Solutions / AI & Business Automation
AI & Business Automation
AI & Business Automation Solutions
AI assistants and automation platforms can save time on the right tasks. Start with what you are working on, see the main paths side by side and, where we list a suitable option, go to the provider when you are ready.
- Start from your situationFive business contexts
- Same criteria throughoutEight dimensions for every option
- Trade-offs up frontWhat to watch for, stated plainly
Which area are you working on?
Choose the closest match. Each context sets out the questions to ask, the main solution paths and, where we list one, a suitable option.
Automating repetitive work
Handing repeatable tasks to software, with the right amount of control.
Building internal tools and workflows
Getting a purpose-built tool that off-the-shelf software does not quite cover.
Working with documents, content and knowledge
Drafting, summarising, sorting and recording information with AI help.
Handling repetitive customer interactions
Answering common customer questions quickly, with a route to a person.
Managing AI use across the organisation
Deciding what may be entered into AI tools and which tools people use.
Find the solution that fits
Work through the context that matches yours. For the full decision, read the AI & Business Automation guide.
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Automating repetitive work
In most organisations a surprising amount of time goes on work that follows the same pattern each time: copying details from an enquiry into a spreadsheet, sorting messages, summarising documents, sending the same follow-up. Some of it can be handed to software, and what kind depends on the work. If the steps never vary, simple rules are enough. If it involves reading, judging or drafting, an AI agent may help, with a person checking the result. Some businesses want it to run on infrastructure they control.
More about this context
In most organisations a surprising amount of time goes on work that follows the same pattern each time: copying details from an enquiry into a spreadsheet, sorting incoming messages, summarising documents, moving information between two tools that do not talk to each other, sending the same follow-up. Some of that can be handed to software.
What kind of software depends on the work. If the steps never vary, simple rules are enough. If the work involves reading, judging or drafting, an AI agent may help, with a person checking the result. And some businesses want the automation to run on infrastructure they control. This section is for working out which of those fits.
How the paths differ
There are three broad routes. Rules-based automation connects tools with fixed “if this, then that” logic and is predictable. Managed no-code AI automation lets people build AI agents and workflows visually on a hosted platform, so there are no servers to run. Self-hosted workflow automation runs on your own server, which gives control over where data goes and what it connects to, with the maintenance that implies.
Supporting detail
Questions to ask yourself. 4 questions to settle first
- Which tasks repeat, and how much of each is judgement rather than fixed steps?
- Should a person, a rule or an AI agent do each step?
- Who will build and maintain the automations?
- Does the work need to run on infrastructure you control?
Why this decision matters. What is at stake
Automation is attractive because the saving repeats, but the wrong tool creates its own work: rules that break when inputs vary, AI output nobody reviews, or a self-hosted system nobody maintains. Starting from the task, how much judgement it needs, who will build and maintain the automation and how sensitive the data is avoids most of that.
Before choosing. A short checklist
- Decide first whether a rule, an AI agent or a person should do each step
- Start with one task that repeats often and is easy to check
- Check what data the automation will touch and where it is processed
- Decide who owns the automation after it is built, and what happens if it fails
- Check plan limits and usage-based charges on hosted platforms
Want the full background? Decide first: rules, AI or a person
Choose the path that fits
Select a path to see why it fits and what to watch for.
Rules-based automation
Fixed “if this, then that” rules that move information between tools, without AI.
Best fit
- Steps that never vary
- Rules you can state exactly
Main thing to check
Fixed rules break when inputs vary
Managed no-code AI automation
A hosted platform where you design automations or AI agents visually.
Best fit
- Business users building it
- No server to maintain
Main thing to check
You depend on the platform’s features and plans
Self-hosted workflow automation
A workflow tool you install and run on your own server.
Best fit
- Technical teams
- Data-location or privacy needs
Main thing to check
You maintain, secure and back it up
Rules-based automation
Why choose it
- Predictable and easy to check
Best when
- The steps are always the same
- You can state the rule exactly
Watch for
- Fixed rules break when inputs vary
- Decide first whether a rule, AI or a person should do the step
Full decision detail
Why it helps
- Predictable and easy to check
Fits when
- The steps are always the same
- You can state the rule exactly
Watch for
- Fixed rules break when inputs vary
- Decide first whether a rule, AI or a person should do the step
Fixed “if this, then that” rules that move information between tools, without AI.
Rules-based automation is the right starting point for work that is genuinely repetitive and predictable: when a form is submitted, add a row, notify a person, create a task. It is transparent and easy to check. It struggles when inputs differ from what the rule expects, and it cannot read or judge free text.
When you may need more. When the task needs reading, summarising or deciding rather than moving fixed fields, an AI-assisted approach is more suitable.
Managed no-code AI automation
Why choose it
- Automations go live without any server to set up or maintain
- Business users can design them visually, without writing code
- Repetitive tasks stop depending on someone doing them by hand
Best when
- Speed of setup matters more than control of the infrastructure
- Business users, rather than developers, will build the automations
Watch for
- You depend on the provider's platform: its features, limits and plans
Full decision detail
Why it helps
- Automations go live without any server to set up or maintain
- Business users can design them visually, without writing code
- Repetitive tasks stop depending on someone doing them by hand
Fits when
- Speed of setup matters more than control of the infrastructure
- Business users, rather than developers, will build the automations
- You do not want to maintain a server
Watch for
- You depend on the provider's platform: its features, limits and plans
A hosted platform where you design automations or AI agents visually. The provider runs the infrastructure; you design the workflow.
A managed no-code platform is for people who want to build an AI agent or an automated workflow without writing code or running servers. You design the steps visually, connect the tools you use, and the platform runs it. It is quick to start and puts the building within reach of operations and business teams. You depend on the platform’s features, limits and plans, and anything an agent produces still needs checking.
When you may need more. If data must stay on your own infrastructure, or you need to run automation entirely under your control, a self-hosted route is the alternative.
Need automations to run on infrastructure you control? See self-hosted workflow automation
Self-hosted workflow automation
Why choose it
- You decide where automations run and what they connect to
- Data stays on infrastructure you control
- It sits comfortably alongside other tools you already run yourself
Best when
- You want control over where automations run and what they connect to
- You have the technical capability to maintain a server
Watch for
- You are responsible for updates, security and backups of the server and the tool
Full decision detail
Why it helps
- You decide where automations run and what they connect to
- Data stays on infrastructure you control
- It sits comfortably alongside other tools you already run yourself
Fits when
- You want control over where automations run and what they connect to
- You have the technical capability to maintain a server
- You already run, or want to run, your own tools
Watch for
- You are responsible for updates, security and backups of the server and the tool
A workflow automation tool you install and run on your own server. You get control and flexibility, and you take on responsibility for running and maintaining it.
Self-hosted workflow automation means installing an automation tool on a server you control. It suits technical teams, and businesses with data-location or privacy requirements, who want to decide where automations run and what they connect to. The trade-off is operational: someone has to update it, secure it and keep it backed up.
When you may need more. If nobody on the team wants to maintain a server, a managed platform is the lower-maintenance option.
Prefer not to manage infrastructure? See managed no-code AI automation
Compare the main paths
Show the comparison. The paths side by side
| What to look at | Rules-based | Managed no-code AI | Self-hosted |
|---|---|---|---|
| What it handles | Predictable steps, such as adding a row or notifying a person when a form is submitted | Reading, judging or drafting, with a person checking the result | Workflows you want to run under your own control |
| Best suited to | Steps that never vary; transparent and easy to check | Business users building visually; speed over control of infrastructure | Technical teams, and data-location or privacy requirements |
| What you take on | Rules that struggle when inputs differ | Dependence on the platform’s features, limits and plans | Updates, security and backups of the server and the tool |
| Main thing to check | Decide first whether a rule, AI or a person should do each step | Anything an agent produces still needs checking | Whether you can maintain a server |
Building internal tools and workflows
Sometimes the software a team needs does not exist in quite the right shape: a dashboard that pulls two data sources together, an intake form that feeds a workflow, a small tool for a task the business does in an unusual way. Off-the-shelf products come close but not close enough, and a conventional development project feels heavy. Many teams prototype with an AI app builder and decide later whether conventional development is needed.
More about this context
Sometimes the software a team needs does not exist in quite the right shape: a dashboard that pulls two data sources together, an intake form that feeds a workflow, a small tool for a task the business does in an unusual way. Off-the-shelf products come close but not close enough, and a conventional development project feels heavy for something this specific.
This section is about getting a purpose-built internal tool or workflow: when to build, how it can be built, and what you take on once it exists.
How the paths differ
Two routes cover most cases. AI app builders let someone describe the tool in everyday language and generate a working first version, which suits prototypes, internal dashboards and small utilities. Conventional development, in-house or with an agency, takes longer and costs more but gives full control over design, data and hosting, and is the safer route for complex, long-lived or sensitive systems. Many teams prototype with the first and decide later whether the second is needed.
Supporting detail
Questions to ask yourself. 4 questions to settle first
- What exactly should the tool do, and for whom?
- Is there off-the-shelf software that already does most of it?
- Who will maintain it once it exists?
- How sensitive is the data it will handle?
Why this decision matters. What is at stake
A tool that fits the process exactly can remove workarounds, but a tool someone built once and nobody maintains becomes one more thing to rely on blindly. The decision is less about how fast a first version appears and more about who maintains it, who owns the code and the data, and how sensitive the information it handles is.
Before choosing. A short checklist
- Describe exactly what the tool must do, and for whom, before choosing how to build it
- Check where it will be hosted and who owns the code and the data
- Decide who maintains it, and what happens if its builder is unavailable
- Test it with real examples before relying on it
- Check how usage is charged if the builder works on credits or limits
Want the full background? The tool classes
Choose the path that fits
Select a path to see why it fits and what to watch for.
AI app and internal-tool builder
Describe the tool in everyday language and AI agents design, build and deploy it.
Best fit
- Internal tools and dashboards
- A quick first version
Main thing to check
Test it, and know who owns the code
Conventional development, in-house or with an agency
Developers design, build, test and maintain the tool, in-house or at an agency.
Best fit
- Complex or long-lived tools
- Sensitive data
Main thing to check
Higher cost and a longer time to a working version
AI app and internal-tool builder
Why choose it
- A working first version comes from a plain-language description
- It can bring a purpose-built tool into use without starting a conventional development project
Best when
- You need a purpose-built internal tool, dashboard or small app
- You want a working first version quickly
Watch for
- The output still has to be tested like any software
- Know where it is hosted and who owns the code
Full decision detail
Why it helps
- A working first version comes from a plain-language description
- It can bring a purpose-built tool into use without starting a conventional development project
Fits when
- You need a purpose-built internal tool, dashboard or small app
- You want a working first version quickly
- You do not want to begin from a conventional development stack
A platform where you describe what you want in everyday language and AI agents design, build and deploy the application.
An AI app builder generates an application from a plain-language description and lets you refine it by conversation. It can bring a purpose-built tool into use without a conventional development project, which is why it suits internal tools, dashboards and early versions of ideas. The output still has to be tested like any software, and you need to know where it is hosted, who owns the code and what happens when you want to change it.
When you may need more. If the tool is complex, must integrate deeply with existing systems or handles highly sensitive data, conventional development gives more control.
Conventional development, in-house or with an agency
Why choose it
- Full control over design, data and hosting
Best when
- The tool is complex, long-lived or handles sensitive data
- You have developers or a dependable agency
Watch for
- Higher cost and a longer time to a working version
- You carry maintenance, security and hosting
Full decision detail
Why it helps
- Full control over design, data and hosting
Fits when
- The tool is complex, long-lived or handles sensitive data
- You have developers or a dependable agency
Watch for
- Higher cost and a longer time to a working version
- You carry maintenance, security and hosting
A custom tool built by developers on a conventional technology stack.
Conventional development means developers design, build, test and maintain the tool, in your team or at an agency. It takes longer and costs more at the start, and it gives control over design, data handling and hosting, with a clear owner for maintenance.
When you may need more. For a simple internal tool that is mostly a form and a dashboard, the full effort may not be justified; a builder or an off-the-shelf product can be enough.
Compare the main paths
Show the comparison. The paths side by side
| What to look at | AI app builder | Conventional development |
|---|---|---|
| How it is built | Described in everyday language; AI builds and deploys it | Developers build, test and maintain it |
| Best suited to | Prototypes, internal dashboards and small utilities | Complex, long-lived or sensitive systems |
| Control | Check where it is hosted and who owns the code | Full control over design, data and hosting |
| Cost and time | A working first version quickly | Takes longer and costs more at the start |
| Main thing to check | Test it like any software | You carry maintenance, security and hosting |
Working with documents, content and knowledge
AI tools can draft, rewrite, summarise and sort content quickly. Their output is a draft: it can be wrong, inconsistent or made up, and what you put in can leak. AI assistants and writing tools help with drafting and summarising, used through a business account with a person reviewing the output. Meeting-notes and transcription tools involve other people’s voices and often confidential discussion, so consent and retention matter.
More about this context
AI tools can draft, rewrite, summarise and sort content quickly. Their output is a draft: it can be wrong, inconsistent or made up, and what you put in can leak.
AI tools can draft, rewrite, summarise and sort content quickly. Their output is a draft: it can be wrong, inconsistent or made up, and what you put in can leak.
How the paths differ
AI assistants and writing tools help with drafting and summarising, used through a business account with a person reviewing the output. Meeting-notes and transcription tools record, transcribe and summarise meetings, and involve other people’s voices and often confidential discussion, so consent and retention matter.
Supporting detail
Questions to ask yourself. 4 questions to settle first
- What will you use it for: drafting, summarising, sorting, meeting notes?
- Who will review everything before it reaches a customer or a decision?
- What data may be entered into the tool?
- Is it a business account with admin controls?
Why this decision matters. What is at stake
AI tools can draft, rewrite, summarise and sort content quickly, but their output is a draft: it can be wrong, inconsistent or made up, and what you put in can leak. A person reviewing the output and clear rules on data make the difference.
Before choosing. A short checklist
- Confident errors: review before use
- Sensitive data pasted into prompts
- Check what is stored, who can see it and whether it trains models
- Use business accounts with multifactor sign-in and admin controls, not personal ones
Want the full background? Before you put business data into an AI tool
Choose the path that fits
Select a path to see why it fits and what to watch for.
AI assistants and writing tools
General AI assistants and writing tools, used through a business account with a person reviewing.
Best fit
- Drafting and summarising
- Messy, unstructured input
Main thing to check
Confident errors: review before use
Meeting notes and transcription
Tools that record, transcribe and summarise meetings.
Best fit
- Notes from meetings or calls
- Consent from participants
Main thing to check
Recording consent and retention
AI assistants and writing tools
Why choose it
- Faster first drafts
- They cope with messy or unstructured input
- A person stays in charge of what is sent
Best when
- The task involves language, documents or judgement where fixed rules break down
- Someone can review everything before it reaches a customer or a decision
Watch for
- Confident errors: review before use
- Sensitive data pasted into prompts
Full decision detail
Why it helps
- Faster first drafts
- They cope with messy or unstructured input
- A person stays in charge of what is sent
Fits when
- The task involves language, documents or judgement where fixed rules break down
- Someone can review everything before it reaches a customer or a decision
- You have rules on what data may be entered
Watch for
- Confident errors: review before use
- Sensitive data pasted into prompts
- Check what is stored, who can see it and whether it trains models
- Use business accounts with multifactor sign-in and admin controls, not personal ones
General AI assistants and writing tools, used through a business account with a person reviewing the output.
Meeting notes and transcription
Why choose it
- Notes and summaries without manual note-taking
- A written record of each meeting
Best when
- You need notes from meetings or calls afterwards
- You can get consent from participants
Watch for
- Recording consent: every participant should know and agree
- Check where recordings are stored and how long they are kept
Full decision detail
Why it helps
- Notes and summaries without manual note-taking
- A written record of each meeting
Fits when
- You need notes from meetings or calls afterwards
- You can get consent from participants
- Confidential content is covered by clear retention rules
Watch for
- Recording consent: every participant should know and agree
- Check where recordings are stored and how long they are kept
- Confidential discussions: decide who can see the recordings
Meeting notes and transcription tools: they record, transcribe and summarise meetings.
Compare the main paths
Show the comparison. The paths side by side
| What to look at | AI assistants and writing tools | Meeting notes and transcription |
|---|---|---|
| What it does | Drafts, rewrites, summarises and sorts content | Records, transcribes and summarises meetings |
| Best suited to | Language, documents or judgement where fixed rules break down | Notes from meetings or calls, with participants’ consent |
| Main thing to check | Sensitive data pasted into prompts; what is stored and who can see it | Recording consent; where recordings are stored and for how long |
No option listed yet
We’re currently reviewing suitable options for this area.
Handling repetitive customer interactions
Customers ask many of the same questions, and a quick, accurate answer helps both sides. The risk is a wrong answer presented as fact, or no route to a person. Chatbots and virtual assistants answer customer or staff questions from sources you provide. A self-service help centre lets customers find answers themselves. In both cases, keep answers current, make clear when it is a bot, and make it easy to reach a person.
More about this context
Customers ask many of the same questions and you want quick answers without losing the route to a person.
Customers ask many of the same questions, and a quick, accurate answer helps both sides. The risk is a wrong answer presented as fact, or no route to a person.
How the paths differ
Chatbots and virtual assistants answer customer or staff questions from sources you provide. A self-service help centre lets customers find answers themselves. In both cases, keep answers current, make clear when it is a bot, and make it easy to reach a person.
Supporting detail
Questions to ask yourself. 4 questions to settle first
- Which questions come up again and again?
- Do you have accurate sources for the answers?
- How easily can a person take over?
- How will you test it before launch?
Why this decision matters. What is at stake
Wrong answers presented as facts are the main risk of automated replies. Accurate sources, a clear signal that it is a bot and an easy route to a person decide whether it helps customers or annoys them.
Before choosing. A short checklist
- Wrong answers presented as facts are the main risk
- Make it clear that it is a bot
- Provide an easy route to a person
- Test it on real examples before launch
Want the full background? Decide first: rules, AI or a person
Choose the path that fits
Select a path to see why it fits and what to watch for.
Chatbots and virtual assistants
Answers customer or staff questions from sources you provide.
Best fit
- Many similar questions
- Accurate sources to answer from
Main thing to check
Wrong answers presented as facts
AI-assisted self-service support
A searchable set of answers customers can read without contacting you.
Best fit
- Many similar questions
- Answers written once
Main thing to check
Keep answers current
Chatbots and virtual assistants
Why choose it
- Common questions get an immediate answer
- Staff questions can be answered the same way
Best when
- Many customers ask similar questions
- You can give it accurate sources to answer from
Watch for
- Wrong answers presented as facts are the main risk
- Make it clear that it is a bot
Full decision detail
Why it helps
- Common questions get an immediate answer
- Staff questions can be answered the same way
Fits when
- Many customers ask similar questions
- You can give it accurate sources to answer from
- A person can take over easily
Watch for
- Wrong answers presented as facts are the main risk
- Make it clear that it is a bot
- Provide an easy route to a person
- Test it on real examples before launch
Chatbots and virtual assistants: they answer customer or staff questions.
AI-assisted self-service support
Why choose it
- Common questions are answered once, in one place
Best when
- Many customers ask similar questions
Watch for
- Answers must be kept current
- Make sure customers can still reach a person
Full decision detail
Why it helps
- Common questions are answered once, in one place
Fits when
- Many customers ask similar questions
Watch for
- Answers must be kept current
- Make sure customers can still reach a person
A searchable set of answers, with AI-assisted search or replies on top, that customers can use without contacting you. The general help desk and shared inbox sit under Customer Operations.
Compare the main paths
Show the comparison. The paths side by side
| What to look at | Chatbots and assistants | Self-service help centre |
|---|---|---|
| How it answers | A bot answers at once, from sources you provide | Customers search for answers themselves |
| Best suited to | Many similar questions, with a person who can take over | Many customers asking similar questions |
| Main thing to check | Wrong answers, being clear it is a bot, and an easy route to a person | Keeping answers current; customers can still reach a person |
No option listed yet
We’re currently reviewing suitable options for this area.
Managing AI use across the organisation
People are often already using AI tools, probably on personal accounts, and nobody has decided what may be entered into them. The most common incident is confidential data pasted into a tool nobody checked. A short written policy that people can read, with a list of approved tools and a clear statement of what may never be entered, comes first. Business accounts with multifactor sign-in and admin controls then give visibility.
More about this context
People are already using AI tools, probably on personal accounts, and nobody has decided what may be entered into them. The most common incident is confidential data pasted into a tool nobody checked.
People are often already using AI tools, probably on personal accounts, and nobody has decided what may be entered into them. The most common incident is confidential data pasted into a tool nobody checked.
How the paths differ
A short written policy that people can read, with a list of approved tools and a clear statement of what may never be entered, comes first. Business accounts with multifactor sign-in and admin controls then give visibility. Read what each provider does with inputs, and check business terms rather than consumer ones.
Supporting detail
Questions to ask yourself. 4 questions to settle first
- Which AI tools are people already using, and on which accounts?
- What must never be entered: customer data, contracts, credentials?
- What does each provider do with inputs?
- Do you have business terms and admin controls?
Why this decision matters. What is at stake
The most common AI incident is confidential data pasted into a tool nobody checked. A short written policy and business accounts give you visibility before something goes wrong.
Before choosing. A short checklist
- Decide what may never be entered, such as customer personal data, contracts and credentials
- Read what the provider does with inputs: whether data is stored, for how long, who can access it, and whether it is used to improve models
- Check business terms, not just consumer ones
- Check where data is processed
Want the full background? Before you put business data into an AI tool
Choose the path that fits
Select a path to see why it fits and what to watch for.
A short written AI policy
A short policy people can read: approved tools and what may never be entered.
Best fit
- AI already used informally
- Confidential data held
Main thing to check
A policy only works if people can follow it
Business accounts with admin controls
AI tools used through business accounts with multifactor sign-in and admin controls.
Best fit
- Rules people will follow
- Visibility for admins
Main thing to check
What the provider does with inputs
A short written AI policy
Why choose it
- People know which tools they may use
- Confidential data is protected before an incident
Best when
- AI use is already happening informally
- You hold customer personal data, contracts or credentials
Watch for
- A policy only works if people can follow it
Full decision detail
Why it helps
- People know which tools they may use
- Confidential data is protected before an incident
Fits when
- AI use is already happening informally
- You hold customer personal data, contracts or credentials
Watch for
- A policy only works if people can follow it
A short written policy that people can actually read, with a list of approved tools and a clear statement of what may never be entered.
Business accounts with admin controls
Why choose it
- Admin controls give you visibility
Best when
- You want rules people will actually follow
Watch for
- Read what the provider does with inputs, including whether data is used to improve models
- Check business terms, not just consumer ones, and where data is processed
Full decision detail
Why it helps
- Admin controls give you visibility
Fits when
- You want rules people will actually follow
Watch for
- Read what the provider does with inputs, including whether data is used to improve models
- Check business terms, not just consumer ones, and where data is processed
AI tools used through business accounts with multifactor sign-in and admin controls, instead of personal accounts.
How the two fit together
Show the comparison. The paths side by side
| What to look at | Written AI policy | Business accounts |
|---|---|---|
| What it is | A written policy and a list of approved tools | Business accounts with multifactor sign-in and admin controls |
| What it gives you | People know which tools they may use | Admin controls give you visibility |
| Main thing to check | People have to be able to follow it | What the provider does with inputs; business terms and where data is processed |
No option listed yet
We’re currently reviewing suitable options for this area.
Want to know what sits behind each option? See how we evaluate AI and automation tools