AI is changing the once predictable economics of software use. Costs are now frequently linked to consumption, and this is creating new management and governance challenges. The answer isn’t to slow AI adoption, but to ensure costs are aligned to business value – and the solution can be found in FinOps.

AI is introducing a new pricing model

Historically, Microsoft licensing has largely been user-based, but AI is introducing a growing mix of consumption-based services alongside this.

The traditional model continues for Microsoft 365, including Microsoft 365 Copilot, which is characterised by an annual commitment linked to user numbers. Adding users increases costs linearly, so expenditure is predictable and you can could make an accurate prediction of next year’s spend now.

With AI, organisations typically pay for activity rather than people. The organisation pays for prompts, responses, tokens, compute, storage, orchestration, retrieval, and automation.

Examples include Azure OpenAI tokens, Copilot Studio message consumption, Azure AI Foundry model inference, Microsoft Fabric capacities, Azure AI Search, AI agents making API calls, vector search, and embeddings. It’s like the difference between paying a fixed monthly gym membership, versus paying for the time you’re actually in the gym.

How costs can unexpectedly spiral

AI differs from traditional software because usage itself drives spending. Organisations typically start small, but as use becomes widespread the resulting costs can be an unwelcome surprise.

With a handful of users making twenty prompts a day, costs are barely noticeable. Scale that to a thousand employees, or hundreds of AI agents operating around the clock, and consumption increases dramatically.

It wasn’t a conscious increase in spending – the organisation simply embraced AI. Costs aren’t rising because something has gone wrong, but because adoption is successful.

Nevertheless, it creates a management problem.

Where costs spiral

There are four key areas where costs can soar.

Hidden usage growth

Employees start using AI for drafting, summarising, coding, meetings, and search. At this stage they’re probably using Microsoft 365 Copilot (which isn’t consumption based) and encouraged to experiment widely.

But more advanced services, such as Microsoft Copilot Studio, Power Automate, and Azure AI services often include consumption-based costs. While individual AI requests may cost very little, widespread experimentation and adoption can turn thousands of low-cost interactions into a significant operational expense.

Agent-driven consumption

This is probably the least understood issue. While human use tends to be limited to their working day, AI agents may be working around the clock – continuously monitoring inboxes, reviewing documents, triggering workflows, querying databases, and generating API calls.

One poorly designed agent can generate enormous consumption.

Lack of guardrails

Without the right governance in place, AI experimentation can soon become a production cost.

Established governance models aren’t usually AI appropriate. They don’t say who can – and with what approval – create agents, connect agents to data, use premium services, and deploy automations.

While you don’t want to stifle innovation, you do need governance.

Limited visibility

Perhaps the biggest issue is visibility. Most organisations know their Azure spend and the parts of the business that drive it, but few know which departments generated this month’s AI bill, which agents generated value, and which consumed resources unnecessarily.

The AI cost trap

The combination of hidden use, consumption pricing, insufficient governance, and poor visibility can feel like a huge cost trap.

But the trap isn’t high cost – it is invisible cost, or growing costs that are disconnected from growing value.

The issue isn’t that a department creates ten or twenty agents, but often that ownership is unclear, no one measures them, and no one retires non-performers. A year on, consumption continues and nobody knows why.

You probably recognise that this is exactly what happened with early cloud deployments. The answer wasn’t to slow adoption, but to improve management. Organisations adopted FinOps to provide greater cost visibility, clearer accountability, stronger governance and continuous optimisation to align spend with business value.

AI FinOps: 11 practical ways to keep control

If you’re unfamiliar with the term, FinOps is a contraction of Financial Operations. It is both a cultural practice and operational framework that brings together IT, operational teams, and finance to manage and optimise spending. Its aim isn’t simply to reduce cost, but to maximise business value by balancing speed, cost, and quality.

How might this work in practice? We’ll look at 11 practical mechanisms that can be put in place to support:

• Financial control
• Operational governance, and
• Business value realisation.

Remember: the objective isn’t to limit use of AI, but to ensure AI adoption remains predictable, measurable and aligned with business value.

Financial controls

1. Budgets: Set budgets for AI consumption at departmental and project level. These shouldn’t necessarily be hard limits, but they should be thresholds that trigger investigation if costs escalate. By configuring automated alerts you won’t have to wait for period-end to discover unexpected spending.

2. Usage dashboards: You can’t manage what you can’t see, so provide dashboards showing AI consumption by department, application, agent or workload, together with trends over time. Visibility makes it easier to spot unusual patterns, identify opportunities for optimisation and demonstrate value.

3. Chargeback or showback: It’s helpful if business units understand the financial impact of their AI usage. Some organisations chargeback costs directly to departments, while others report the costs without transferring budget (‘showback’). Both encourage more informed decision-making and discourage waste.

4. Portfolio reviews: Just as you regularly review software and other resources, you should proactively review your AI estate. Consider:

• Which AI services are producing the greatest business value?
• Are there duplicate AI agents solving the same problem?
• Are some workloads using more expensive models than necessary?
• Which production services should now be retired?

Guardrails and governance

5. Role-based access: Not everyone needs access to every AI capability – many may only need Microsoft 365 Copilot. Match access to need, with perhaps only selected power users able to build agents.

6. Premium model approval: AI platforms often offer a choice of models with different capabilities and costs and the most capable may not always be needed. Require approval before teams deploy premium or higher-cost models, especially where there are lower-cost alternatives. Both these measures are likely to build upon existing governance policies.

7. Agent lifecycle management: Like applications, agents shouldn’t exist indefinitely. Every agent should have an owner, a documented purpose and periodic review.

Otherwise, the organisation will accumulate ‘orphaned’ agents that consume resources long after their business value has disappeared.

8. Retiring unused agents: Many agents begin life as experiments, especially in the early days. While some will prove valuable, others will not. Regularly review agents and retire those that are no longer in use or delivering measurable benefit. Every unnecessary agent represents avoidable complexity and, possibly, ongoing cost.

Business value

9. Value measurement: This is probably the most important control of all. Don’t just measure consumption or spend, measure outcomes. Consumption and cost are only meaningful relative to business benefit. Examples include hours saved, reduced waiting times, improved customer satisfaction, faster response times, and fewer manual processes.

10. Departmental ownership: Establish this simple principle: no AI workload without a business owner. Every department deploying agents should understand what they’re spending, why, and the benefits expected in return.

11. Education: Just as important as controls is employee education. Well-informed users will make better decisions, improving both cost efficiency and governance. Ensure that employees understand:

• when AI should be used
• which tools are approved
• key policies and their reasons
• how different services incur costs, and
• why using AI efficiently matters.

Looking ahead

Historically, IT has managed infrastructure, licences, and devices. but increasingly IT will need to govern AI consumption. Not because it is a danger, but because it is becoming an operational utility much like electricity or cloud compute. Every prompt, every agent and every workflow may have a marginal cost, and although individually these costs are tiny, collectively they can become significant.

The organisations that thrive won’t necessarily be those spending the most on AI, they’ll be the ones spending smartest. They will understand exactly where AI is creating value and where it isn’t, characterised by:

• intentional, considered use of AI
• predictable, scalable adoption, and
• clear visibility of cost versus impact.

The organisations that develop these capabilities now will be able to scale AI confidently, sustainably and with far fewer surprises.

How Cloud Direct can help

Cloud Direct helps organisations put these principles into practice, with advisory, optimisation, and management services. Request a call with a subject matter expert, or view our AI Productivity services, to understand how Cloud Direct can help you support AI adoption with the right controls.

By Darren Taylor, Head of Service Design

A year ago, you may have been wondering how AI will change IT managed services. Now, you know that it will. The key questions are how quickly, how deeply and how that changes relationships with managed services partners (MSPs)?

Managed Services have focused on stability

Traditionally, managed services clients wanted stability and reliability. Clients valued predictable operations, repeatable processes, standardisation, reduced risk of change, and fixed support models. MSPs therefore focused on minimising disruption, maintaining uptime, process discipline, operational consistency, ticket resolution metrics, and SLAs.  

All of this meant a measured rate of change driven by periodic platform migrations, annual refresh cycles and quarterly reviews.   

This contrasts sharply with AI-era expectations. 

AI is accelerating operational change

You know from your own experience that AI is developing quickly – and much faster than enterprise IT operational models. 

Within Microsoft’s AI ecosystem we’re seeing continuous evolution:  

• Copilot capabilities changing monthly 

• new agents and automations emerging 

• developing governance controls 

• rapid feature rollout across Microsoft 365. 

We also know that many employees are no longer waiting for IT to introduce AI – see our blog Shadow AI: Why the Rapid Increase and What IT Should Do Next. 

Whereas historically IT drove new technology introductions, now we’re seeing employees independently discovering AI tools and creating automations.  

This changes the client-MSP dynamic.  

In the AI era static support models will become unsustainable.  

AI is changing expectations of IT support

We’re becoming more accustomed to Copilot-style interactions and natural language requests, along with AI-assisted productivity. As a result, traditional support experiences can begin to feel slow or outdated. 

Regardless of whether you handle first-line support in-house or through an MSP, your users are expecting: 

• greater immediacy 

• conversational interfaces 

• intelligent assistance 

• self-service and 

• proactive support. 

And not just ticket queues.  

This adds to the pressure for MSPs to improve support delivery with modernised workflows, knowledge management, and automation.  

AI is changing the tempo of IT operations, and not just the tooling. 

Boundaries between support, automation, and consultancy are blurring 

Traditionally, support, projects, consultancy, and automation have been distinct disciplines, and in many organisations still are. But as the AI era develops organisations will need faster operational evolution.  

Support will need to become more consultative, broadening the role of your MSP to include:  

• automating operational tasks 

• continuously optimising workflows  

• recommending governance improvements 

• building AI-assisted processes 

• helping govern employee-built agents. 

This is a fundamental shift from simply managing infrastructure, to managing continuous operational change. 

And it calls into question the very essence of traditional MSP relationships.  

Static service models will be increasingly divergent from need

We’re already seeing that AI changes environments too quickly for traditional approaches to remain fully effective. Approaches that are characterised by rigid operational models, fixed, often inflexible, scopes, and static service catalogues.  

AI capabilities are evolving much faster than contract cycles. The rapid changes we’re seeing with data governance, Purview, AI policies and compliance underline this. 

New operational requirements and opportunities are emerging all the time and MSPs will need to be much more adaptive. At the moment we might consider the following:  

• AI-assisted ticket triage 

• workflow automation 

• intelligent reporting 

• predictive remediation. 

But in a few months, this may have evolved so MSPs need to be capable of being highly adaptive.  

To be of value your MSP needs to be your ‘Operational Evolution Partner’ 

AI is changing the way organisations and people work. To be of value to their clients MSPs need to change the way they work.  

Instead of maintaining static environments, MSPs need to be working with their clients to enable operational evolution.   

This requires MSPs to help: 

• evaluating changes 

• operationalising changes 

• governing them and 

• safely pacing change.  

It requires MSPs to have the knowledge to enable clients to achieve and maintain AI-enabled operational maturity.  

Governance is central 

Where governance considerations were once peripheral, with AI they become central.  

Faster operational change creates greater demands on governance – and with AI the emphasis needs to be on safe operation rather than restriction. 

There are big questions around the governance of:  

• employee-built agents 

• AI-assisted workflows 

• data exposure and 

• automation governance. 

Additionally, your Microsoft environment is evolving unusually quickly. We’re seeing repeated changes to Copilot, Fabric, Purview, Defender, Sentinel, Entra, agentic capabilities, Power Platform many with governance implications.  

MSP’s will need to have the operational experience of managing these changes.   

Questions to ask an MSP 

Ask an MSP about how they use and manage AI. Consider:    

• Operational adaptability – How do you evolve operations? 

• AI governance – How do you govern AI use within your organisation? 

• Automation strategy – Which operational processes are already AI-assisted? 

• Human oversight – Where does human validation remain mandatory? 

• Visibility – How do you monitor shadow AI or unofficial automation? 

• Commercial evolution – How does AI alter your service model? 

• Microsoft readiness – How do you assess and operationalise new Microsoft AI capabilities? 

A different commercial model 

Historically, ticket volume has been the basis of managed services commercial relationships. Value has been directly linked to human effort.  

But AI can automate repetitive operational work. For clients there is clear value in faster, more efficient incident resolution. But if AI reduces effort, should service costs reduce too? If operational AI transformation requires investment, who should fund that? 

Whereas traditional IT projects have been a very clear transformation from here to there. In the AI era, customers expect MSP relationships to support continuous operational evolution of platforms and services. This requires a more flexible commercial model, perhaps based on shared rewards. 

The human element becomes more, not less, important! 

AI promises significant productivity gains, so it’s perhaps counterintuitive to suggest that the human element will become more important.  
 

But as AI accelerates, governance, prioritisation, business judgement, trust, pacing, and risk management will become more important. So, while the future may bring less human involvement it will demand more strategic human involvement. 

The most valuable MSPs will be those that continuously help their clients adapt safely, rather than just operating efficiently. 

At Cloud Direct we talk of ‘human-led, AI-powered, outcomes that matter.’ 

Take the next step

If your MSP isn’t built for continuous change, it’s time to challenge it!

Use the form below to speak to a Cloud Direct expert.

For the last two years, the AI conversation has been dominated by copilots, chat assistants, and increasingly powerful language models. The demos have been impressive and the pilots have been promising, but for many organisations, the reality has felt underwhelming. 

At best, AI has often behaved like a highly sophisticated Q&A machine. Useful? Absolutely. Transformational? Not always. 

The challenge has never really been intelligence. Today’s AI models are already incredibly capable. The real challenge is context. 

Microsoft Build 2026 brought that shift into focus. Microsoft’s vision, which they’re calling Microsoft IQ, moves beyond model capability and towards context. Rather than focusing solely on model capability, it introduces a unified context layer that helps AI understand how work actually happens, what business data means, and what it should or shouldn’t have access to. 

The result is a meaningful shift in how organisations should think about AI adoption. 

Why are AI projects struggling to deliver value? 

We’re all familiar with the fact that AI can summarise meetings, draft emails, analyse documents and answer questions. However, when asked to support complex business processes, connect information across teams, or make informed recommendations, it frequently falls short. 

This is because AI often lacks three critical forms of context: 

• An understanding of how work happens across the organisation 

• An understanding of what business data actually means 

• An understanding of organisational rules, policies and permissions 

Without these foundations, AI remains disconnected from the reality of the business. Microsoft IQ is designed to close that gap. 

What is Microsoft IQ? 

Microsoft IQ is Microsoft’s vision for providing enterprise AI with organisational context. 

Rather than treating AI as a standalone assistant, it creates a foundation that helps AI understand the people, processes, data and governance structures that sit behind the business. 

Right now, Microsoft IQ has three context layers: 

• Work IQ 

• Fabric IQ 

• Foundry IQ 

Who benefits most from Microsoft IQ? 

The organisations likely to benefit most from Microsoft IQ are those looking to move beyond basic AI use cases. 

The real opportunity lies in helping AI support decision-making, operational processes and end-to-end workflows. 

Teams that will benefit most include: 

• Sales teams managing complex customer relationships 

• Project teams coordinating multiple stakeholders 

• Operational teams handling large volumes of information 

• Business analysts working with organisational data 

• Leadership teams making strategic decisions 

In reality, almost every knowledge worker can benefit when AI understands the context surrounding their work. 

How Work IQ helps AI understand work 

One of the most important components of Microsoft IQ is Work IQ. Think of it as the organisational memory of your business. 

Work IQ helps AI understand how work actually happens by connecting information from meetings, conversations, emails, documents and collaboration tools. 

Instead of simply locating information, Work IQ helps AI understand: 

• Who owns a project 

• Which stakeholders are involved 

• Where decisions were made 

• What actions are outstanding 

• How teams collaborate 

Rather than acting like a search engine, AI starts behaving more like a knowledgeable colleague who understands the wider context surrounding a task. 

How Fabric IQ gives AI business context 

Fabric IQ helps AI understand business data. Traditional AI can access data, but often lacks an understanding of what that data represents. 

For example, an AI model might see a figure of £500,000 without understanding whether that number represents revenue, cost, profit, budget or forecast. 

Fabric IQ adds business meaning to data. 

It connects AI to: 

• Business metrics 

• Semantic models 

• Organisational definitions 

• Data relationships 

• Performance indicators 

This enables AI to understand not only what the data says, but why it matters. Instead of simply reporting figures, AI can interpret performance against business objectives and provide more meaningful insights. 

How Foundry IQ keeps AI secure and governed 

The third pillar of Microsoft IQ is Foundry IQ, which focuses on governance, security and organisational knowledge. 

Enterprise AI must operate within clear boundaries. It needs to understand what information exists, who can access it, and how it should behave. 

Foundry IQ helps ground AI in: 

• Internal policies 

• Governance frameworks 

• Security controls 

• Compliance requirements 

• Organisational knowledge 

Without this layer, AI introduces risk. With it, organisations can deploy AI with greater confidence and trust. 

How Work IQ, Fabric IQ and Foundry IQ work together 

The real value of Microsoft IQ emerges when these context layers operate together. Imagine a scenario where regional sales performance suddenly drops below target. A traditional AI assistant might flag the decline and produce a report, but an AI agent powered by Microsoft IQ goes much further. 

Work IQ brings in the human context to surface the teams, projects and conversations connected to the issue. 

Fabric IQ layers in business context, analysing performance data against sales targets and broader objectives. 

At the same time, Foundry IQ ensures everything operates within the right guardrails, limiting access to authorised data and enforcing organisational policies. 

The result is not just a summary, but a recommendation grounded in real context. Instead of presenting disconnected insights, AI combines human, business and governance context into a single, coherent view that’s far more useful for decision-making. 

When should organisations start preparing?

The short and simple answer is now. 

Not because Microsoft IQ is the latest technology trend, but because context is rapidly becoming the foundation of successful AI adoption. 

 Most organisations already have access to powerful AI tools. The next challenge is getting consistent value from them at scale. 

That requires organisations to focus on: 

• Data quality 

• Information architecture 

• Governance 

• Security permissions 

• Knowledge management 

• Clear business processes 

The organisations that invest in these foundations today will be best positioned to take advantage of the next generation of AI capabilities. 

Is your organisation ready for context-aware AI? 

As you move beyond experimentation and towards AI at scale, success will increasingly depend on the quality of your data, governance and business context. 

If you’re exploring Microsoft Copilot, AI agents, Microsoft Fabric, or preparing your organisation for the next wave of AI innovation, getting those foundations right is what makes the difference. 

We spend a lot of time with organisations who’ve already rolled out Copilot or started experimenting with AI, but aren’t seeing the value they expected. 

If you’re in that position, we can help you work through it. Contact us below.  

AI is already reshaping how organisations work — but most are still only scratching the surface. While tools like Copilot have improved individual productivity, the real shift is happening now: AI is moving from assisting tasks to executing work.

This guide explores how AI agents are enabling organisations to move beyond experimentation and into operational impact. Whether you’re driving change within your team or influencing wider strategy, it will help you understand where agents create value, and how to apply them in a practical, scalable way.

Built for those championing innovation from within, this is your starting point for turning AI ambition into measurable outcomes.

Inside the guide:

• What agentic AI is — and how it differs from traditional AI and copilots
• Where AI agents are already delivering value across sales, marketing, finance, HR, IT and more
• How organisations are moving from isolated use cases to end-to-end transformation
• A practical, 7-step framework for successfully designing, deploying, and scaling AI agents
• The role of data, governance, and people in making AI agent initiatives succeed
• What it means to work alongside AI agents — and how roles are evolving
• How to identify high-impact opportunities and avoid common adoption pitfalls

Download the guide to understand not just what AI agents are — but how to make them work in the real world.

Shadow AI is the AI-era evolution of ‘Shadow IT’, only more virulent. We consider how widespread it is, the difficulties of tracking it, the risks, and what you can do about it.  

With just a browser and a personal account, employees can introduce powerful technology into the workplace: no need for procurement approval, no infrastructure needs, no formal development cycle.  

What is Shadow AI? 

Shadow AI is the unauthorised use of AI tools and services by employees without IT approval, oversight, or knowledge. It’s much more than just using ChatGPT in place of search, and includes employee-built automations, AI-assisted coding, and summarisation tools 

The term ‘Shadow AI’ suggests something akin to ‘Shadow IT’, but there are fundamental differences: 

• it’s often instantly accessible with no installation or set-up 

• it can be rapidly adopted 

• it almost always involves use of your data 

• it carries much higher operational risk, including output inaccuracies and irreversible data exposure, and 

• with very low employee awareness of the issues it brings. 

In many organisations, employees are already using unsanctioned AI to summarise documents, draft content, analyse data, automate workflows, and even build simple AI agents. 

How widespread is Shadow AI?

Shadow AI appears to be a widespread problem. In October last year, Microsoft released research stating that 71% of UK employees have used unapproved AI tools at work, with 51% doing so every week. These are staggering numbers. While these figures may have included a lot of entry level AI use, that was last year. In the world of AI things are developing quickly. By now, reality is likely to be way ahead of anything a point in time survey can tell us.    

So, how widespread is it in your organisation?  

You probably know that employees are already using unsanctioned AI at work and it’s easy to see why: 

• AI tools are incredibly accessible 
  An employee can start using AI in minutes rather than months. The tools are often free, or low cost, and instantly accessible via a browser.  

• Employees can see immediate productivity gains 
  People are quick to realise that AI can make their life easier – there’s a big incentive to experiment with drafting, summarisation, analysis, automation, coding and research. 

• There are no restrictions in place 
  In most organisations, Governance is still catching up. They are still assessing risk, and don’t have mature AI policies, and don’t have approved tools.  

But if you don’t know what sort of AI is being used, where, and for what purposes that’s not unusual.  

Can we see what’s happening? 

Traditional IT visibility was not designed to deal with the world we now find ourselves in. 

While Microsoft is building a comprehensive shadow AI visibility and governance stack, its capability is distributed across several products.  

For example, Microsoft Defender for Cloud Apps can show which AI apps are being used on managed devices, identifying ChatGPT, Claude, Gemini, Perplexity and many other services. As well as usage levels, traffic volumes, and risk scores. Tie this with Purview Data Security Posture Management for AI and you can have real insight into what sensitive information is being put into AI services and how they are being used. 

These tools do come with limitations though such as: 

• unmanaged devices 

• reliance on Microsoft Defender for Endpoint 

• reliance on the use of supported browsers such as Microsoft Edge 

Regardless of what sellers might want you to believe, right now there isn’t a tool that can give you a comprehensive view of Shadow AI.  

Which raises the question, ‘what are the business risks if you ignore this?’ 

The real risks of Shadow AI

Shadow AI is no longer hypothetical. You’re probably already aware of some of the risks – it’s partly why you’re reading this blog – but they’re worth stating.  

1 Data leakage  
Probably the biggest concern is sensitive data being pasted into public AI tools. Examples include customer data, employee records, financial information, and intellectual property.  

2 Compliance and regulatory exposure 
Data leaks go hand in hand with compliance issues potentially around GDPR, retention policies, auditability, and regulatory breaches.  

3 Uncontrolled agents and automation 
Employee created workflows, automations, and decision-making agents are unlikely to be accompanied by the correct level of testing, governance, resilience, and oversight. 

4 AI-generated inaccuracies 
While you’re aware of the dangers of AI hallucinating, misinterpreting, and producing incorrect outputs, are employees? Without governance, and education, bad outputs may enter operational processes. 

5 Fragmentation and duplication 
Without oversight there’s a real risk of different individuals and teams adopting different tools, creating overlapping agents, and duplicating effort. Causing inconsistency, inefficiency, and IT management and support problems.  

So, should you restrict AI or enable it?  

Can we block Shadow AI use?

Blocking AI is unrealistic. Increasingly, AI adoption is coming from employees themselves who value the productivity gains offered. Bans will simply drive usage underground.  

It’s important therefore to recognise that Shadow AI is often a symptom, rather than the problem. Employees want to work faster, but in the absence of adequate, sanctioned tools they are finding their own. Use of Shadow AI is evidence of unmet demand. 

So, if you can’t block use of AI and you recognise the dangers of uncontrolled adoption what should you do?  

What should you do? 

Perhaps the easiest part of enablement is the technology. Much of what you need is already available through Microsoft 365 Copilot, Microsoft Copilot Studio, and Microsoft Power Automate.  

The bigger questions relate to process, security and governance:  

• How do you prevent sensitive data leakage?  

• What policies should we implement? 

• How do we avoid stifling innovation?  

• What sort of employee enablement is effective? 

• How do we govern employee-built agents? 

Our recent blogs From AI Anxiety to AI Action: Your Route to Agent Readiness and How to Enable DIY AI Agents: Governance, Tools, and Education offer lots of practical tips.  

While a good partner can save you reinventing the wheel by providing guidance on what works and how to implement it.   

This is also where a Microsoft 365 Copilot Readiness Assessment will, crucially, help fast-track adoption with: 

• An objective, documented view of readiness 

• Identification of data exposure, access controls and governance gaps  

• Prioritised, actionable recommendations. 

Discover how Cloud Direct can help you combat Shadow AI with appealing, approved, and well governed AI by requesting a call using the form below.

Employee use of AI agents is inevitable. But for those in senior IT roles, the challenge is not whether to adopt AI, but how to do it safely and effectively. We examine the key issues and show how a well-structured assessment can accelerate AI readiness.  

Do you see the strategic importance of AI, feel pressurised to ‘do something’, but remain unsure how to operationalise it safely and at scale? If so, you’re not alone.  

Although IT hesitancy reflects a real and valid awareness of the risks and inherent ambiguities, the pressures to act will only increase.  

Not a normal rollout 

Part of the challenge is that AI isn’t just another technology rollout – in many ways it’s different:  

• Employees can access and synthesise vast amounts of organisational data 

• Non-developers can create their own automations and agents 

• Outputs are dynamic, not deterministic, and 

• Value comes from thousands of small improvements rather than one big system. 

This changes the way we need to govern and manage the software, as well as the way employees use it – increasingly they will create and delegate work to it.   

Why Agentic AI is daunting

Talking to IT leaders the same concerns come up repeatedly: data risks, governance needs, unclear readiness, and pressure to act.  

There are several very real areas of uncertainty that can easily become barriers to action.  

Security and data risks 

Tools like Microsoft 365 Copilot have the capability to surface organisational data in new ways, raising questions around data accessibility and visibility:  

• What can Copilot access? 

• Will sensitive data leak? 

• Is our permissions model strong enough? 

Governance shortcomings 

Unlike traditional IT systems, agentic AI brings with it non-deterministic outputs, employee-built agents, and dynamic workflows. This is prompting IT leaders to ask:  

• Who owns these agents? 

• What’s allowed and what’s not? 

• How do we audit decisions? 

Most existing governance models haven’t anticipated these sorts of questions.   

The shift to ‘citizen AI development’ 

Tools like Microsoft Copilot Studio allow non-developers to build agents. While this creates huge opportunity, it lessens central control and it raises questions around how this should be handled.  

Some have likened it to the early anxiety around Power Platform adoption, but this comparison risks underplaying the potentially far greater consequences. 

Who is responsible for AI? 

While AI technologies typically fall under IT’s remit, AI as a strategic initiative may not. Should it be driven by Digital, by the business, or shared? If so, how should ownership be structured? How should adoption be scaled? 

ROI is hard to quantify 

Where budgets are linked to ROI, at least initially, the returns can be hard to quantify. The benefits are likely to be widely dispersed, gains are incremental, and the real value comes from many small improvements.  

With so many unanswered questions and no clear starting point, there’s a danger of inactivity. 

Gaining the confidence to press ‘Go’ 

For many, the fundamental problem isn’t one of technology. Most organisations already have the foundations in place with Microsoft 365, SharePoint and Teams data, identity and access controls, and sound security and governance. While these may need building on, it’s not the technology that’s holding IT back: it’s a lack of clarity: 

• Where are the risks? 

• What is needed to safely proceed? 

• What needs fixing first? 

• How do we justify investment and timing? 

This is where a structured readiness assessment matters.  

How a Microsoft 365 Copilot Readiness Assessment will help 

A well-designed Copilot readiness assessment will provide clarity and move you from uncertainty to informed action. 

Done properly, it will deliver five critical outcomes: 

1. Reduced adoption risk 
   By identifying where data exposure, access controls or governance gaps exist, these can be proactively addressed. 

2. An objective, documented view of readiness 
   It provides an evidence-based assessment of Copilot readiness, to enable confident go/no-go or phased rollout decisions. It will also help to combat ill-considered demands to ‘just do it’.  

3. Prioritised, actionable recommendations 
   Not everything needs fixing, or fixing now. The assessment focuses effort on the areas that will have the greatest impact on risk reduction and value realisation. 

4. Alignment of IT, security, and the business 
   Through structured discovery and analysis, it creates a shared understanding, reducing stakeholder friction and accelerating decision-making. 

5. Clarity without an open-ended commitment 
   Delivered as a fixed-price, time-bound engagement with clear governance and sign-off, it gives meaningful outcomes without launching a large, undefined programme. 

The risks of delay 

A recent MIT report reveals that over 90% of employees are already using gateway AI tools like ChatGPT.  Although only 40% of companies had purchased licenses.   

Increasingly, employees will have friends that are progressing beyond this to use generative AI to improve their work. 

The implication is clear: if you don’t provide approved tools, guidance, and safe environments, employees will explore external AI tools and build unmanaged solutions.  

It’s critical that the organisation ensures AI use happens in the right way.  

Regaining the initiative 

Successful organisations aren’t treating AI as a single project, but as the introduction of a capability to be progressively developed and scaled. 

That starts with: 

• Understanding your current state 

• Developing a clear view of what needs to be addressed 

• Enabling early use cases, and 

• Building confidence across the organisation. 

A structured readiness assessment doesn’t just tell you where you are — it gives you a defensible, practical path forward. 

And right now, that’s exactly what many IT leaders need. 

Learn more about Cloud Direct’s Microsoft 365 Copilot Readiness Assessment. 

Request a call with one of our experts using the form below, and find out how we can remove AI uncertainty and provide you with a clear route to adoption.

AI agents are quickly becoming the digital teammates we never knew we needed. If you’re not sure where to get started, we’ve put together this A–Z Agent Guide to spark your inspiration and show just how versatile agents can be across roles, teams, and industries. From practical, high impact helpers to your personal agony aunts, we’ve got it all covered.  

You haven’t begun using agents? No problem, we have recently written a blog on how to build your own. It includes key considerations to ensure you are building agents that are not only increasing your productivity, but that are compliant and grounded within your organisation’s policies.

Whether you’re just starting your AI journey or looking to scale your agent strategy, use this guide to discover what’s possible.

The AI Agent A-Z Index

A – Analyst Agent 
Your always in-the-know agent. The Analyst Agent can pull data from across your systems, spot patterns, and serve up clear summaries so you can make decisions based on evidence, not instinct alone. This is a great one to have on hand when a senior leader asks for your latest project results.    

B – Branding Agent
The guardian of your brand. Whether you want to ensure your personal brand stays consistent or get through company brand reviews with less feedback, this agent reviews copy and visuals for tone and style, suggesting tweaks so every asset is aligned.

C – Career Coach Agent
Your personal development partner. It can help you map skills to roles, suggest training paths, and provide tailored guidance to help you excel. Use this agent when preparing for performance reviews and potentially secure your next promotion.

D – Data Visualiser Agent
The spreadsheet specialist sidekick. It turns complex datasets into intuitive charts, dashboards, and infographics that stakeholders can understand at a glance.

E – Executive Summary Agent
The TL;DR star. This agent turns long reports, meeting notes, or research papers into concise, high‑impact summaries tailored for decision‑makers. Whether you would like to gain insights from an industry report in a hurry or need to condense information for a company presentation, this agent has your back.

F – Financial Forecaster Agent
Your numbers navigator. This agent builds and updates financial models, forecasts revenue and costs, and highlights variances so finance and business leaders stay aligned.

G – Governance Agent
The rules-and-guardrails specialist. It monitors how agents and tools are being used, checks activity against your policies, and can alert you when something looks offside.

H – HR Management Agent
A digital HR partner for employees and managers. It can answer policy questions, help you find useful employee documents, and guide managers through tricky employee processes.

I – Inventory Management Agent
Your real-time stock scout. It tracks inventory levels, predicts reorder points, and can even draft purchase orders to prevent stockouts or over-ordering. If you manage the merchandise cupboard or employee devices, this agent will give you the availability lowdown in an instant.

J – Justification Agent
Your persuasion partner. When you’re preparing business cases, this agent pulls relevant data, benchmarks, and risks into a clear rationale you can use to impress and quickly gain buy-in from business leaders. You will never go into a meeting unprepared again.

K – Keyword Agent
Your SEO and search whisperer. It generates keyword lists, clusters terms by intent, and suggests optimised copy so your content gets found at the right time by the right people.

L – Language Agent
A multilingual wordsmith. It translates, localises, and rephrases content while keeping your tone of voice consistent across regions and audiences. Your content will never get lost in translation with this agent.

M – Market Reporter Agent
Your always-on market analyst. It scans news, reports, and competitor activity, then summarises implications so you stay ahead of shifts in your industry and sector.

N – Negotiation Coach Agent
A pocket-size negotiation trainer. It helps you plan negotiation strategies, role-play conversations, and suggest talking points and trade-offs before you step into the room.

O – Onboarding Agent
The friendly first-week buddy. The HR or recruiting team can create this agent to help ensure new starters have everything they need to succeed from the beginning. It can walk new employees through key tools, people, and processes, answering common questions.

P – Prompt Coach Agent
Your prompt architect. It teaches you and your teams how to ask better questions and refine prompts, so every agent you use understands you better and performs better. It can also save you time as you’ll receive a more appropriate response faster, rather than trying to get the right response with multiple prompts.

Q – Quality Control Agent
The detail checker. If you’ve got an important presentation coming up and need to ensure no faults can be picked up, this is the agent for you. It reviews content, data, and documents for accuracy, consistency, and compliance with your standards making sure nothing slips.

R – Researcher Agent
A tireless desk researcher. It gathers information from internal and external sources, organises it into structured notes, and highlights key insights and gaps. The good news for Microsoft 365 Copilot users, is the Copilot Research Agent is already configured and ready to use.

S – Survey Agent
Your feedback collector. It designs surveys, suggests questions, analyses responses, and summarises sentiment so you can quickly act on what customers or employees are telling you.

T – Trend Spotter Agent
Your opportunity scout. What’s in vogue? This is the agent that knows. It monitors behaviours, content, and performance over time to spot emerging trends. This can help you get ahead by moving from reactive to proactive.

U – UX Agent
Your user-experience expert. It reviews online journeys, suggests improvements, and summarises user feedback to help teams design smoother, more intuitive experiences for both current customers and new prospects.

V – Vendor Directory Agent
The supplier-savvy one. It maintains an up-to-date view of vendors, contracts, and performance metrics, and can recommend the best-fit supplier for any given need.

W – Writer Agent
Your copy partner. From emails and social posts to proposals and blog drafts, it helps you get from blank page to first draft in minutes. Top tip: when setting up this agent make sure to provide it with a tone of voice using previous materials you have created so that it sounds more like you.

X – Xtra Pair of Hands Agent
A catch-all helper for the everyday grind. From filing notes and summarising meetings to chasing actions and tidying documents, this agent picks up the small tasks that eat up your day. It can even just be a sounding board for when you’re unsure. If there is one agent to rely on, this would be it.

Y – Year-in-Review Agent
Your annual storyteller. It pulls together performance metrics, milestones, customer quotes, and highlights into polished “year in review” summaries for leadership, board, or all-hands meetings.

Z – Zero-Inbox Agent
The inbox declutterer. It categorises, summarises, and drafts replies so you can tame email overload and get closer to that mythical “Inbox Zero”.

In Summary

Hopefully this A–Z Agent Guide has sparked your imagination and shown just how many ways AI agents can support and elevate the work you do. We hope this has inspired your thinking about agents, but don’t feel like you need to use them all at once. Often, just one thoughtfully chosen agent can make a meaningful difference across a team or workflow.

Ready to take the next step?

If you’d like to understand how to implement AI agents effectively across your organisation, from ensuring you have the right data infrastructure in place to identifying high‑value use cases, our experts are here to help. Reach out to the team using the form below.

Or to find out more about our AI agent offering, check out our Copilot Landing Zone Accelerator.