AI is everywhere.
It is in boardroom conversations, software roadmaps, vendor pitches and social media feeds. Leaders are under pressure to “do something with AI”, but many are still asking the same basic question:
Where do we actually start?
The challenge is rarely lack of interest. It is knowing where AI can create real value. The market moves quickly, the terminology changes, and it is easy to jump to tools before agreeing the problem.
Start with problems, not tools.
Look for areas where work is slow, repetitive, hard to scale, overly manual or dependent on people searching, summarising, rewriting or chasing information. That is usually where AI can add the most value first.
Most businesses do not need an AI moonshot. They need a few clear use cases that save time, improve decisions or remove manual effort.
In this first Mavents insight, we have picked five practical starting points that are relevant across many organisations. They are not the only use cases for AI, but they are some of the most accessible and commercially useful places to begin.
Most of the examples below are based on generative AI. In simple terms, that means AI systems that can understand language, generate content, summarise information and interact with people more naturally. Tools like Microsoft Copilot, ChatGPT, Claude and Google Gemini sit in this category, as do many of the assistants now appearing in business applications.
We will come back to the underlying technologies and tools in more detail in future insights. For now, the key point is simple: the value comes from how AI is applied to real work, not from using AI for its own sake.
1. Personal and team productivity
For many organisations, this is the easiest place to start.
A large amount of day-to-day work is made up of drafting, summarising, planning, reviewing, searching, rewording and pulling information together. That applies whether you work in operations, sales, technology, HR, finance or customer service.
AI can help speed up the early stages of that work, especially drafting, summarising and structuring information.
It can draft emails, summarise meetings, turn notes into action lists, help structure presentations, create first drafts of documents, analyse spreadsheet content, support coding tasks and help people get started more quickly when they are facing a blank page.
That does not mean it replaces judgement. AI is usually strongest as an assistant, not a decision-maker. It can help create a first draft, but people still need to review, refine and challenge the result. That matters even more where the output is customer-facing, commercially sensitive or factually important.
This use case is attractive because the barrier to entry is low. It does not usually require a major systems programme to get started, and the benefits are easy for leaders to picture. Teams spend less time on admin-heavy work and more time on work that requires real expertise.
There are still important considerations.
The more context these tools have, the more useful they become. But giving them access to internal documents, emails or collaboration platforms introduces questions around data privacy, security, permissions and governance. There is also a training and adoption challenge. Good results do not come just from buying licences. People need clear guidance on how to use the tools well, when to trust them, when not to trust them, and what data they should and should not use.
In practice, the business value is often:
- time saved across knowledge work
- faster turnaround on routine outputs
- reduced admin burden
- better use of skilled staff time
- a low-friction way to build confidence with AI
Typical technologies and terms here include generative AI, large language models (LLMs), Microsoft Copilot, ChatGPT, Claude, Google Gemini, AI assistants and coding assistants.
This is also why personal productivity makes a strong bridge into the next insight in the series. One of the quickest starting points for AI is often the everyday productivity layer, but choosing the right tools and using them safely still needs thought.
2. Customer service and support
Customer service is one of the clearest areas where AI can improve both efficiency and experience.
Many organisations deal with high volumes of repetitive questions, inconsistent responses, long handling times and growing pressure on service teams. Even where good people are in place, the service model can still be slowed down by knowledge gaps, manual triage and too much time spent repeating the same work.
AI can help in several ways. It can support self-service through chatbots and virtual assistants. It can assist service agents by suggesting answers, summarising previous interactions, retrieving relevant knowledge articles and drafting responses. It can help classify requests, route them to the right team and reduce the effort involved in handling common issues.
Used well, this can improve response times, reduce avoidable demand and free up service teams to focus on more complex or sensitive interactions. It can also improve customer satisfaction when people get the right answer quickly and can reach a human easily when they need to.
Used badly, it creates frustration.
Customer service is one of the clearest examples of where AI should usually support people, not try to remove them entirely. Sensitive complaints, emotionally charged situations, vulnerable customers and cases requiring discretion still need human judgement. The goal is not to force every interaction through automation. The goal is to make routine service simpler and give people better support where human handling is still needed.
This use case also depends heavily on the quality of the underlying knowledge. If your policies are unclear, your content is outdated, or your service processes are inconsistent, AI will not magically fix that. It may simply surface bad information faster.
The key risks and design considerations usually include:
- whether the knowledge content is accurate and current
- how responses are controlled and assured
- whether the assistant reflects the right tone of voice
- how exceptions are escalated
- how customer data is protected
- where human review is required
In practical terms, the value often comes from:
- faster responses
- reduced workload on service teams
- more consistent answers
- better self-service for common queries
- improved customer experience without adding headcount
Common terms here include customer service AI, AI chatbot, virtual assistant, conversational AI, contact centre AI, agent assist and retrieval-augmented generation (RAG).
3. Document and workflow automation
This is where AI starts to move from individual productivity into operational improvement.
Many business processes still rely on people reading emails, extracting information from documents, rekeying data, routing requests, checking forms, chasing approvals and piecing together work across disconnected systems. These activities are often slow, inconsistent and difficult to scale.
AI can help by dealing with unstructured inputs that traditional automation struggles with.
For example, it can extract key information from forms, emails or attachments. It can classify incoming requests, suggest routing decisions, generate standard responses and support the handling of common variations or exceptions. Combined with workflow tools, business rules and system integration, that can significantly reduce manual effort.
This is also the area where terms like agentic AI often appear.
There is real value here, but it is worth being careful with the hype. Agentic AI is not magic. In most organisations, the best results come from combining AI with well-designed workflows, clear rules, strong controls and clean integration into existing systems. The process still needs ownership. Exceptions still need handling. Auditability still matters.
AI is not a substitute for a broken process.
If the underlying workflow is poorly defined, the ownership is unclear, or the data is unreliable, adding AI may simply increase complexity rather than remove it. This is why document and workflow automation often benefits from a more joined-up view across process design, architecture, data and governance.
The business value includes:
- reduced manual handling
- faster end-to-end processing
- fewer errors caused by rekeying or inconsistent interpretation
- better process visibility
- improved scalability without simply adding more people
Key technologies and terms include workflow automation, workflow orchestration, intelligent automation, robotic process automation (RPA), AI document processing, document AI, large language models (LLMs) and agentic AI.
For many organisations, this is where the real operational gains begin, but it is also where design discipline matters most.
4. Internal knowledge assistants
A lot of time is wasted in organisations simply trying to find what the business already knows.
Policies sit in one place, project documents in another, meeting notes somewhere else, and critical knowledge often lives in inboxes, Teams chats or the heads of experienced employees. The result is duplicated effort, slow onboarding and too much time spent searching rather than acting.
Internal knowledge assistants aim to solve that problem. They allow people to ask questions in natural language and retrieve relevant answers from internal sources such as SharePoint, Teams, document repositories, policies, proposals, manuals or knowledge bases. They can summarise long documents, point users to the most relevant source material and make internal information much easier to navigate.
There is some overlap here with the productivity use case, particularly where tools like Microsoft Copilot sit across documents, meetings, files and collaboration platforms. The difference is that this use case is more explicitly about helping people access trusted organisational knowledge, rather than just drafting or summarising content.
This can be a very strong use case because it solves a common, visible pain point: people cannot find information quickly enough, or they do not trust what they find.
As with customer service, quality matters. A knowledge assistant is only as good as the content behind it. If the source material is outdated, duplicated, poorly governed or inconsistently permissioned, the answers will be unreliable. Good access controls are also essential. An assistant should not surface information to the wrong people just because it is technically available somewhere in the estate.
The value often comes through:
- less time wasted searching
- faster onboarding for new starters
- more consistent answers to internal questions
- better use of existing knowledge assets
- improved decision support for operational and professional teams
Typical terms here include enterprise search, internal AI assistant, knowledge assistant, retrieval-augmented generation (RAG) and vector search.
This is one of the clearest examples of why data foundations and governance matter. The AI layer may be the visible part, but the quality of the outcome depends heavily on the structure, quality and control of the information underneath.
5. Sales, marketing and personalisation
This is a valuable use case, but one that tends to work best when the basics are already clear: who you are targeting, what you want to say, what data you trust and how leads or customers should be handled.
Sales and marketing teams often spend too much time creating content, drafting outreach, tailoring messages, following up leads and trying to segment audiences in a more meaningful way. AI can help increase capacity across all of those activities.
It can support campaign drafting, proposal creation, sales outreach preparation, lead qualification, content variation and product or content recommendations. In some contexts, it can also support personalisation, helping teams tailor messages or experiences based on customer behaviour, preferences or stage in the journey.
That can be commercially useful, particularly in retail, eCommerce and B2B services where speed, relevance and follow-up quality all matter.
But this is also an area where poor use of AI becomes obvious very quickly.
If the brand message is unclear, the customer data is weak, or the outreach process is already unfocused, AI will not solve the underlying problem. It may simply generate more content, more quickly, without making it any better. Over-automated messaging can also feel generic or impersonal, which weakens trust rather than building it.
This is why the best use cases in sales and marketing tend to be those where AI supports teams rather than running unchecked. It can help people do more with the same headcount, but the brand, process and data still need to be sound.
The value includes:
- faster campaign and content production
- better support for sales follow-up
- greater capacity without immediate headcount growth
- more relevant customer communications
- improved conversion support where the underlying process is already solid
Terms worth mentioning here include AI in sales, AI marketing tools, personalisation, recommendation engines, CRM AI, lead scoring and next best action.
For some businesses this will be a major area of value. For others it will come later, once the data, process and proposition are clearer.
How to choose a sensible starting point
If all of this sounds interesting but still broad, that is normal.
The right place to start is usually not the most impressive demo or the most talked-about tool. It is the use case where a few practical things come together:
- a clear business problem people already feel
- visible value if the work improves
- a process frequent enough for the improvement to matter
- enough data, process definition or content quality to make it work
- manageable risk and appropriate guardrails
- a realistic route into live business use
It is also worth remembering that the best first use case is not always the biggest one. Often, the smartest move is to start with something practical and learn from it. That builds confidence, surfaces delivery issues early and helps teams understand what good adoption really takes.
A final thought
These five use cases are not the whole AI picture. They are simply some of the most practical starting points for many organisations today.
There are many others we will return to in future insights, including reporting and decision support, anomaly detection, predictive analytics, forecasting, matching and classification, computer vision, optimisation and next-best-action models.
But if you are asking where to start, the answer is usually not “everywhere”. Start where AI can remove friction, save time, improve consistency or make better use of the information you already have. That is where momentum usually begins.
And in many organisations, one of the lowest-friction places to begin is with the productivity tools people are already hearing about. In our next insight, we will look more closely at that space, including tools such as ChatGPT, Claude and Microsoft Copilot, what they are good at, where they differ, and what businesses should consider before rolling them out more widely.