, it is critical that that agents have clear instructions and well-defined tools. Your dev team may benefit from specific training in this area.

Getting started: Azure AI Foundry Agent Service provides a range of Agent samples and quickstarts to accelerate development. As well as an Agent Playground that allows instructions, tools, and workflows to be tested.

Planning for success

Successful use of Agentic AI requires a considered approach. While some of these are straightforward checks and practical steps, others are more demanding ‘blank sheet of paper’ exercises that involve asking questions like ‘can this be fast-tracked?’ ‘What are others doing?’ ‘Why?’ ‘Are there ready-made good, or even best, practices I can follow?’ Only then will you be setting off on the right foot and heading towards Agentic AI success.

That wraps up our three-part blog series. Feel free to read back over blogs one and two, or if you’re ready to explore Agentic AI in more depth then you can request an introductory call with one of our subject matter experts to see how Cloud Direct help you successfully benefit from the use of it.  

This is the second of a three-part series on Agentic AI. Here, we look at where it’s starting to appear, the power of multi-agent workflows, and how the Azure AI Foundry Agent Service can help. Check out part one if you haven’t already.

Agentic AI isn’t just assistive in nature. Instead, it’s able to use adaptive decision-making to complete a goal or reach a set target. Although it’s still early days with much greater sophistication to follow, we are already seeing Agentic AI offer many exciting possibilities and teams are beginning to be truly empowered in a number of ways.

Built-in capabilities

Agentic AI is starting to appear within existing systems and applications, and that shows no signs of slowing down. In IT, we’re seeing the beginnings of automated incident management (eg ServiceNow AI Agents), where an AI agent monitors infrastructure and, when it detects anomalies, takes action such as restarting services or allocating more resources. There are obvious benefits in terms of reduced mean time to repair (MTTR), fewer outages, and improved SLA compliance.

But Agentic AI is also appearing in other systems, and some of these may have departmental ownership which sit outside of IT’s direct control, but create governance issues for them to address.

Marketing automation platforms (such as Hubspot) are automating aspects of lead prospecting and qualification. By scanning both internal and external data sources, like LinkedIn, these platforms can identify high-value prospects target them with personalised communications, and prioritise them for conversion. Meanwhile in HR and recruitment, Agentic AI capabilities are being introduced to source, screen, and rank candidates.

Endless possibilities

Some of the most powerful uses of Agentic AI occur where it focuses on your business’ processes and challenges.

The Japanese IT provider Fujitsu has used Microsoft’s Azure AI Foundry Agent Service to automate the creation of sales proposals, using multiple specialist agents to interpret customer needs, access dispersed knowledge, apply reasoning, and generate a tailored proposal that’s contextually accurate and strategically aligned. It helps relatively new staff by surfacing insights and guidance and helps all sales staff by speeding up the process, with Fujitsu reporting a 67 per cent increase in productivity.

It’s where multiple agents are orchestrated to work together that the outcomes are really striking.

Microsoft has described how a financial services firm can automate customer onboarding process – from document collection, through identity verification and compliance checks, to account provisioning. Here, a Document Intake Agent receives a form or scanned document and, using File Search and Azure AI Search, separates information into its component parts. This then enables the agent to perform initial validation by checking required fields for completeness. A Review Agent compares customer data with regulation parameters and Know Your Customer norms, flagging any anomalies and, after a green light, a Setup Agent invokes provisioning tools to create customer accounts and send welcome messages.

The possibilities are almost endless, and you may already be thinking how such capabilities could be applied within your own organisation. That’s where Microsoft’s Azure AI Foundry Agent Service could help you.

Azure AI Foundry Agent Service: What you need to know

It’s been described as ‘an assembly line for intelligent agents’. The Azure AI Foundry Agent Service provides a platform and a framework for building intelligent agents that can reason and act.

• What you get: You get the components to create AI agents to achieve specific goals and to orchestrate them together to execute complete workflows.
• Choice: Agents are composed of a Model, Instructions, and Tools. You can choose which model you use from a growing catalogue which includes GPT-4o, GPT-4, GPT-3.5 (Azure OpenAI), Llama and others.
• Customisable: You get that same flexibility at every stage. Defining the agent’s goals, behaviour, and constraints, the tools used, and their orchestration (via Connected Agents).
• Availability: Azure AI Foundry Agent Service progressed from Preview to General Availability in May 2025, so it’s now fully supported for production workloads.
• Licensing: Licensing follows Azure’s standard consumption-based pricing, with PAYG (ie runtime), provisioned throughput, and enterprise agreement pricing options.
• Trustworthy: Microsoft has built in the security, governance and compliance features necessary to satisfy all of its enterprise clients.  

Microsoft’s Agent Service isn’t the only game in town, but it does offer an extensive capability and enormous flexibility combined with superb integration with your existing infrastructure, security and governance models. For most organisations, this makes it a strong contender.

Next steps

Before you start exploring Azure AI Foundry Agent Service, there are many ways in which Agentic AI could trip you up. In our third and final blog of this series, we look at how you can get started with six key considerations. But, if you’re keen to lean more now, then you can request an introductory call with one of our subject matter experts to find out how Cloud Direct help you successfully benefit from the use of Agentic AI. 

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.   

1. 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.  
2. Wasted time  
   Employees waste hours finding, verifying, or correcting information.  
3. 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. 
4. 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.  
5. Operational inefficiencies 
   Errors in billing, shipping, or inventory management cause rework, returns, and delays – all of which carry a cost.  
6. 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’.

1. 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.  
2. 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.
3. 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. 
4. 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.
5. 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. 
6. 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.

In this first of a three-part series on Agentic AI, we take a look at what it is, where it’s being used, and why it’s different to its technological predecessors. In parts two and three, we’ll go on to look at use cases, Microsoft’s Azure AI Foundry Agent Service, and six key considerations to get started.

If you’re on slightly shaky ground with Agentic AI, then you’re in the right place. Although Agentic AI is a rapidly emerging area of artificial intelligence, it’s still not widely understood outside those specialising in autonomous systems.

What is Agentic AI?

Rather than just responding to prompts like a chatbot, Agentic AI proactively pursues objectives – with autonomy, goal-directed behaviour, and adaptive decision-making.

In psychology, ‘agency’ is the capacity to act and produce results, so we’re talking about artificial intelligence that demonstrates that ability. Agentic AI’s aim is to achieve specific goals without constant human oversight.

Why Agentic AI is different

While much of the AI you may be familiar with follows predefined rules or produces prompt-based content, Agentic AI…

• Exhibits autonomy | This means it can initiate actions and not just react to instructions
• Solves multi-step problems | It can deal with complex, sequential workflows
• Learns and adapts | Like a real-life coworker, it will improve over time through feedback loops and real-world interactions
• Coordinates tasks | This enables an agent to handle specific tasks and work in tandem with other agents towards a shared goal

And it’s the element of autonomy that makes Agentic AI so different. While assistants such as Copilot support people, Agents complete goals.

How Agentic AI works

Before we look at the underlying technology, let’s first understand the process.

Agentic AI typically follows a four-step process.

• Perceive | First, it gathers data from sources such as sensors, databases, or user interactions
• Reason | Then, using a large language model, it understands the task and generates strategies
• Act | Utilising APIs, software tools, or other systems, it executes the task
• Learn | Finally, using feedback and outcomes from previous actions, it refines its performance

From a technical perspective, each Agent has three core components:

• A Large Language Model which powers reasoning, language understanding, and planning
• Instructions to define the Agent’s goals, behaviour, and constraints
• Tools which allow the agent to retrieve knowledge or take actions

In reality, this relies on a whole host of underlying technology, such as OpenAI GPT, Claude, Mistral or Gemini for the Large Language Model (LLM), the likes of LangChain, AutoGPT, MetaGPT or CrewAI for the autonomous agents framework which enables multi-step task execution and decision-making, and integration tools and APIs which will allow Agents to interact with external systems, for example Zapier, REST APIs, and browser automation.

Then there are vector databases like Pinecone or Weaviate, so Agents can retain informations across tasks and sessions, Reinforcement Learning Libraries (such as Ray RLlib and OpenAI Gym) which train Agents to make better decisions, and Orchestration Platforms such as Microsoft Azure AI Studio, HuggingFace Transformers, and IBM Watson Orchestrate to coordinate multiple Agents and workflows.

On first glance that probably sounds rather daunting, but that’s where technologies like Microsoft’s Azure AI Foundry Agent Service come in. Essentially, it’s a fully managed platform for building, deploying, and scaling Agentic AI systems, which we look at in further detail in the second blog of this series.

Where might you use Agentic AI?

In customer service, Agentic AI is already being used to handle refunds, schedule appointments, and resolve issues proactively. In finance, we’re seeing Agentic AI used to assess creditworthiness, automate mortgage or loan approvals, and manage aspects of compliance. In healthcare, it can extend accessibility providing after hours appointment booking and triaging patient queries, while the Government’s Department of Science, Innovation and Technology is exploring how Agentic AI can help people access and register for a range of public services.

With capabilities like these, it follows that you can build personal productivity agents for your knowledge workers (or yourself!) that manage calendar, emails, routine tasks, and to-do lists.

What must you be careful of?

There are also a series of potential ‘gotchas’ with Agentic AI: questions that need careful consideration.

What should or shouldn’t you use Agentic AI for? What boundaries do you want to set? Does it affect IT security? How do you avoid bias and ensure fairness? What governance measures will be required? And what of compliance?   

Most importantly of all, how do you ensure that as a result of all this work Agentic AI delivers genuine value to your organisation? We’ll look at this in detail in our third and final blog of the series when we set out six key considerations to getting started with Agentic AI.

Next steps

Be sure to follow up this blog with the following two parts, but if you’re keen to learn more directly from an expert, then we’re here to help. Request a call with a member of our team today and find out how Cloud Direct can help you successfully benefit from the use of Agentic AI.

Microsoft Azure offers an incredibly rich ecosystem for building, deploying, and managing applications. However, without a strategic approach to resource management, inefficiencies that lead to bill shock or under-utilisation can creep in. Maximising your Azure investment means actively identifying and resolving these inefficiencies. 

Here are five key steps to help you spot inefficiencies and drive continuous optimisation within your Azure environment.

Step 1: Gain comprehensive visibility with Azure monitoring and cost management 

You can’t optimise what you can’t see or understand. The foundational step to tackling Azure inefficiencies is establishing a clear, holistic view of your entire Azure footprint, encompassing performance, health, and cost allocation. 

Action: Leverage Azure Monitor to collect and analyse metrics and logs from all your Azure resources (VMs, App Services, databases). Utilise Log Analytics Workspaces for centralised log collection and analysis and, for application-level insights, deploy Application Insights. Critically, use Azure Cost Management + Billing for detailed cost analysis, understanding spending patterns by resource group, tag, and service. 

Optimisation Focus: Proactive identification of idle resources, performance bottlenecks indicated by high latency or low throughput, and unexpected cost spikes. Azure Cost Management + Billing will highlight where your money is going and identify anomalous spending. 

Cloud Direct customers get access to the Provide™ Portal as their single-pane-of-glass view for all Azure resources. This platform includes everything from spend monitoring, managing licenses, raising support tickets, and tips for performance and security improvements.

Step 2: Right-size and rationalise your Azure resources 

Over-provisioning is a primary driver of unnecessary costs in Azure. Many resources are initially deployed with more capacity than required, leading to consistent under-utilisation. 

Action: Regularly review the “Cost” and “Performance” recommendations within Azure Advisor, which provides personalised, actionable advice to right-size VMs, Azure SQL Databases, and other resources based on actual usage patterns. Downsize VM SKUs, adjust Azure SQL Database service tiers (from General Purpose to Basic/Standard if appropriate, for example), and utilise Blob Storage tiers (Hot, Cool, Archive) to match data access frequency. Identify and decommission unused resources like orphaned disks, unattached public IP addresses, and idle ExpressRoute circuits, and use Azure Resource Graph queries to find “zombie” resources. 

Optimisation Focus: Directly reduce infrastructure costs by matching resource allocation precisely to demand, eliminating waste from over-provisioning and idle assets. 

Step 3: Implement strong Azure governance and cost policies 

Without robust governance, “Azure sprawl” can quickly lead to an uncontrolled explosion in costs. Establishing clear policies and processes for resource provisioning, tagging, and budget management is critical to prevent inefficiencies before they take hold. 

Action: Define a comprehensive Azure Tagging strategy (for cost centres, environments, owners) and enforce it using Azure Policy to ensure resources are consistently tagged for granular cost reporting in Azure Cost Management. Set up budgets and spending alerts in Azure Cost Management + Billing at the subscription or resource group level. Implementing Azure Policy will also enable you to enforce compliance such as restricting regions, disallowing specific resource types, or automatically shutting down VMs in Dev/Test subscriptions after hours, while Azure DevTest Labs can be used for development environments, which offers built-in auto-shutdown features. 

Optimisation Focus: Gaining control over spending, improving accountability, preventing shadow IT, and ensuring that Azure resources are provisioned and used according to organisational guidelines. 

Step 4: Leverage Azure automation and Infrastructure-as-Code for efficiency 

Manual processes in Azure are not only time-consuming but also prone to errors and inconsistency, which only hinder efficiency. Automation is key to streamlining operations and ensuring consistent, cost-effective deployments. 

Action: Automate routine tasks using Azure Automation Runbooks (PowerShell, Python) for things like scheduled VM shutdowns, patch management, and backup operations. Implement Infrastructure-as-Code (IaC) using ARM Templates or Bicep to define and deploy your Azure infrastructure in a consistent, repeatable, and version-controlled manner. Use Azure DevOps or GitHub Actions for CI/CD pipelines to automate deployments. For dynamic workloads, configure Azure Auto-scaling for Virtual Machine Scale Sets, App Services, and Azure Kubernetes Service (AKS). 

Optimisation Focus: Reducing operational overhead, minimising human error, ensuring consistent and optimised configurations, and enabling your Azure infrastructure to dynamically adapt to demand, thereby using resources more efficiently. 

Step 5: Foster a culture of continuous Azure optimisation 

Cloud optimisation in Azure isn’t a one-time project; it’s an ongoing commitment. The dynamic nature of the cloud and evolving business needs require a continuous loop of review, refinement, and improvement. 

Action: Embrace FinOps principles, fostering collaboration between finance, operations, and development teams to drive cost accountability and efficiency. Schedule regular reviews of Azure spend and performance metrics using Azure Cost Management dashboards. Continuously monitor Azure Advisor for new recommendations. Stay informed about new Azure services, features, and pricing models (such as Azure Reservations for significant savings on consistent workloads, Azure Hybrid Benefit for existing Windows Server/SQL Server licences). Actively engage with Azure’s Well-Architected Framework, particularly its Cost Optimisation pillar. 

Optimisation Focus: Embedding cost awareness and efficiency into your organisational culture, ensuring that optimisation becomes a routine part of your Azure operations, leading to sustained cost savings and improved performance over time. 

The Result

By systematically taking these five actions, you can effectively identify and eliminate inefficiencies and create a more cost-effective, performant, and resilient Azure environment. This proactive approach not only saves money, but also frees up resources to drive innovation and support your strategic goals on the Microsoft Azure platform. 

If you want to learn more about driving optimising inefficiencies in your Azure environment, then an Innovation Workshop will provide a platform for us to collaboratively examine your business context, and identify opportunities for maximising the return from your Azure spend. 

When you commit to Microsoft Azure, you’re launching into an exciting world of potential. It’s a cloud platform where you can innovate and scale with a purpose.

However, after the initial excitement of this journey, a feeling of uncertainty might start to creep in. If you take your eye off the ball, you’ll soon find yourself drowning in a complex cloud environment with spiralling costs.

You’re not alone

This is a common experience. In fact, a staggering 84 per cent of organisations struggle to manage their cloud spend, with cloud budgets typically being exceeded by 17 per cent. This alone tells us that many are struggling when it comes to financial control. 

Many organisations find themselves at this exact crossroads, asking themselves “how can we maximise our cloud resources and Azure potential?” One path is to go it alone, learning the ropes through trial and error, hoping you can avoid the multitude of expensive mistakes that are often made when using your own organisation as guinea pigs.

The other path involves working with an expert partner. Traditionally, these relationships involve handing over the reins to an external party – but we’re reimagining things. We’re keeping you in the driving seat and helping you on your way.

Addressing your biggest challenges

Managing your Azure environment with sub-par support can come with a range of challenges. You might be familiar with a few of these…  

First, cost. It’s the Monday morning meeting where leadership holds up an Azure invoice that is higher than expected and asks a simple question: “What are we actually paying for?” This is the dreaded bill shock, and it comes from a lack of clear visibility and control over cloud spending. It is a problem compounded by the fact that more than 80 per cent of container spend is reportedly wasted on idle resources. Without an expert eye, costs can spiral, and it can feel almost impossible to track down which services are consuming your budget. 

Then, there’s the expertise gap. Azure is a universe in itself, and Microsoft is adding new features and making changes at breathtaking pace. Your internal IT team may be brilliant, but expecting them to be world-class experts in Azure security, performance tuning, cost management, and every other part of their day job is a huge ask. It often leads to teams feeling overwhelmed, and technical roadblocks slowing down genuine innovation.  

Finally, you’re faced with an inflexible, all-or-nothing choice for support. You either have basic support, where you log a ticket and hope for the best, or you pay a premium for a managed service that might be overkill for your needs. It leaves you wishing for a middle ground – a flexible partnership that provides expertise and guidance without taking away your autonomy. 

Introducing Azure CSP+ 

We’ve had countless conversations with businesses facing these exact issues. They love the power of Azure, and their teams are eager to drive it, but too often, other CSP models force them into a choice between basic ticket-logging support and costly, fully managed services. 

Cloud Direct’s CSP+ takes a different approach. Rather than just a product or toolkit, it offers a more cost-effective way to transact your Azure spend, built around a flexible support service. It’s designed for organisations that want to manage their own environments while having the reassurance of a trusted expert on hand. At its core, CSP+ is built on complete transparency and genuine partnership, featuring:

• Transparent cost-plus pricing: We do not believe in hiding costs behind complex calculations. Our CSP+ service uses a simple and clear pricing model. You see what you are paying for, allowing you to choose a service level that fits your budget perfectly.  

• Smarter management: Every CSP+ customer gets access to our exclusive Provide™ portal. This is your centralised platform to monitor your security posture, track costs and carbon emissions, and automate resource deployment. 

• Expertise on demand: Tools are great, but nothing replaces human experience and expertise. CSP+ allows you to tap into our Azure-certified engineers, cloud consultants and solution architects whenever you need them.  

Finding the perfect plan for you 

Every organisation’s journey is different, so we designed CSP+ with three flexible tiers:

• Essential: The most cost-effective way to transact your Azure spend with business hours support for any platform issues, in addition to full Provide™ Portal access and reassurance from a trusted Azure Expert MSP partner.  

• Enhanced: For organisations where performance and cost are top priorities, this tier provides access to 24×7 support, on top of proactive quarterly optimisation reviews to keep your environment in peak condition. 

• Enterprise: This is our top-tier service for organisations looking to utilise Azure to build strategic advantage. You get everything in the Enhanced tier, plus monthly optimisation reviews and direct access to our senior Tier 4 engineers and Cloud Architects. This gives you rapid problem-solving for complex issues in addition to strategic guidance on architecture and innovation to help you stay ahead. 

The result is a partnership that empowers you. Instead of unpredictable bills, you get financial clarity. Instead of hitting technical roadblocks, you accelerate issue resolution and innovation. Free up your talented team to focus on the strategic projects that drive your business forward. 

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: 

1. Faster Time to Value with centralised expertise and reusable assets, AI projects can be delivered more quickly and efficiently. 
2. Improved ROI by focusing on high-impact use cases and avoiding duplication, the CoE ensures that AI investments generate measurable business outcomes. 
3. Stronger Governance the CoE provides a structured approach to managing AI risks, from data privacy to algorithmic bias. 
4. Scalable Innovation as AI maturity grows, the CoE helps scale successful pilots into enterprise-wide solutions. 
5. 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? 

Written by Paul Sells

Digital transformation isn’t at the forefront of everyone’s minds just because it’s a nice to have – it’s because it’s a necessity. Recent studies suggest companies that embrace digital transformation are 26% more profitable than their peers, and yet many businesses struggle to navigate the complexities of digital transformation.

I’ve been in your shoes. Over the past five years as Cloud Direct’s Chief Technology Officer, I have been faced with a number of technological challenges that, one way or another, we’ve had to find resolutions for.

As companies start looking to AI, and that transformation journey steps up a notch, I’ve picked out 10 key challenges that I’ve seen both ourselves and our customers face, and identified ways that you can overcome them.

Resistance to change

Challenge

Resistance to change is a natural human reaction, especially when it comes to adopting new technologies. Employees may fear that digital transformation will render their skills obsolete or disrupt their daily routines. This resistance can significantly hinder progress and innovation.

Impact

Resistance to change can lead to decreased productivity, low morale, and even high employee turnover. It can also slow down the implementation of new technologies, delaying the benefits of digital transformation.

Solutions

Implement change management strategies

Change management is crucial for easing the transition. This involves clear communication, setting realistic expectations, and involving employees in the process. By addressing concerns and providing support, businesses can foster a more positive attitude towards change. 

Provide comprehensive training and support

Offering training programs can help employees feel more confident and capable in using new technologies. This can include workshops, online courses, and one-on-one coaching sessions.

Communicate the benefits clearly and effectively

It’s essential to highlight the benefits of digital transformation, such as increased efficiency, better customer service, and new opportunities for growth. By showing how these changes will positively impact their work, employees are more likely to embrace them.

Data security concerns

Challenge

With the increasing reliance on digital technologies, data security has become a top priority for businesses. Cyberattacks and data breaches can have devastating consequences, including financial losses, reputational damage, and legal repercussions. 

Impact

Data security concerns can lead to a lack of trust in digital transformation initiatives. Businesses may be hesitant to adopt new technologies if they fear that their data will be compromised.

Solutions

Invest in robust cybersecurity measures

Implementing advanced security protocols, such as encryption, multi-factor authentication, intrusion detection systems and adopting Zero-trust principles, can help protect sensitive data.

Conduct regular security audits and assessments

Regularly reviewing and updating security measures can help identify vulnerabilities and ensure that the latest protections are in place.

Educate employees on best practices for data security

Providing training on data security best practices, such as recognising phishing attempts and using strong passwords, can help prevent breaches caused by human error.

Skill gaps in the workforce

Challenge

The rapid pace of technological advancement has created a significant skills gap in the workforce. Many employees lack the necessary skills to effectively use new digital tools and technologies. 

Impact

A lack of skilled workers can slow down digital transformation efforts and reduce the overall effectiveness of new technologies. It can also lead to increased costs as businesses may need to hire external experts or invest in extensive training programs.

Solutions

Offer training programmes to upskill current employees

Investing in employee development can help bridge the skills gap. This can include offering courses, certifications, and hands-on training opportunities.

Partner with educational institutions to create a talent pipeline

Collaborating with universities and technical schools can help create a steady stream of qualified candidates. This can include internships, co-op programs, and sponsored research projects.

Hire external experts or consultants for specialised tasks

For highly specialised tasks, it may be more efficient to hire external experts or consultants. This can provide access to the necessary skills and augment internal resources/capabilities.

Integration with existing systems

Challenge

Integrating new digital technologies with existing legacy systems can be a complex and challenging process. Compatibility issues, data silos, and outdated infrastructure can all pose significant obstacles. 

Impact

Integration challenges can lead to disruptions in business operations, data inconsistencies, and increased costs. They can also delay the implementation of new technologies, reducing the overall benefits of transformation.

Solutions

Conduct a thorough assessment

Before implementing new technologies, it’s essential to conduct a comprehensive assessment of existing systems. This can help identify potential compatibility issues and areas that need improvement.

Use middleware solutions to facilitate integration

Middleware solutions can help bridge the gap between new and existing systems, allowing them to work together seamlessly.

Plan for phased implementation

Implementing new technologies in phases can help minimise disruptions and allow for adjustments along the way. This can also provide an opportunity to test and refine the integration process.

High implementation costs

Challenge

The costs associated with digital transformation can be significant. This includes the cost of new technologies, training programs, and potential disruptions to business operations.

Impact

High implementation costs can be a major barrier for many businesses, particularly small and medium-sized enterprises. It can also lead to budget overruns and financial strain.

Solutions

Create a detailed budget and ROI analysis

Developing a comprehensive budget and ROI analysis can help businesses understand the financial implications of digital transformation. This can also help identify areas where costs can be reduced. 

Explore financing options or grants

There are various financing options, grants & funding available to support digital transformation initiatives. Researching and applying for these can help offset some of the costs. 

Prioritise high-impact areas for initial investment

Focusing on high-impact areas can help maximise the benefits of digital transformation while minimising costs. This can include areas that offer the greatest potential for efficiency gains or revenue growth.

Unclear return-on-investment

Challenge

Measuring the return on investment (ROI) for digital transformation initiatives can be challenging. This is particularly true for long-term projects where the benefits may not be immediately apparent.

Impact

Unclear ROI can make it difficult for businesses to justify the costs of digital transformation. It can also lead to uncertainty and hesitation in decision-making.

Solutions

Define clear metrics and KPIs

Establishing clear metrics and key performance indicators (KPIs) can help measure the success of digital transformation initiatives. This can include metrics related to efficiency, customer satisfaction, and revenue growth.

Conduct pilot projects to demonstrate value

Running pilot projects can provide valuable insights into the potential benefits of digital transformation. This can help build a business case for larger-scale implementation.

Regularly review and adjust strategies based on performance data

Continuously monitoring and analysing performance data can help identify areas for improvement and ensure that digital transformation initiatives are on track to deliver the desired ROI.

Complexity of AI technologies

Challenge

AI technologies can be complex and difficult to understand. This can create barriers to adoption, particularly for businesses that lack the necessary expertise. 

Impact

The complexity of AI technologies can lead to confusion and hesitation in adoption. It can also result in suboptimal implementation and reduced effectiveness.

Solutions

Simplify AI adoption with user-friendly tools

Using user-friendly AI tools and platforms can help make adoption easier. This can include tools with intuitive interfaces and built-in support features.

Provide ongoing training and support

Offering ongoing training and support can help employees feel more confident in using AI technologies. This can include workshops, online courses, and access to AI experts.

Collaborate with AI experts to develop tailored solutions

Working with AI experts can help develop customised solutions that meet the specific needs of the business. This can also provide access to the latest advancements in AI technology.

Cultural barriers

Challenge

Cultural barriers can significantly impact the success of digital transformation initiatives. This includes resistance to change, lack of collaboration, and a risk-averse mindset. 

Impact

Cultural barriers can slow down progress and reduce the overall effectiveness of digital transformation. They can also lead to low employee engagement and morale.

Solutions

Foster a culture of innovation and continuous improvement

Encouraging a culture of innovation can help overcome resistance to change. This can include promoting experimentation, rewarding creativity, and celebrating successes.

Encourage cross-functional collaboration and communication

Promoting collaboration and communication across different departments can help break down silos and foster a more cohesive approach to digital transformation.

Recognise and reward employees who embrace digital transformation

Recognising and rewarding employees who actively participate in digital transformation initiatives can help motivate others to do the same. This can include awards, bonuses, and public recognition.

Regulatory compliance

Challenge

Adhering to regulations and compliance requirements is a critical aspect of digital transformation. This includes data protection laws, industry-specific regulations, and internal policies. 

Impact

Non-compliance can result in legal and financial repercussions, including fines, lawsuits, and reputational damage. It can also create barriers to the adoption of new technologies.

Solutions

Stay informed about relevant regulations

Keeping up-to-date with the latest regulations and compliance requirements is essential. This can include subscribing to industry newsletters, attending conferences, and consulting with legal experts.

Implement compliance management systems

Using compliance management systems can help ensure that all regulatory requirements are met. This can include automated monitoring, reporting, and documentation.

Work with legal experts to ensure adherence

Collaborating with legal experts can provide valuable insights and guidance on compliance issues. This can help businesses navigate complex regulations and avoid potential pitfalls.

Customer adoption

Challenge

Getting customers to adopt new digital solutions can be challenging. This includes overcoming resistance to change, addressing usability issues, and providing adequate support. 

Impact

Low customer adoption rates can reduce the overall effectiveness of digital transformation initiatives. It can also lead to decreased customer satisfaction and loyalty.

Solutions

Develop user-friendly interfaces and experiences

Creating intuitive and user-friendly interfaces can help improve customer adoption. This can include simplifying navigation, providing clear instructions, and offering personalised experiences.

Provide customer education and support

Offering educational resources and support can help customers feel more comfortable using new digital solutions. This can include tutorials, FAQs, and dedicated support teams.

Gather and act on customer feedback

Collecting and analysing customer feedback can provide valuable insights into areas for improvement. This can help businesses make necessary adjustments and enhance the overall customer experience.

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Leadership plays a pivotal role in the success of digital transformation and AI adoption. Strong leadership can inspire and motivate employees, drive innovation, and ensure that the organisation stays on track to achieve its goals. Leaders should have a clear vision for the future and articulate a compelling strategy for achieving that vision.  

And that’s where you come in. Provide the necessary resources, training, and support to promote an environment where employees feel valued and encouraged to take risks and innovate. Leaders should do just that – lead. Make sure you’re embracing change and demonstrating resilience in the face of challenges, setting a positive example for your teams. Demonstrate that they too can navigate uncertainties and adapt strategies as needed to ensure the success of digital transformation initiatives.

Do that, and anything’s possible.

Written by

Paul Sells CTO, Cloud Direct

The rise of hybrid working has completely changed the security perimeter for good. Security perimeters used to be defined by your organisation’s location, as that’s where your desktops, servers and employees were. But in the hybrid workplace, this extends beyond your offices to any access point that hosts, stores, or accesses corporate resources and services. In the new world of working, employees need secure access to their resources, regardless of where they are working.

Now’s the time to rethink your security strategy. Cyber-attacks have become increasingly sophisticated, and the old castle-and-moat approach is no longer an effective method of securing your environment.

Time to ditch the castle-and-moat approach

We all know the castle-and-moat approach – defend your perimeter while assuming everything that’s already inside doesn’t pose a threat and is already cleared for access. Well, if you’re still using that mentality, it’s time to leave the castle-and-moat approach in the past. Why? It’s been proven time and time again that it doesn’t work in the modern workplace. There’s been endless data breaches as hackers have gained access inside of the firewalls and were able to seamlessly move through internal systems with minimal resistance.

Traditional security practices, such as castle-and-moat, are unable to keep up with the complexity of the ever-evolving workplace. Gone the days where the ‘castle’ works in isolation as it used to. Businesses no longer have data centres for a contained network, but instead, have applications both on-premise and in the cloud with different users accessing them from multiple devices and locations.

It’s important you have a security strategy in place that’s aligned with the modern, hybrid work environment which will look at keeping anything inside and outside your perimeter safe. This is why we want to introduce you to Zero Trust, an end-to-end security strategy that’s been adopted by millions of organisations across the world to protect their technology ecosystem.

What is Zero Trust?

Zero Trust Network is a security best practice model which was created back in 2010. Over a decade later, IT managers across the world are implementing Zero Trust as their security strategy. It’s never been easier to adopt a Zero Trust approach as more common technologies, such as Microsoft’s security solutions, are supporting it. Simply put, the Zero Trust concept is built on the belief that businesses shouldn’t trust anything inside or outside its perimeters and must verify anything and everything to connect its systems before granting access.

What does this mean for you? It would require a change in mindset – instead of assuming everything behind your firewall is safe, assume breach and the need to verify each request as it comes from an open network. Zero Trust encourages you “never trust, always verify”, meaning no cyber attack slips through the gaps. Having a Zero Trust security strategy means:

• Any access request should be authenticated and authorised before granting access.
• You should utilise analytics to detect and respond to any anomalies in real time. Mitigating any risks before they become a real threat.
• Microsegmentation and least privileged access principles are applied to minimize lateral movement
• Leverage analytics to identify what’s happened, if anything was compromised and how to stop it.

The three guiding principles of Zero Trust

There are three principles that a Zero Trust strategy is built on; verify explicitly, use least privileged access, and assume breach. When you have a Zero Trust strategy in place, these three principles should be at the heart of your IT and at the forefront of your mind.

Verify explicitly

Always authenticate and authorise data points. This includes identity, location, device and workload.

Use least privileged access

Limit user access in order to protect your data.

Assume breach

Verify that all end points are encrypted end to end. Leverage analytics to get visibility, drive threat detection and improve defences.

Six elements to Zero Trust

The Zero Trust security methodology is made up of six core elements: identities, devices, apps, data, infrastructure and networks. Think of these six elements as the pillars to building a Zero Trust environment.  They need to be defended in order for you to stay secure, and you can do this by assuming breach.

Why you should consider adopting Zero Trust

Adopting a Zero Trust strategy will benefit your business in other ways than just protecting your business. For starters, you’ll have a true sense of how secure you truly are and will be able to better manage your overall security posture.

You’ll also have the opportunity to scale back on any overlapping security spend. Businesses can often buy security products for tactical reasons that will often work in isolation to look after a single aspect of your security. Overtime there often becomes a pileup of products that aren’t delivering optimal coverage or value for money. Adopting a Zero Trust method will help you unify your security and ultimately help you save on costs as you streamline your technologies.

Finally, there is currently a huge skills gap when it comes to cyber security, and that gap is only going to grow. As more businesses identify they can no longer trust everything within their network, and realise they need to adopt a Zero Trust approach, this can often highlight a industry wide issue that businesses across the UK are currently facing a massive skills gap when it comes to Cyber Security. But Zero Trust can help this as the methodology will help give you a clear foundation to follow. Plus. if you don’t have the in house capabilities available, then you will have the option to draw upon the expertise from a company who specialises in security. Psst… we have a Zero Trust Assessment available, which brings us nicely onto wrapping up this blog post.

Looking to implement Zero Trust

The truth is, building Zero Trust within an organisation doesn’t happen overnight. There’s a lot of planning that’s involved. Microsoft suggests you start by evaluating your current environment, available resources and priorities. From there you can start pulling together a plan to implement a Zero Trust strategy that meets your business needs.

Implementing Zero Trust in your organisation can often seem overwhelming. The best first step is to ensure you have the right technology in place to help you enable Zero Trust. Microsoft Technologies have the security tools and features you need to implement Zero Trust – Enabling you to gain control over your organisation’s security. Head over to Microsoft’s website to find out how Bridgewater leveraged Microsoft 365 to deploy a Zero Trust security model.

As an Azure Expert MSP with several Microsoft Security specialisations, we’re in a unique position to help you adopt Zero Trust within your organisation. Not only could we help you deploy Microsoft technologies, but we’ll also help you implement the Zero Trust best practices and framework. Simply get in touch to find out more.

Whether it be banking, insurance, capital markets or investment management, these sectors all have one thing in common – they are data goldmines. These types of financial institutions generate massive amounts of data every day, from customer transactions and interactions to regulatory reports and analysis. While this data holds immense potential for driving innovation and crucial business decisions, it also presents significant challenges.

In this blog, we’ll explore the top data challenges faced by financial services organisations and how a unified modern data platform like Microsoft Fabric, can be a game changer for IT, Data and AI leaders in this sector.

What are the data challenges?

The financial services sector operates at the intersection of vast data volumes, high regulatory demands, and evolving customer expectations. However, unlocking the full potential of data comes with its fair share of hurdles. Let’s take a closer look at the key challenges :

Data Fragmentation: Many financial institutions might find that they often have data scattered across multiple systems like legacy platforms, CRM tools, trading systems, and more. Having your data in multiple silos can hinder collaboration and make it difficult to gain a holistic view of your customers or your operations.

Data Volume and Complexity: With the rise of digital banking, real-time payments, and customer analytics, data volumes are skyrocketing. Being able to process, analyse, and draw insights from this data at scale is a significant challenge in itself.

Data Security and Privacy: Growing cyber threats and the need to protect sensitive customer information are ongoing concerns across financial services. Making sure that data is stored securely is non-negotiable, but are you properly managing and maintaining your secure environment?

Real-Time Insights: Financial markets operate very quickly, and things can change suddenly. Gaining real-time insights from transactional data to drive decisions, detect fraud, or enhance customer experiences is critical, but it demands robust infrastructure and tooling – which some financial services organisations lack.

Regulatory Compliance: The financial sector in the UK is tightly regulated, with laws and standards demanding robust data governance from financial service institutions. Having the ability to meet these high standards while maintaining agility can be a daunting task.

These top data challenges are just the tip of the iceberg, with many more unique challenges facing financial services businesses across the UK. By addressing these challenges head-on, businesses can unlock significant value from their data while meeting regulatory and security requirements. But overcoming these hurdles requires the right tools, platforms and strategies – which is where Microsoft Fabric comes into play.

How can Microsoft Fabric help with data challenges?

Microsoft Fabric is an end-to-end analytics and data platform designed for businesses that require a unified data solution. The platform encompasses data movement, processing, ingestion, transformation, real-time analytics and report building. All in all, Fabric helps to simplify and unify the complexities of modern data management.

With its unified OneLake architecture, Fabric brings all your data – structured and unstructured – into one cohesive platform, making it easier for teams to collaborate and giving you a full 360-degree view of your operations and customers. No more frustrating silos! It also makes meeting regulatory requirements a breeze, thanks to built-in tools like Microsoft Purview that handle data lineage, auditing, and compliance tracking so you can stay agile while staying compliant.

When it comes to big data, Fabric has you covered with many powerful integrations.  Allowing you to process large datasets effortlessly for tasks like risk modelling, fraud detection, and real-time analytics. Security is a top priority, with features like role-based access controls, encryption, and Azure Directory ensuring your sensitive data is protected while keeping access easy for your team. With Synapse Real-Time Analytics, you can tap into the insights from transactional data to detect fraud, optimise trading, and deliver exceptional customer experiences – all in the moment.

What’s next?

Navigating the data challenges of the financial services sector doesn’t have to be overwhelming. With Microsoft Fabric, organisations can unlock the full potential of their data while staying compliant, secure, and efficient.

There’s plenty more to learn about how you can solve common data problems at your financial services business. We didn’t want to spoil you with everything at once, so register to join our webinar, Overcoming Data Challenges in Financial Services on Thursday 12^th December. Hear from data experts as they expand upon the data challenges you could face and the tools and strategies to overcome them.

As one of Microsoft’s most trusted UK partners, Cloud Direct is perfectly positioned to help your financial services business unlock data innovation. Supported by an extensive list of accreditations, including being one of the most established Azure Expert Managed Service Providers, our team of experts build the foundations that ambitious financial service organisations need to grow, innovate and succeed.

If you’d like to find out more about Microsoft Fabric for your business, get in touch today!