Every IT decision maker understands the importance of a clear cloud strategy. It is not just about where you host your apps. Your strategy must actively support the overall mission of your company. The challenge is that Azure evolves at lightning speed. New features, services, and security standards appear constantly, making it hard for teams to keep up. 

This is where many organisations fall into the trap of reactive cloud management. They spend all their time fixing problems and responding to immediate demands. Whereas proactive management requires something different. It needs dedicated expertise that can look years ahead and guide your architecture toward future success. 

Reactive vs Proactive Cloud Management 

Reactive management focuses on solving today’s issues. Proactive management anticipates tomorrow’s challenges and positions your business to take advantage of new opportunities. Without expert guidance, your cloud strategy risks falling behind. This is what we call ‘strategy drift’. 

The Risks of Strategy Drift 

When your cloud configuration moves away from best practices, you face three major risks: 

• Unnecessary Costs: You miss new cost saving features or fail to adapt to licensing changes. 

• Missed Innovation: You do not adopt AI or data services that could give your business a competitive edge. 

• Security Gaps: Your architecture fails to keep up with the latest security standards, leaving vulnerabilities unaddressed. 

A dedicated expert resource such as a specialist Azure Solution Architect can help to prevent these risks. However, hiring a full time Azure Solution Architect is expensive and for most businesses their expertise is only needed for major projects or complex upgrades. So how do you access that high level insight when you need it most? 

Five Expert Tips for IT Decision Makers 

To future proof your Azure strategy, here are five essential tips: 

1. Prioritise Proactive FinOps: Do not wait for the bill to arrive before thinking about costs. Implement automated rules and architectural best practices to ensure continuous cost optimisation. 

2. Plan for AI Adoption: Azure is rapidly integrating AI features. Your cloud strategy should include a roadmap for leveraging services like Copilot and advanced Data and AI platforms, such as Microsoft Fabric, to drive business growth. 

3. Strengthen Security Posture: Regularly review your architecture against the latest security standards. Proactive security checks prevent vulnerabilities before they become incidents. 

4. Align with Sustainability Goals: Track CO₂ emissions in your Azure usage and integrate sustainability targets into your cloud roadmap. 

5. Leverage Azure Expert Advisory: The best way to ensure your strategy is sound is by partnering with a Microsoft Azure Expert MSP. Selecting the right partner will bring certified expertise and proven experience to help you deliver business value. 

CSP+ Delivers Azure Expert Advisory 

Our Cloud Direct CSP+ programme is designed to help you close the expertise gap. CSP+ is a tiered model so that you can pick the level of support that your business needs. From the essentials tier where you’ll gain an initial onboarding health check and business hours support. To the enterprise tier for 24×7 support, monthly optimisation reports and access to a dedicated Azure Solution Architect to deliver strategic advisory sessions.  

These experts provide ongoing architectural reviews to keep your Azure strategy aligned with your business goals. This includes: 

• Strategic Feature Adoption: Guidance on deploying new Azure features safely and effectively. 

• Long Term Architecture: Support in designing scalable, resilient, and secure environments. 

• Risk Mitigation: Reviewing your environment to eliminate potential security and performance issues before they become incidents. 

Expert insight through CSP+ means your internal team is never alone. You have the full weight of a certified Azure Expert MSP behind you, ensuring your cloud platform is architecturally optimised and ready for innovation. 

Ready to Strengthen Your Azure Strategy 

Stop reacting and start planning for the future. Try our CSP+ calculator to find the right plan for your business. Plus, save on costs instantly.

It’s becoming increasingly apparent that artificial intelligence will be integral to how organisations operate effectively and remain competitive. But responsibility is a topic that regularly rears its head, and the question of how you use AI responsibly isn’t one purely for IT but for your organisation’s executive leadership. Here we consider how to benefit from AI, while remaining true to your organisation’s values, obligations and stakeholders.  

Much is written and spoken of AI’s power to drive business transformation, efficiency and innovation. But as the saying goes, ‘with great power comes great responsibility’.

Using AI responsibly isn’t just about regulatory compliance. It’s about trust, safeguarding reputation, and ensuring that AI strengthens rather than undermines the organisation’s values and purpose. There are three important topics to consider – People, Planet, and Policy.

People

Let’s start by confronting the really big question: jobs. We’ve all seen and heard carefully worded references to AI’s labour-saving capabilities. It does less work, it means fewer workers, but does that also mean redundancies? This question needs considering, carefully, at a very senior level, and very early on.

They’ll need to know the expected time savings and whether these affect fractions of roles or entire roles. You should also consider timeframes in each area, and how these compare to natural attrition, retirement and contract expiration timescales. They’ll also need to know recruitment pipelines, so hiring can be slowed or redirected rather than abruptly frozen, as well as redeployment opportunities and the skills required for new or expanded roles such as AI oversight, data literacy, and creative problem-solving.

This will impact much of what follows.      

Enablement, not displacement

Responsible AI should augment human judgement and not replace it – freeing people from repetitive work. But to achieve this, it needs to be accompanied byreskilling and digital literacy programmes that enable employees to work effectively with AI systems. Success should be measured in terms of human productivity and satisfaction, and not headcount reduction.

Transparent and ethical

Everyone involved with AI, from developers to decision-makers, must understand what AI can and cannot do. Build a culture of AI literacy and ethical awareness supported by specific training on responsible data use, bias awareness, and explainability. Employees using AI outputs should be able to interpret and justify its decisions, especially in regulated sectors. Staff must appreciate that humans remain accountable for AI-assisted outcomes and feel confident challenging algorithmic decisions without recrimination.

Inclusion and fairness

Similarly, fairness and inclusion must be embedded in your use of AI. These systems will typically maintain or increase any biases in training data, so utilise diverse teams in AI design and validation. Train models with diverse data sets and monitor for bias, especially in HR, credit, or customer-facing use cases.Treat governance of AI fairness with the importance of a workplace equality and diversity issue, rather than a technical issue.

Planet

AI’s benefits should also be considered in the context of its environmental impact and sustainability. During training AI models can consume significant energy, and operationally AI infrastructure has a significant carbon footprint. But with the right actions, this can be mitigated.

Opt for energy-efficient architectures

Data centres powered by renewable energy, with liquid cooling, and using energy-optimised GPUs (Graphic Processing Units) and ASICs (Application-Specific Integrated Circuits) are more energy efficient. Also consider scheduling AI workloads to optimise power use.

Actively manage your technology lifecycle

Using cloud and hybrid models can allow you to dynamically scale, without having an over-provisioned on-premises infrastructure. Apply sustainability principles to AI hardware: responsibly sourcing, refurbishing and/or reusing, and recycling at end-of-life.

Use AI for sustainability

‘Planet’ doesn’t just mean mitigating AI’s environment impact. AI can also make a positive contribution towards meeting corporate sustainability goals through data-driven energy optimisation, intelligent logistics routing that lowers emissions, predictive maintenance to reduce waste, and carbon accounting.

Policy

A responsible use of AI also depends on robust governance that ensures transparency, accountability, and compliance. A key consideration for the board is who will be accountable for AI ethics and compliance, and how governance can be shown to be effective?

A best practice approach combines collective ownership with clear executive accountability. It is likely to blend existing structures with some new, specialised capabilities. This might take the form of a Chief Information Officer or Chief Digital/Technology Officer with primary accountability, working with a cross-functional AI Governance Board. This would include Technology, Data, HR/People, Legal, Compliance, Risk, Operations, your ESG (Environmental, Social, and Governance) team, and business unit leaders.

This will provide the basis for effectively actioning the following. 

Establish an AI governance framework

Determine the principles which will guide your use of AI. These need to be consistent with your organisation’s values and risk appetite and will typically encompass fairness, transparency, accountability, privacy, and sustainability. Bear in mind that different contexts may require different ethical considerations – what’s appropriate in one area may not be in another. AI ethics will touch IT, legal, HR and compliance so ensure that there is clear ownership within and across these areas.

Control and oversight

Integrate AI risk management into existing risk frameworks, with a focus on model validation, auditability, explainability, and version control. Track who built which model, with what data, and how it is performing. Require human-in-the-loop oversight for all critical decision and systems.

Regulatory alignment

There will be external interest in your AI use from regulators, customers, investors and other stakeholders, so aim to stay ahead of expectation. There is an EU AI Act, with most provisions applying from August 2026, and a UK AI Assurance Framework. The Information Commissioner’s Office has provided AI guidance, with sector-specific guidance expected in several areas (like from the FCA in financial services). Maintain audit trails for AI models, data lineage, and decision logic to satisfy auditors and regulators.

But, above all, be transparent about how AI is used, governed, and improved.

A final thought

Using AI responsibly requires deliberate, pre-emptive leadership. It means ensuring that AI use aligns with organisational purpose, is trusted by employees and other stakeholders, and contributes to sustainable growth. Many will do this badly, but those that do it well can successfully position their organisations as trustworthy and responsible innovators.

Cloud Direct can help you successfully benefit from AI in a real and responsible way. Request a call with a subject matter experts through the form below.

Cloud tracking and optimisation often slip to the bottom of the IT to do list, especially in the midst of daily firefighting and urgent fixes. But when you are managing a complex Azure environment, operating without visibility is like driving at night without headlights.

You must consider whether it’s worth exposing your business to unnecessary risk. Without clear data, decisions about your digital strategy become guesswork. That guesswork often leads to wasted spend, compliance concerns, and missed opportunities. 

Cloud visibility matters for cost and compliance (and your sanity) 

Visibility impacts vital outcomes for organisations, including: 

• Financial Control:  Continuous cost management keeps you financially competitive and allows you to make thoughtful decisions. It is crucial to identify underutilised resources and right-size virtual machines to stop unnecessary spending. While this is true, tracking every penny spent is difficult, especially with numerous systems and reports to analyse. Although, the dream of real time visibility to proactively monitor spend across all licenses and resources might be closer than you think. 

• Governance and Compliance: A strong security posture is essential in today’s cyber landscape with AI advancing at an unprecedented rate. Ensuring your environment is fully secure on all fronts is crucial, but not straightforward. Gaining visibility can be pivotal here for maintaining robust governance and compliance across your Azure estate. Visibility enables you to continuously monitor for policy violations, misconfigurations, and unauthorised changes, reducing the risk of data breaches and regulatory penalties. 

• Drive Efficiency: Sifting through multiple reports and dashboards to find the information you need is draining your time and resources. But it’s not just you – many businesses are rife with fragmented data, making manual investigation a necessary chore. The cure is a centralised platform where you can gain actionable insights instantly and free up your team to focus on more strategic initiatives. When you can quickly pinpoint underperforming services or areas for improvement, overall business productivity is boosted. It’s about empowering your team with the right information at the right moment so you can deliver greater results. 

Gain control of Azure and end the admin nightmare 

Now wondering where you can find this one-stop-shop for your Azure environment? That is exactly what the Provide™ Portal delivers. It is a centralised platform that provides all the Azure visibility you need in one place. But it also goes far beyond basic Azure reporting to provide you with actionable recommendations and optimisations, including: 

• Cost and License Management:  Set budgets and receive alerts before you hit unplanned expenditure, track spend across all M365 subscriptions and Azure resources instantly, and better plan for the future with forecasted spend outlook. This proactive approach is aligned to Microsoft’s Well Architected Framework (WAF) and helps you avoid surprises and keep your cloud costs predictable. 

• Monitor Security Posture:  Track cloud compliance levels, identify misconfigurations, and review risk exposure across your environment – as well as access to your Microsoft Secure Score to understand your current secure posture and how to improve it. With real time alerts, you can address vulnerabilities before they become serious threats. 

• Performance Metrics: Observe the health and efficiency of your running services to maintain optimal speeds and availability. This ensures your applications deliver the experience your users expect. 

• Sustainability Goals: Visibility even extends to tracking CO₂ emissions within your Azure usage. If your organisation has committed to strong sustainability goals, this sometimes overlooked metric helps align your cloud strategy with environmental targets. 

Real time data means no more manual reports. Instead of tracking down and dissecting last month’s costs, your monthly review becomes a proactive planning session focused on optimisation and growth. 

Beyond the Portal to expert optimisation reports 

Cloud Direct CSP+ enhances the Provide™ Portal with expert oversight. Regular optimisation reports will deliver personalised improvement suggestions on cost, security, and performance. Higher tiers also include direct access to cloud architects to support your future strategy and ambitions. This is the difference between simply having data and having expert insight applied to act strategically with that data. 

The combination of cutting-edge technology and certified expert review ensures your cloud environment is continually optimised and you extract maximum value from your investment. Plus, you can save money on your Azure spending. 

Ready to take control of your Azure environment? 

Stop guessing and start making informed decisions with real time visibility. Try our CSP+ calculator to find the right plan for your business.

If you’re an IT manager, you know how managing support tickets can feel like a second job. You spend hours juggling internal requests and chasing updates while waiting for service providers to resolve complex issues. It is frustrating and it wastes time – but there is a better way.

The real cost of slow Azure support  

• Productivity loss: According to a recent study, employees spend an average of 6 hours per month waiting for IT issue resolution. Employees who report long IT support delays also state the negative effects on morale and job satisfaction.  

• Financial impact: Gartner estimates that IT downtime costs businesses an average of £4,400 per minute for critical systems. Even smaller outages can accumulate tens of thousands in lost revenue. 

• Reputation damage: Slow IT queues can harm customer experience and lead to public complaints. Studies have shown that organisations with support ticket backlogs report lower customer satisfaction scores.  
• Security exposure: Delays in patching or fixing access issues leave doors open for attackers. A single missed update can lead to compliance breaches or data loss. 

Think about what happens when delays drag on: 

• Employee productivity stalls: When staff can’t access critical resources, deadlines slip and payroll pounds go to waste. 

• Customer confidence erodes: If the issue impacts customer-facing services, trust evaporates fast. 

• Innovation freezes: Instead of driving projects forward, your IT team is stuck firefighting. 

You have a capable IT team, but when a complex Azure problem pops up, they need expert help fast.

CSP+ advances your cloud support

Many businesses assume premium Azure support is too expensive or that switching providers is a hassle. That is why we created Cloud Direct CSP+ – to make expert Azure support simple and accessible. 

We integrate support directly into your Azure consumption model, and allow you to choose the tier that fits your needs. 

• Essentials: Monday to Friday standard business hour support for response and resolution of platform issues. In addition, access to an Azure Expert MSP partner for escalations. 

• Enhanced: 24×7 enhanced support with reduced SLA’s and expert human support. Direct escalations to Microsoft through our specialist team. 

• Enterprise: 24×7 enhanced support and direct escalations to Microsoft. Plus, direct access to Tier 4 Cloud Engineers who know your environment and can fix issues fast. 

This means no more ticket queues. No generic helpdesk. Just immediate access to the right expert when you need them.  

Why Enterprise tier changes everything 

Have you ever had a critical app go down on Friday afternoon? How long did it take to get help? Imagine this… instead of waiting days for a ticket to be picked up, you are on a call with a Tier 4 Azure engineer who knows your architecture and can resolve the issue in hours, not days. That is the difference CSP+ makes. It gives your IT team the freedom to stop firefighting and start innovating.  

Unlock your team’s potential 

CSP+ means embedding an expert support team into your business. That means fewer delays and more time for strategic work that drives growth. 

Here’s what your IT team could focus on if they weren’t stuck in support queues: 

• Cloud optimisation: Fine-tuning workloads for cost efficiency and performance. 

• Security hardening: Implementing advanced threat protection and compliance frameworks. 

• Automation projects: Building workflows to eliminate manual processes. 

• Innovation initiatives: Deploying new apps, migrating legacy systems, and enabling AI-driven solutions. 

Instead of firefighting, your team can finally deliver the projects that transform your business. 

Ready to eliminate downtime?

Key takeaways from the Microsoft Digital Defence Report, written by Leon Godwin

We drew inspiration from the Churchill War Rooms to host our latest Security Briefing – a venue where strategic defence decisions once shaped our history, and now where security professionals learned from Cloud Direct and Microsoft about the new cyber landscape being shaped by AI-driven threats. 

To paraphrase Winston Churchill: “Never before in the field of digital defence has the security of so many relied so heavily on the vigilance of so few.” The battleground consists of intelligence, speed, and resilience, and adversaries are using AI-powered attacks to rapidly infiltrate and compromise organisations, faster than human-based defences can respond. 

From a day in the life of a modern CISO through attack simulations, to insights from Microsoft’s Aileen Finlay and concrete steps that you can take to adjust to the new threats, I’ll reflect on the event and share my take on the newly released Microsoft Digital Defence Report 2025. 

The reality on the ground

On 13 October, the UK government took the unprecedented step of sending a letter out to all UK businesses to highlight the significance of new cyber threats. The letter’s goal was to fundamentally reclassify cyber security from a technical operational task to a critical board-level imperative. By issuing a direct mandate, the government signaled that the intense and sophisticated nature of modern threats now constitutes a primary risk to national economic stability.  

The Microsoft Digital Defence Report 

The recent release of the Microsoft Digital Defence Report makes it clear why the UK government is so concerned, and why you should be too. 

The threat landscape isn’t just evolving – it’s accelerating. Attacks are more aggressive, more organised, and frankly, more relentless than ever. The UK is now ranked number two in the global index of countries most impacted by cyber threats. 

Defence Report takeaways for the Modern CISO 

One theme that kept coming up during the event was the “prevention versus response” paradigm, or what the military calls “Left of Bang” and “Right of Bang.” The Microsoft Digital Defence Report 2025 makes it clear; you can’t choose one over the other. You need both. 

Here’s a breakdown of the key findings of the report, and actions to take off the back of it.  

1. Identity is the Battleground 

Problem: Attackers aren’t only breaking in, they’re logging in. Identity compromise is still the number one entry point for ransomware and data theft, and it’s getting smarter. When you login to a computer you gate a token that is your permission to use that session for a period of time before you need to reauthenticate. Token theft and Adversary-in-the-Middle (AiTM) attacks are on the rise, bypassing traditional protections. Your traditional Multi-Factor Authentication (MFA) that secured you for many years is now simply not enough. 

Solution: Phishing-resistant MFA is the gold standard. 

Action: 

• Audit your Entra ID environment today. 

• Enforce phishing-resistant MFA for everyone, especially admins. 

• Update legacy authentication protocols.

Impact: Phishing-resistant MFA blocks over 99% of unauthorised access attempts, according to the Microsoft report. If you do one thing this quarter, make it updating your systems from traditional MFA to phishing-resistant MFA. 

2. The Double-Edged Sword of AI 

Problem: AI isn’t just our friend, it’s the attacker’s too. They’re using it to craft convincing phishing lures, scale attacks, and even create deepfakes for fraud. 

Solution: We fight fire with fire. AI-driven defence can now contain breaches in seconds, suspending compromised accounts before a human is aware of an issue. This is helped further now that Microsoft Copilot has been bundled into the M365 E5 licenses, rather than an expensive bolt-on. 

Action: 

• Put an AI governance framework in place. ISO 42001 is a great starting point.  

• Deploy AI-powered tools like Copilot for Security, Microsoft Sentinel, and Defender XDR to automate detection and response. 

• You already have access to the phishing simulations within your M365 subscriptions, you should increase the schedule to be at least weekly. 

Impact: Moving from reactive to proactive defence shrinks dwell time, improves awareness, and limits the blast radius of an attack. 

3. Cyber Risk is Business Risk 

Problem: Too often, security is treated as an IT issue. But as we see in the examination of real-world breaches, it doesn’t just impact systems. It’s effecting revenue, supply chains and reputation. In one case this resulted in liquidation of the business and termination of it’s 700 employees.  

Solution: Security needs a seat at the boardroom table. 

Action: 

• Build reports with metrics that matter including, MFA coverage, patch latency, incident response times.  

• Run tabletop roleplaying exercises so your executive team knows what to do when, not if, the breach happens. 

Impact: A resilient culture means the business keeps moving, even when attackers try to stop it. 

What you can do next 

The MDDR 2025 isn’t just a collection of scary stats, it’s a wake-up call. 

If you’re planning your 2026 roadmap and wondering how to prioritise (or fund) these improvements, let’s talk. We can help secure funding for assessments to pinpoint your weakest links and help provide guidance on your security journey.  

Don’t wait for the breach to happen. Build resilience now.

Sign up to one of our Security Innovation consultancy sessions. These sessions are designed to help you with your specific business challenges  

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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Ready to strengthen your security posture?

The Microsoft Security Briefing: Data Defence and Governance

Join industry experts and peers to explore the latest strategies, tools, and real-world insights for protecting your organisation in today’s threat landscape.

Secure your spot

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.