Copilot Studio agent cost calculator - a tool to help forecast costs

Agents are forming a big part of AI these days, bringing the promise of highly-capable assistants with gen AI intelligence and the ability to use tools, data, and apps in multi-step processes. One of Microsoft's big moves recently is to allow agents to be used in the free version of Copilot (Microsoft 365 Copilot chat) by any employee with a Microsoft 365 license - in other words, no need for the circa $30/£23 per user per month "full fat" Microsoft 365 Copilot license. In this approach, the AI usage is simply charged based on consumption in a Pay As You Go manner, thus avoiding the need to license the entire organisation with Microsoft 365 Copilot. This is interesting because it starts to make AI very useful (e.g. by having it understand company data and documents) without committing to the $30/£23 per user per month cost of M365 Copilot. 

But how much do you pay? In the Microsoft world, Copilot Studio agents will be the most popular since they can be created by business users without code - arguably it's a new form of Power Apps, but exposed prominently to all Microsoft 365 users. Other types of agents can be created in the Microsoft world - including declarative agents and custom engine agents - but these typically require pro developers and a more specialist skillset. We're focused on Copilot Studio agents in this article due to their likely popularity. As usual with AI, it pays to understand what costs are likely to be ahead of time - also as usual, the devil is in the detail and there are some surprises. In this post I share an Excel calculator which can help you forecast costs and outline some of the considerations.

Agent costs - and the relationship with Microsoft 365 Copilot

To avoid doubling up of costs, agents are largely free for any employees licensed with Microsoft 365 Copilot - you're already paying for full Copilot license for these folks after all, so most agent actions are free for these users. Not all actions however - here's a summary:

• Users without a M365 Copilot license - all actions within an agent count as 'consumption'
• Users with a M365 Copilot license - most actions are free, but the exception is 'autonomous actions' which use generative AI to make decisions on how to process

So even before we get to the detail, that's a wrinkle which adds some complexity to predicting costs. Will you (or the business) create agents which rely on autonomous actions? That's hard to say without getting close to use cases and agent design. Unfortunately there's no way to prevent this type of action being used, so even if your organisation licenses lots of users with full M365 Copilot you may still incur AI agent costs. See my footnote at the bottom of this article for more on this.  

For the purposes of simplicity, this article (and the linked Excel calculator) will focus on forecasting costs for users without a M365 Copilot license - in other words, the "free Microsoft 365 Copilot chat + Copilot Studio agents" combo. Lots of organisations are interested in this since organisational use cases get unlocked, potentially with a simpler investment case. If your organisation has lots of M365 Copilot licensed users and you think agents using autonomous actions will be created by the business (there's no way to stop this currently), layering in these additional costs onto my Excel would certainly be possible. 

Before we get to costs, let's think about agent flavours.

Types of agent and surface points 

There are lots of ways of categorising agents, but this classification based on what the agent is primarily there for is helpful:

A retrieval agent could help employees find answers from key documents or a knowledge base, whereas at the other end of the spectrum an autonomous agent could be tasked with competitor research and proposing new service lines for a company. At that level of complexity there's lots to think about of course, but it does illustrate the range of agents that can exist. The above capabilities can obviously be combined, and some agents will use all three elements of retrieval, task completion, and autonomy within a process.

In terms of surface points and user experience, Copilot Studio agents can be used to:

• Provide a standalone agent experience - for example, an employee onboarding agent embedded in a HR site or in Teams
• Extend Microsoft 365 Copilot - for example, to tailor behaviour so that questions about company policies are sourced from the HR handbook only

Both of those examples are essentially retrieval agents. 

Copilot Studio pricing and message burn rates 

Copilot Studio agent usage is charged in 'messages' based on complexity of the action. You have two ways of paying for these:

• Pay As You Go - no upfront commitment, $0.01 per message 
• Buy a message pack - 25,000 messages per month, also $0.01 per message

Regardless of how you pay, the key element is the 'burn rate' of different actions your agents may use. Microsoft publish the table below to explain how different agent actions consume messages - remember from the earlier note, employees without a Copilot license will be charged for all actions in this list, and even employees with a full M365 Copilot license will incur costs if autonomous actions are used:

April 2025 pricing update

Microsoft announced some significant price decreases for certain agent actions listed below - for example, 'tenant graph grounding' decreased from counting as 30 messages to 10. All of the info in this post and the linked Excel calculator has been updated with these changes.

 

So, there's no way around needing to understand (or guess) what your agents may do. To simplify things, one approach is to imagine a typical agent and the combination of actions it performs, then multiply it by how many such agents you anticipate the business to use. 

Let's look at some scenarios.

Calculating agent costs

We need some quick assumptions. I'm assuming that a typical conversation an employee has with an agent will perform:

• 3 info lookups, known as tenant graph grounding (i.e. to get the info from Microsoft 365,  whether that's a knowledge base, site, or set of documents, to answer the prompt/question)
• 4 usages of generative AI (i.e. to generate the response text)

You might disagree with these averages, but they are all editable in the calculator. My logic is that most conversations with AI are not single shot - it's common to ask a follow-up question, and if we say that happens around 3 times in a typical conversation I believe that's about right. We also need to consider how many conversations per day - in these default calculations, I go for 30 per day. Again, that could be highly variable for you (e.g. based on size of company, how many employees the agent is relevant to etc.) - all editable in the calculator, but I think 30 is reasonable for a useful agent in a medium/large business. 

Scenario 1 - 10 agents, 30 conversations per day

This scenario gives an annual cost of $30,096:

Scenario 2 - 30 agents, 30 conversations per day

"But Chris, we're probably not going to end up with just 10 agents across our entire business - it would be more than that in time." I'm hearing this frequently, so let's model that - costs obviously multiply by 3, giving a yearly total of $90,288:

Both of these costs typically come as a surprise. We can debate exactly what the right parameters should be for each business (and we should, because the inputs used will influence the costs heavily) - but clearly there is something significant to perform diligence on here, and I think it may not be apparent to most. Anyone thinking of using agents without "full" Copilot licensing across the board should be looking at this closely - but it won't necessarily come through in the Microsoft documentation, you need to do your own modelling.

Accessing the calculator

The calculator is a simple Excel file available for download here:

   Link to calculator

Closing thoughts 

The combo of "minimal M365 Copilot licensing + agents" is quite alluring for organisations where rolling out M365 Copilot to all employees doesn't fit, or those simply struggling with the business case. However, this alternative to full Copilot may not be as cost effective as anticipated - and for their part, Microsoft are clearly hoping that it can work as an on-ramp and tip organisations into making a more substantial investment in their premier AI tool. If not, that's probably fine too because revenue from PAYG usage may be just as beneficial and potentially carry a higher margin. I wouldn't say there's anything too cynical or mercenary from Microsoft here - every major SaaS vendor has a degree of PAYG pricing for AI consumption in their platform, since it brings flexibility and the avoidance of an upfront commitment.

Will agents drive more organisations to license M365 Copilot more broadly? I'd say yes - after all, once agents are in use and departments, teams, and individuals are all using them to get work done, good luck taking them away! Few leaders will be able to do that easily if agents are delivering the gen AI promise.

It also won't be easy to calculate the exact tipping point where it becomes worthwhile to switch to M365 Copilot licensing - that will all depend on your agent usage and number of employees being considered for licenses. Being able to implement cross-charging for AI consumption will also grow in importance, though for some businesses a cost will be a cost wherever it lands.

As ever, AI continues to raise complexities that need expertise on the team (whether in-house or with a partner) to help navigate the choices. This new world of "AI FinOps" is just one part of it - but there's no doubt organisations are realising gains from AI now, so the bigger long-term risk may come from not seeking to master these factors.

References:

• Copilot Studio licensing - Microsoft Copilot Studio
• Manage message capacity - Microsoft Copilot Studio

Footnote - M365 Copilot licensed users and autonomous actions in agents

I highlight in the article that even M365 Copilot licensed users are charged for autonomous actions in agents, though every other action type is free. I derive this from the Microsoft documentation where autonomous actions is omitted from the list of actions free for these users:

From Copilot Studio licensing - Microsoft Copilot Studio:

Autofill columns in Microsoft 365/SharePoint – comparing AI costs to alternatives

As detailed in the last article, “autofill” columns in SharePoint are a very interesting built-in AI capability in Microsoft 365 where LLM/GPT capabilities can be used very easily with your documents. Unlike Copilot, which can’t really be used in bulk across your documents because it’s driven by the end-user, autofill columns could be used to summarise, categorise, or extract info from hundreds or thousands of documents very easily indeed. Pseudo-automation approaches with Copilot do exist such as creating an agent, but not all employees will do that and autofill columns are perhaps the simplest way of tapping into AI to get some of the benefits. Today, autofill columns use GPT-4 Turbo as the LLM and we can expect Microsoft to continue upgrading as newer models become available. 

As a recap, I gave these examples in the last article:

• Document summaries – the full document summarised automatically in your preferred format (e.g. 3 bullet points, a few sentences etc.)
• Key takeaways – a summary focused not on the full document, but the conclusions only
• Key info extraction – identify the client/project/business unit this file relates to
• Key info extraction – list the people/organisations/concepts/[something else] mentioned in this document
• Classification – categorise the document, either generically or according to a list you provide (e.g. into “RFP”, “proposal”, “statement of work”)
• Automated assessment - based on the contents of the document, should this be approved or sent for human review?
• New info generation – based on the contents of the document, how does it correspond to X?

If more inspiration is useful, my last article shows the results of implementing a couple of these scenarios, showing the prompt used and what the AI did.

Costs - does this cost more, less, or the same as other AI tools I could use?

Experimenting with AI is generally cheap, but to use in real-world scenarios someone somewhere needs to understand the costs and pricing model closely. However, you’ll rarely see comparison tables on different AI approaches and respective costs published by vendors like Microsoft – you’ll see them for AI models, but rarely for overall approaches. It’s not that AI vendors are necessarily being disingenuous, more that there are a few variables involved such as whether your developer/team/partner has the skills to use different approaches – perhaps they can only use low-code or easy to consume AI for example and code development is out of the question.

But things do open up when you can plug into Azure OpenAI directly for LLM models for instance, because you avoid paying the “abstraction premium” for simpler approaches where Microsoft have done more of the work for you. It was ever thus as we say in England, and this is certainly the case for autofill columns.

Hit me with some numbers

Let’s make two comparisons with other ways you could consume AI, focusing on use cases like those above which align well with autofill columns:

• Syntex document understanding (for extraction or classification scenarios especially)
• A Copilot agent or Power Automate solution where the document contents are passed to an AI model (most likely by calling into Azure OpenAI)

Comparing with Syntex costs

[Updated February 2025, see the "newsflash" section later in this article] From March 2025, SharePoint autofill columns are priced at $0.005 per transaction (which means a page) - significantly cheaper than Syntex unstructured document processing at $0.05 per transaction, or the pre-built Syntex models at $0.01 per transaction. However, we may see similar cost reductions in Syntex which evens this out following changes to autofill pricing. On the two Syntex approaches, it comes down to whether there’s a pre-built Syntex model that suits your needs (e.g. for simple work with contracts, invoices, receipts, forms, PII detection or language detection), costs here are much lower at $0.01 per transaction (page).

Result – cheaper with autofill (unless Syntex pricing gets adjusted too)

Syntex specialises in extracting key info from documents – but if you want to use AI in some other way and/or you need generative AI and LLMs you’ll need to look elsewhere. So, what about the idea of a Copilot agent or Power Automate solution?

Comparing with Azure OpenAI costs (via a Copilot agent or Flow)

Here’s where things get interesting. If you have the technical skills to create an agent or automation which calls into AI and passes documents to it, you can reduce AI costs *fairly* dramatically - but note the differential has (happily) come down since writing this article - full details of the update are below. This is partly because going directly to AI is always cheaper than going via an “end-user” approach in Microsoft 365, and partly because you can control the AI model used. With autofill columns the model used is down to Microsoft (GPT-4 Turbo is referenced in the documentation), but with a built solution, choosing one of the latest “mini” models rather than full blown GPT-4 Turbo or similar has a dramatic effect.

A cost comparison calls for a quick spreadsheet. Let’s compare autofill columns with using two of the current AI models but going direct:

• GPT-4o
• GPT-4o mini

Here are the details of the scenario I’m modelling (see footnote at the end if you want more on this):

• 1000 documents per month processed by AI (e.g. summarised, analysed etc.)
• Each doc has 100 pages
• Assume average word density
• Azure OpenAI pricing details – USD (to help compare to autofill pricing which is also in $), East US Azure region, non-provisioned capacity

Here’s the calculation and findings (based on pricing in January 2025) – the costs for the scenario per month are highlighted in blue, with the differential to autofill pricing in red:

Newsflash! Significantly reduced autofill costs

Clearly this entire topic was in focus for Microsoft, because soon after publish of this article a 90% reduction to autofill costs was announced - from $0.05 per page to $0.005 per page, starting March 2025. The change would have already been in motion rather than triggered by anything I highlighted, but demonstrates that Microsoft *are* being proactive in getting newer and cheaper AI models behind built-in gen AI services like autofill and Copilot and passing on cost reductions to clients.

*With that said*, the updated calculations below show there can still be a significant differential between autofill and developer-led AI usage. The cost decreases certainly diminish the impact unless you're working at scale, but in some cases that's exactly where the AI value is. See the updated numbers below - what do you think?

 

To summarise:

• You’ll pay 3.31 times as much to use autofill columns compared to going direct with a similar model (GPT-4o)
• You’ll pay 55.11 times as much to use autofill columns compared to going direct with a cheaper “mini” model (GPT-4o mini)

The costs for the scenario using each approach are:

• GPT-4o mini - $9.07 per month
• GPT-4o - $151.20 per month
• Autofill columns - $500 per month

Before the update, those numbers were even more striking (at 33x and 551x difference respectively, and a total bill of $5,000 per month using autofill for the scenario) - but even with the February 2025 price reductions, it does still leave something to consider. Over a year, the difference is magnified of course and we’d be talking $109 for the cheapest approach with GPT-40 mini vs. $6,000 for autofill columns.

Result – going direct to Azure AI is (still) significantly cheaper

What do we take from this? Should autofill columns be avoided?

Even with these cost differences, autofill columns could absolutely be the most cost effective and ‘valid’ choice for your AI use case. The key considerations which come into view are:

• The scale you’re working at/how much AI you need to fulfil it (e.g. how many documents and pages)
• Possible avoidance of development costs

Autofill columns are ready-to-go AI that the business can use in a self-serve way. Certainly, cost governance measures need to be put in place to avoid bill shock, but there are options for this – at least in terms of someone being notified that spend might go beyond a defined threshold so they can intervene (by way of implementing an Azure budget on the Syntex meters). With other approaches, AI costs using Azure OpenAI are much lower but may require development or solution maker time – and that implementation cost could outweigh the cheaper AI costs, depending on effort required, day rates etc.

Overall, any options which put AI in the hands of the business should be welcomed since that’s where processes are understood and value can be easily created. Just take care with those cost governance measures if you enable any form of Pay As You Go AI.

Summary (and a view as a Microsoft partner/solution provider)

As someone working for a partner delivering services at the forefront of Microsoft AI, it’s interesting to consider the impact of “ready to go AI” in Microsoft 365 on companies like ours. If our clients can use AI without our help (such as autofill columns), will we deliver less AI advisory and implementation work? Will all our efforts to deeply understand the technologies, the factors in using each one, and how to create business value with them be to waste? Well, frankly it’s hard to imagine that based on the realities of getting the value from AI in production scenarios – if anything I think the reverse might be true. What we’ve seen in this analysis (and any other time we look at the “what to use when in AI”) is:

• You always pay a significant premium for simplified/abstracted AI – whether it’s Copilot, autofill columns, or something else. As a result, a ‘developed’ solution can save £1000s per year for many use cases due to the sheer difference in AI costs
• It’s a complex landscape where partners can add a lot of value - understanding the factors takes a lot of context and closeness to Microsoft developments
• Deriving likely AI costs for a use case is not simple – some modelling is required using a deep understanding of Microsoft technology. And cost comparisons differences across approaches are rarely found in the documentation

Nevertheless, the value is out there. While it's certainly true that the right decisions need to be made within a well-run "POC to production" motion, we're seeing more and more AI use cases related to high-value processes where there's a significant cost saving to the organisation. Often there are some accompanying benefits to employees too, and both will only accrue further as time goes on. 

Understanding the right AI technology and approach for the use case is the key of course - no more, no less. 

---

Footnote - more detail on the cost comparison

For simplicity, I’m considering the AI consumption costs only. Depending on circumstances you may also have some Copilot agent costs or Power Automate costs to consider too.

To compare the AI costs for autofill columns vs. Azure OpenAI, we need to calculate a "per page" cost for both approaches. Autofill columns and Syntex models are priced by page, but consuming GPT models directly is always priced by token usage. To compare apples to apples, I modelled an AI usage similar to the “create summaries for all my documents” autofill column scenario, using:

• 600 input tokens (average word density of a Word document is 500-700 tokens from my testing)
• 100 pages per document
• 120 output tokens for the AI response (which corresponds to around 200 words of output, the same as the prompt I used in my autofill column examples above)

These details are shown in the yellow box at the bottom of the Excel sheet and are used in the formulas.

Automate AI prompts across your files - autofill columns in SharePoint Premium/Syntex

As we go through the AI era, Microsoft continue to embed generative AI into the Microsoft 365 user experience in interesting ways. If you’re a Copilot user like me, you’ll most likely appreciate the capabilities in chat and meetings of course, and Copilot in Word and PowerPoint can be powerful for document work - drafting new content, rewording and enhancement, summarisation, identifying key points, and performing analysis on the text are all tasks Copilot can do well. But these are all single document scenarios – one consideration with Copilot is that there’s no way to process lots of documents in bulk, but for some use cases that’s exactly what you want to do. If you could effectively “run the AI” over many documents without having to open each one and type a prompt, this unlocks AI being applied to many processes and workflows. I list some examples below, but to call out one – the idea of having a 3-sentence summary displayed next to each document could be powerful. How many documents would that save you opening per week as you go looking for something? Or what about asking AI for the broad type of document (perhaps from some pre-defined categories), so you had more context than just the filename and location? There are lots of ways AI can be useful with documents in a ‘pre-processing’ way, where the AI has done it’s work before you come to the document.

The realisation is that Copilot itself cannot easily be automated. While the new Copilot Actions capability brings a certain level of this, the concept there is more about a scheduled prompt rather than something to run across your files. In terms of other options, a more technical maker could create a Copilot agent and find a way of looping through lots of files to do something with AI – but that’s not something most employees will do.

Microsoft have thought about this and have introduced a ‘built-in AI’ capability for files in Microsoft 365 – it’s part of the SharePoint Premium/Microsoft Syntex capability and is called autofill columns.

The idea is that you can run an AI prompt across every file in a SharePoint library and have the result stored in a column next to the document. You’re essentially passing each file automatically to a GPT model with the prompt of your choice. This offers a powerful new AI approach in how we manage information – I can foresee that every content management app and platform will have this in the future. As far as I can tell, Microsoft are the first to weave the capability into a core platform in this way.

Here are my top uses for SharePoint autofill columns – many of which you'll notice are forms of metadata generation:

• Document summaries – the full document summarised automatically in your preferred format (e.g. 3 bullet points, a few sentences etc.)
• Key takeaways – a summary focused not on the full document, but the conclusions only
• Key info extraction – identify the client/project/business unit this file relates to
• Key info extraction – list the people/organisations/concepts/[something else] mentioned in this document
• Classification – categorise the document, either generically or according to a list you provide (e.g. into “RFP”, “proposal”, “statement of work”)
• Automated assessment - based on the contents of the document, should this be approved or sent for human review?
• New info generation – based on the contents of the document, how does it correspond to X?

Of course, all those are quite generic approaches – even more powerful examples can come when dealing with specific document types. You might consider asking AI to extract contract values and start dates from contracts, to analyse the same contracts for specific risks, or to synthesise the key themes from research papers for example. There are lots of interesting possibilities arising here - we’ve had ways of automatically extracting information from or classifying documents before (in the Microsoft world, that’s where Syntex started), but nothing with the capabilities of today’s GPT-based AI models. If you're familiar with Microsoft Syntex, it's fair to see autofill columns as the new way of implementing many AI document understanding solutions in Microsoft 365 - a shift away from the 'machine teaching' Syntex approach of training a model to understand your document and then supplying some positive and negative examples - to something which uses GPT powers to understand the document via your prompt without the need for training. That said, the original approach will still suit some scenarios better and in some cases combining the two might be the best thing - nothing is going away at this point from Syntex.  

Try autofill columns free until June 2025

Like most forms of AI, autofill columns have a cost based on consumption - more on this later. However, until June 2025 Microsoft are giving some free usage to support exploration of the capability - this covers processing of 100 pages per month (e.g. 10 documents of 10 pages each), so doesn't get you too far but will support some free testing. This is baked into the wider promo covering lots of SharePoint Premium/Syntex capabilities - more details on the documentation page at https://learn.microsoft.com/en-us/microsoft-365/syntex/promo-syntex#monthly-included-capacity

Examples of autofill columns in action

As one example, let’s see what happens when we ask SharePoint to automatically summarise a bunch of project statement of work documents. These are real documents from my work at Advania but with client names redacted. In each case, the column on the right is what AI has generated.

Prompt:

“Summarise the project that this document relates to in around 200 words or less. If possible, provide any key dates or expected durations.”

Result:

From this generated summary, I instantly understand the document without having to open it - and at a time when we're creating more documents than ever, that's powerful. Note that while it may seem the summaries are truncated, that’s just SharePoint formatting things to keep the full list in view. Expanding the column shows the full value:

So auto-generated AI summaries work great and can be very powerful, but as per my earlier list you can imagine lots of other prompts too - document classification, extraction, and analysis all have interesting possibilities.

Because autofill columns can work on images too, the capability can be used to solve one of the big challenges with images – search engines don’t work well with them because by default there’s no description or tagging.

Prompt:

“Describe the image, giving as many details of the context as possible.”

Result:

That’s pretty incredible in terms of image recognition (and all down to GPT-4 Turbo’s multi-modal capabilities), with the model even recognising that it can’t fully read the text on the road signs in the first image. Imagine if every image stored in SharePoint got an automatic text description and set of image tags – the impact on searchability alone would be huge.

Some other examples might be:

• What is the nature of the complaint in this document?
• Identify the main theme of this piece of event feedback into “optimism”, “constructive”, “appreciation” or “collaboration”
• Score the agent’s performance in terms of helpfulness and ability to resolve the caller’s issue between 1-5 (with 5 being the highest), and give your reasoning
• Propose a decision on this customer loan application based on the credit history and other background information in the file

The possibilities are as wide as for generative AI prompts in general. With that in mind, clearly autofill columns can be used for a range of purposes including AI decision-making – so Responsible AI considerations may come to the fore in some usages. It’s another AI tool in the toolbox for the business to use, and leaders may want oversight of the use cases to validate against RAI principles.

I have ideas already! Should I apply autofill columns everywhere in Microsoft 365?

I have some great ideas too, but we all know that AI carries a cost and generally comes with some practicalities to consider. Should you leap forward and run autofill columns across tens of millions of documents in your environment? What are the considerations?

To my mind, a few interesting questions emerge - such as:

• Does this cost more, less, or the same as other AI tools I could use?
• How do I control costs?
• Documents are changing all the time in Microsoft 365 – does the AI run each time a document somewhere is edited and saved? That could cost a fortune!
• What types of files can the AI run across?
• Which languages are understood?
• If I have a have a document library with a million documents in and I create a “summarise this” autofill column, will all the existing files be processed?

I'm going to save the cost analysis and my Excel calculations for a part 2 article on this subject. The headline is that "simplified AI" like Copilot or autofill columns always comes at a cost compared to a built solution which integrates more directly to AI services like Azure OpenAI - whether this abstraction premium makes sense comes down to the use case, and factors like whether you can access development skills, potential user experience differences, and so on. In the AI age, we'll all spend more time considering different options simply because the cost factor can rule some out immediately, unless you're working with tiny amounts of data.

Let's deal with some other practicalities here though.

Practical considerations for autofill columns 

Behaviour (e.g. does the AI run each time a document is edited? What about existing files?)

No, autofill columns will only run the prompt if a user asks for this 'manually'. The option is a bit hidden away in the SharePoint interface, accessed via the ellipsis menu (...) and not on the 'Automate' menu as you might expect. If you have multiple documents selected, it will show as:

When an autofill column is created for the first time, it does NOT run over your existing files automatically. That means creating a column is a safe operation in the sense that nothing is going to happen without someone opting in and asking for autofill to do it's thing - though it certainly is a consideration that, once enabled, if you have a document library with 100,000 files stored it's just a few clicks to run AI each one, and that could be expensive.

But what happens if you want to run the AI over existing files at scale? Microsoft say "bulk processing options for existing library files will be added in a future release".

How does billing work?

You need Syntex pay-as-you-go billing to be set up for your tenant, where Syntex charges appear on the Azure bill for the linked Azure subscription. Costs can then be managed using usual Azure cost management approaches (e.g. implementing an Azure Budget) - the process is found at Configure Microsoft Syntex for pay-as-you-go billing - Microsoft Syntex | Microsoft Learn

What types of files are supported?

Essentially all Office files, images, PDF, and markdown - the full list is:

.csv, .doc, .docx, .eml, .heic, .heif, .htm, .html, .jpeg, .jpg, .md, .msg, .pdf, .png, .ppt, .pptx, .rtf, .tif, .tiff, .txt, .xls, and .xlsx.

Which languages are understood?

Only English for now unfortunately - though Microsoft say other languages will come soon.

Can autofill columns be more than text? Which column types can be used?

Yes, and actually quite a few are supported - text, number, yes/no, choice, and date/time columns are all supported. Unfortunately other types such as managed metadata columns and lookup columns are not supported. See the full list at Overview of autofill columns Microsoft Syntex - Microsoft Syntex | Microsoft Learn

Summary

Autofill columns are something of a sleeper in Microsoft 365 at the moment, lost in the other AI noise around Copilot and agents. They offer a huge step forward though, allowing business users to easily tap into generative AI for their documents and processes by virtue of being woven into the SharePoint interface. This democratised AI will also help organisations sidestep development costs or build effort in many cases too - Microsoft have done the work and woven it into Microsoft 365 and SharePoint in a nice way, so there's essentially no work to start getting the benefits (other than establishing the AI prompt(s) you'll use). However, autofill columns do come with a "simplified AI cost premium" compared to other ways of plugging in GPT capabilities - so while you may save on some dev/build costs, could this be far outweighed by the additional AI costs you'll pay?

In the next post, I'll provide some comparisons to current Azure OpenAI costs for different models (e.g. GPT-4 and GPT-4o) and some Excel modelling. While Microsoft product documentation pages rarely cover angles like this, I'd suggest it's a vital consideration for anyone needing to understand how much the AI bill might be for a certain scenario and how different uses of technology can affect this.   

Further reading:

• Leon Armston also has a great article on autofill columns, covering potential uses and considerations - Game Changer: Autofill columns Now Available in SharePoint Premium - Leon Armston's Blog 
• Microsoft documentation for autofill columns - Overview of autofill columns Microsoft Syntex - Microsoft Syntex | Microsoft Learn

Speaking at ESPC 2024 – more AI and Copilot!

ESPC, the European SharePoint, Microsoft 365 and Azure Conference - which always seems to have amazing venues which make you look tiny as a speaker, see left - always bookends the year for many of us in Microsoft tech (at least on this side of the pond). Winter timing allows us to reflect on tech developments through the year as well as discuss announcements from Microsoft’s big Ignite conference, which is typically just before (this week, November 19-22 this year). To speak at ESPC is always a privilege and I think this is my 10th year now. The event is in Stockholm this year between December 2-5 and it’s not too late to get tickets – as one of the year’s best personal development events in our space, it’s a great way to gain knowledge and take some great benefits back to your team and company.

This post is to mention the talks I'll be giving - but before then, some quick facts about the conference:

• Usually around 2000-2500 attendees, a mix of different roles but generally focused on Microsoft tech
• Always a big Microsoft representation – from senior execs like Jeff Teper to key product and strategy folks including Karuana Gatimu, Vesa Juvonen, and others
• December 2-5, starting with a day of (optional) full-day workshops on the 2nd
• Lots of coverage of AI, Copilot, Azure, Power Platform, SharePoint, Teams and more on the tech side – accompanied by lots of strategy, governance, and end-user sessions
• Typically a forum for Microsoft to make key product strategy announcements

Here’s what I’ll be talking about:

 

“AI on your data” deep-dive - comparing Microsoft 365 Copilot, Copilot Studio, and Azure OpenAI on your data

Astract: Every business can gain from combining generative AI and LLMs with company data, and so far Retrieval Augmented Generation (RAG) has been the most common technical approach to this. Providing an LLM with full knowledge of your company - your products and services, clients, employees and expertise, past projects, and other valuable information - has huge potential for simplifying work as we've seen with Copilot and other "gen AI on your data" technologies.

The options are complex however, and many CIOs are wrestling with AI strategy and tool decisions. Is the answer simply Microsoft 365 Copilot? What about no-code Copilot GPTs, low-code Copilot Studio, mid-range Azure OpenAI “on your data”, or building something with a "ChatGPT accelerator" using Azure OpenAI and Azure AI Search? Choosing the right approach can seem like a minefield – do you want to bring in data from Microsoft 365, Azure, SQL, a SaaS app, or simply a public website? Are you trying to provide a Copilot against a small knowledge base or a more expansive ecosystem of sites? Do you want to pay by user or by AI consumption? Should the experience surface in Teams, a Copilot plugin, or be embedded in an intranet or internet site?

This session aims to be a navigator through the Copilot and "AI on your data" maze, informed by battle scars from implementing all these forms of AI.

Implementing gen AI on the UK’s biggest infrastructure project – a ChatGPT and Azure OpenAI story

Abstract: The HS2 railway construction project stands as a cornerstone of the United Kingdom's infrastructure evolution, showcasing an ambitious leap in engineering and construction innovation. The scale of HS2 introduces a unique data challenge. Every mile of construction feeds into a massive repository of information, encompassing everything from ground surveys and compaction reports to intricate local authority covenants and extensive service contracts. But with more than 20 disparate platforms in use, ranging from in-house catabases, SaaS applications, and custom apps developed by suppliers, piecing together data related to aspects of the construction was becoming more challenging.

With the explosion of generative AI, the question came from programme leadership - “Can’t we just ask something like ChatGPT the questions, and it find the answers from across our systems?” This is a case study of a unique project, where generative AI is streamlining critical processes like incident management in a hugely complex environment. The combination of cutting-edge technology (Azure OpenAI, Azure AI Search, Semantic Kernel and more), talented developers and engineers, and a visionary construction organisation provides a shining example of how gen AI and Large Language Models are making a real difference to the world.

This session aims to convey both the business challenge and the technical solutions, and it’s a story with a few twists and turns along the way. Not unlike the railway line!

Closing thoughts and conference details

We’re now 2 years past the introduction of ChatGPT, and in my experience 2024 has been a year where lots of AI projects have made it into production. Technology has never been more impactful on the world. At the same time, many organisations are still debating whether to go big on Copilot and how to make the right choices in their AI strategy and technology decisions. So, it’s a perfect time for ESPC and the conversations that happen there – I’m *extremely* excited for my sessions and to share knowledge and perspectives from the Advania team and I.

Hopefully see you there!

https://www.sharepointeurope.com/pricing/

Comparing productivity Copilot options - Copilot for M365, Copilot Studio, build your own Copilot etc.

I spend a chunk of my time talking to CIOs and other leaders deciding the AI strategy for their organisation, and a common conversation at the moment is how to frame Copilot for Microsoft 365 against other options. It's the new "what to use when" conversation in Microsoft-land. If the challenge being addressed is 'providing the right set of AI tools to a business', there are multiple options from Microsoft and beyond, and choosing the right approach comes down to properly considering what you're trying to solve for and understanding not just the orientation, but also the capabilities and limitations of the various options. 

Common questions I'm hearing include:

• We're not sure about licensing Copilot for M365 across the entire business, what other options do I have?
• I see ChatGPT has an Enterprise version now - it seems to solve the data privacy risks of the consumer version, and all of our employees are familiar with ChatGPT. Should I use that?  
• Why could I not just use the free version of Microsoft Copilot (i.e. what was "Bing Chat Enterprise")?
• I understand Copilot for Microsoft 365, but where does Copilot Studio fit in?

Understanding the characteristics and pricing of the major options becomes vital to make the right decisions. Sometimes it will be an economic decision, sometimes it will be capability led - we've seen it all. I'm a strong advocate of Copilot for Microsoft 365 (as is my employer, Advania UK - we licensed 100% of employees), but it's not necessarily the answer to every AI question. Indeed, my team has solved AI challenges with "custom Copilot" approaches using Azure OpenAI that simply couldn't be done with Copilot for M365, including integrating large volumes of data from SaaS apps and custom databases, and steering the AI past the one-size-fits-all behaviour of Copilot. Despite it's strengths, Copilot doesn't quite offer the flexibility or cost effectiveness needed for certain scenarios. 

Major AI options on a slide

To help with AI strategy conversations, I produced the slide below which I'm sharing here in case it's useful for others (you can download it in PowerPoint form). It's an attempt to summarise some of the key factors and differences, though I'd be the first to say it doesn't cover every consideration and has some subjectivity to it. When I'm asked a question like "where does X fit?" or "how should I think of Y?" question, I often put the slide up to call out some of the major factors and differences as we walk through some options.

When considering AI options, I see some of the major considerations as:

• Overall positioning and value prop ("Headline" in my slide)
• The cost model
• Costs
• Data sources - which company sources of data can the AI talk to?
• Automation - whether you can fully automate a process with the tool, or whether it's purely end-user driven
• The surface - where the AI shows up
• Key limitations

I often feel this kind of thinking and comparison is what's missing from Microsoft and other vendors. The strengths of a technology are extolled in the documentation and content, but rarely the limitations and "but bear in mind...." considerations. As an example, the Copilot Studio page is unlikely to ever say that it's a great technology for some use cases, "but good luck forecasting your run costs, because the pricing model makes it really hard!" It's factors like this which are hugely relevant if you're deciding (or paying for) the AI strategy for your organisation however. 

There's quite a lot of info - and some opinion - in the slide, so I unfold some of my thinking in the notes below. Here's the slide itself (download link at the end):

Obviously the condensed text and bullets on the slide can only tell half the story in a complex landscape like this, so let's expand the thinking - at least for the major elements rather than every angle.

Headline/value proposition

Most will be familiar with the value proposition of Copilot for Microsoft 365 - your data in M365 (documents, mail, Teams chats and more) integrated with LLM capability and surfaced in the flow of work via Teams and Office apps. If you're an existing user, you'll probably cite the very strong capabilities around Teams calls and meetings as a highlight - especially intelligent recap, with auto-generated meeting summaries, action items, speaker attribution, being able to ask questions of the transcript and more. Alongside, there are lots of other capabilities which boost productivity across many organisational use cases and roles/functions, particularly when working with documents. Sometimes accuracy and results can be mixed, and that sometimes that's due to certain Copilot limitations today - more on this later. Sometimes I'm asked how Copilot Studio relates to Copilot for Microsoft 365 - but this has a different value prop entirely. Copilot Studio is suited to 'focused' Copilots for specific use cases - perhaps to provide answers on HR policies, employee onboarding, product documents, or an FAQ, and they can be surfaced internally or externally on a .com website. Copilot Studio solutions are often modern day chatbots, much more intelligent than those of the past because they combine LLM power, a focused knowledge base or set of data, and particular instructions (grounding) to the AI on how to provide the best possible answers. However, Copilot Studio isn't suited to solving broad AI needs (e.g. an internal Private ChatGPT) because of the limitations on data integration and difficulties predicting costs. For smaller needs it's perfect though - our team has built some amazing solutions already, and I predict many apps and in particular Power Apps, will shift to be Copilot Studio solutions in the next few years. We're just all comfortable with using chat for different things compared to 5 years ago now that it can actually work - CIOs and app makers should be cognisant of this.

Microsoft also offer Copilot (free) and Copilot Pro (£19 per month) as the evolution of what was Bing Chat Enterprise, but these should be seen as competitors to public/consumer ChatGPT rather than a true organisational solution. The main callout is that you have no roadmap or possiblity of integrating company data with these tools - they are LLM only. In Copilot Pro you can use the AI with a document you have open in Word (e.g. for summarisation, generation, rewording, analysis etc) and there is enterprise data protection so any sensitive data can't leak out, but Microsoft Copilot/Copilot Pro don't provide any way of answering questions from company data at scale. A CIO might find Microsoft Copilot appealing as a free option with no barrier to entry, and while it's arguably safer in the workplace than public ChatGPT, my view is you're likely to confuse employees if it's made available as a first step in AI, but then subsequently replaced by other AI tools (e.g. Copilot for M365 or an internal Private ChatGPT) as you unfold your AI strategy.

A Private ChatGPT solution built on Azure OpenAI can be attractive because the costs aren't per-user. Because the costs scale in a different way (i.e. they are "platform + AI consumption" rather than per-user), we've seen a lot of interest in this from mid-sized and larger organisations who aren't sure about a large scale Copilot investment but do want to provide generative AI tools integrated with organisational data. In quite a few cases, the organisation is choosing to deploy a platform like this in addition to Copilot rather than instead of, and at Advania we've had quite a lot of success with our clients in this space. We commonly integrate Microsoft 365/SharePoint/Teams data so the AI is able to answer questions related to the company's clients, projects, people, policies, sales and product information etc. It becomes a powerful tool that changes the employee experience significantly because it provides a new way to find organisational knowledge and get straight to the answer. As alluded to earlier, it can also be the foundation for a tailored AI platform designed to support specific use cases and integrated with data from different platforms. We've integrated Azure OpenAI with incident management systems, HR data, employee skills/certification data, access card systems and much more. As such, a Private ChatGPT solution built on standard gen AI approaches can go beyond simply being a digital assistant for common productivity and creativity tasks, and be the AI platform where high value processes and use cases are enabled. We're seeing many organisations think in terms of an AI platform for the business that can scale and be extended over the next few years as AI opportunities and use cases emerge. 

Further thoughts

As you can probably tell, there are many considerations across multiple dimensions trying to come out in the slide above - and even then it's only a biased, partial view. I'm not unfolding every point referenced on the slide, but just a couple of others to call out:

• Is the tool automatable? 
• Whether the AI tool supports automation is a key factor that isn't always considered. For some use cases you want to throw 100 or 1000 items to the AI in bulk, but ready-to-go solutions like Copilot for Microsoft 365 don't lend themselves to this because they have no API. We're doing a lot of work in this space with Private ChatGPT/Azure OpenAI solutions for Due Diligence Questionnaires, Cyber Security/InfoSec questionnaires, RFPs etc. - in many of these cases, organisations are seeing the potential for better outcomes than with specialist tools they already have, but which don't use AI with their data effectively and/or haven't really landed with the business, which is interesting. I posted about this observation here.
• Limitations 
• Every AI tool has boundaries and someone supporting your decision-making should understand them. Some examples are called out on the slide, but of note is that Private ChatGPT solutions typically can't index Teams chats and e-mails in the way that Copilot can (nor do they provide Teams meeting support) - they are limited to understanding knowledge found in documents primarily. Conversely, Copilot for M365 has limitations in understanding long documents, and this can be very relevant in some scenarios. Microsoft recently announced the limit is now around 80k words when using Copilot in Word, but it's worth also understanding that - as far as I know - it's still only around 20k words in terms of what Copilot Chat will understand (i.e. when asking Copilot questions of your data generally), because that's how much gets indexed into the Semantic Index.
  
  At the same time, these limits and the general value of the Copilot proposition should only improve from here.

Conclusions

There's a lot to know, and as I mentioned at the start this is the new "what to use when" in Microsoft technology - but options and flexibility are rarely a bad thing if you can find your way to the right choices. 

If you're interested in these perspectives and can make it to this year's ESPC24 Conference in Stockholm this December, I'll be speaking about this in my session AI on your data Deep-Dive – Comparing Copilot Studio, Copilot GPTs, and Azure OpenAI on your Data. Hopefully the slide is useful to someone either way.

Download my Copilot options slide here

Join me at ECS 2024 for a full day workshop on Microsoft Copilot solutions with Jussi Roine

2024 will be a pivotal year for Microsoft AI and their range of Copilots in particular - and it was great to hear plans and insights during a week on campus with Microsoft in Seattle for the MVP Summit recently. This is the year when the AI strategy is in full swing for most organisations (or at least, should be), and the need to understand and decide what to provide to employees really takes effect. Between Copilot for Microsoft 365, Microsoft Copilot, role-specific Copilots (such as GitHub Copilot, Copilot for Security, Copilot for Sales, Copilot for Service etc.), or custom Copilots built with Copilot Studio or Azure OpenAI, there’s a lot to consider. For anyone tasked with charting the path forward, the big Microsoft-oriented conferences this year are the place to jump start your learning, hear how others are approaching it, and take back ideas, plans and recommendations – if you haven’t already spoken to your boss about this (or allocated budget if you're the lucky purse holder), now is the time.

With over 2500 attendees, 150+ sessions, talks from senior Microsoft leaders, and a great expo hall with over 75 exhibitors to talk to, the European Collaboration Summit 2024 is actually the biggest Microsoft 365 and Power Platform conference in the world. The conference is held 14-16 May 2024, in Wiesbaden, Germany. I’m thrilled to be running a full day workshop on Microsoft Copilot solutions with my friend and fellow MVP Jussi Roine. Say what you like about that man but you can't deny his vast experience and love for broccoli. We're badging this a "Copilot Powerclass", giving some coverage to a broad range of Copilots but also diving deep on Copilot for Microsoft 365, Windows Copilot, Copilot Studio, and various things to know about data governance, licensing and more.

Despite speaking at big conferences for a decade and half now, I'm excited about this and I have to confess a bit nervous :) This will be my first full-day workshop and it feels like a lot of hours to fill. On the other hand, there's a lot to talk about and every client conversation I have at the moment about Copilot seems to need more time. In any case, Jussi tells me my role is simply to bring vegetables and he'll take care of everything else. 

Still tickets left, but going fast (for both the overall event and our workshop)

At the time of writing (end of March 2024), you're not too late to make it to the European Collaboration Summit - I hear from the organisers that the the vast majority of tickets are allocated, but there's definitely time to join us in Germany in May if you're quick.

And if you like the sound of the full day Copilot session Jussi and I are running, there's still time for that too. We were expecting 30-50 attendees, but at the time of writing we have 87 sign-ups already which is amazing. We still have room for a few more though as the room fits 120 apparently, and we'd love to see you there if you have an interest in learning more about Copilot.

There are also several other workshops with amazing speakers which look great by the way - see ECS 2024 tutorials for the full list). The page to go to is Tickets - European Collaboration Summit (collabsummit.eu)

 

More details on our Copilot workshop

Microsoft Copilot(s) Powerclass

In today's digital age, organizations require powerful tools to improve their productivity, speed up decision making, and secure their environment. The Microsoft Copilot capabilities offer this and more. Join this Full Day Tutorial, designed for business decision-makers and technical decision-makers, to learn more about Microsoft 365 Copilot, Windows Copilot, Security Copilot, and Power Platform Copilot. Over the 8 hours, you will also learn about critical technical topics such as Generative AI, Large Language Models (LLMs), licensing, use cases, productivity, and possibilities. 

Explore how these Copilots can help your organization improve productivity by handling your various operations automatically. Additionally, you will learn about their security capabilities to ensure your environment is kept safe at all times. You will also learn about the licensing requirements for using these Copilots and how they apply to your organization. Finally, there will be discussions on the possibilities and use cases for these Copilots, with hands-on experience that will enable you to harness their full potential. 

The workshop is specially designed to empower you with the knowledge, insights and inspiration you need to make informed decisions around utilizing Microsoft Copilot capabilities to improve your organization's performance. Join us and elevate your business to the level.

LINK: Microsoft Copilot Powerclass - European Collaboration Summit 2024 (collabsummit.eu)

Hopefully see you there! 

Getting started with plugin development for Copilot for Microsoft 365

In my last post we looked at the return on investment for Copilot for Microsoft 365, specifically in terms of time savings required for the $30/£24.70 per user per month licensing investment to make sense. In this post I want to turn attention to extending Copilot and getting started in the world of Copilot plugins. Copilot for Microsoft 365 can become even more powerful when integrated with other company systems - I created the slide below recently for a deck I was working on, and the four areas provide ideas on where the value might be and potential scenarios:

However, at the current time (February 2024) getting started with plugin development is a bit gnarly - there's lots of documentation to read and there are some interesting practicalities to consider. I’ve spent some time on this, and the sections below give a quick summary of initial findings which may be helpful for anyone else going down this path.

Tenants and licensing

Much of the initial complexity falls into this bucket. Things to know include:

• Copilot plugin development needs production Copilot licenses, there’s no way around this. This may mean developing in your production tenant (if you are a licensed user) or buying extra Copilot licenses for other tenants
• Microsoft 365 Developer tenants cannot be used for plugin development 
• If you’re on the Microsoft 365 Developer TAP (we are at Advania), these tenants can be used but you still need to buy Copilot licenses in that tenant
• In addition to the core Copilot license, the user also needs to be assigned a "Microsoft Copilot for Microsoft 365 developer license"
• Copilot for Microsoft 365 licenses effectively grant “Copilot Studio use rights” – importantly, this allows you to create and run M365 Copilot plugins only, not to run standalone Copilots (see note in the blue box below) 
• Since plugin development is still in preview, production tenants need to be enabled for it via a special helpdesk ticket - there's special wording to use, indicating this is somewhat hand-cranked in the backend for now (see the Copilot extensibility prerequisites article linked at the end for the exact words to use) 

Developing standalone Copilots (not plugins)

Coming the other way round from the plugin focus of this article, the other primary use of Copilot Studio is to develop standalone Copilots using a low-code approach. Examples could be a HR chatbot providing answers on policies and internal benefits and hosted in Teams or a SharePoint intranet page, or a customer service chatbot on an external website providing answers from a knowledge base of uploaded documents. In these cases, you don't need a Copilot for Microsoft 365 license but you will instead be paying the $200/£165 per month run cost, which gets you 25k messages across all such Copilots (with additional capacity charged extra). There's a bit of nuance in what constitutes a message - a typical interaction counts as one message but invoking gen AI counts as two - but in short you are calculating based on expected usage.

The image at the bottom of this article more directly compares the two flavours.

Building Copilot plugins using the Power Platform (low-code)

The prospect of using low-code for plugin development is very appealing. In this space, note the following:   

• Power Platform connectors can be turned into Copilot plugins by converting them to be a “Connector AI plugin” – whether custom or pre-built connectors (e.g. ServiceNow, Zendesk etc.) 
• However, today connectors need to be certified – meaning custom connectors cannot easily be used for plugins, since certification is a complex process for major ISVs (i.e. not internal teams or partners simply trying to create solutions for a single organisation)  
• Additionally, only read-only actions are supported for now 
• Other Power Platform approaches are possible – using Power Automate Flows, the new AI prompts capability etc. in Copilot plugins. However, again this does not seem to be possible at the time of writing unfortunately 

Building Copilot plugins using Teams message extensions (pro-code) 

The alternative architecture for Copilot for Microsoft 365 plugins is based on Teams development, specifically Teams message extensions. This makes sense, and before the dawn of GPT we've used this approach at Advania UK to build other conversational bots in Teams. Some details to be aware of here:

• The advantage of Teams message extensions is that plugins with more advanced UI can be used (e.g. adaptive cards in AI responses)
• Conceivably, the solution you build as Copilot plugin could also be a regular Teams message extension in Teams chat and be surfaced in Outlook - enabling you to provide your experience in different ways within Microsoft 365
• Teams message extensions are the right approach if you're working at scale (with large volumes of data or user load) 
• Permissions - if you are developing your plugin using Teams message extensions, you’ll need the ability to side-load apps into the tenant

So that summarises some of the initial considerations in getting started with plugin development. It's also worth noting that Microsoft are pushing to create an entire ecosystem around plugins with marketplace approaches, meaning plugins can be sourced from internal developers, partners, specialist vendors and so on.

Also remember, Copilot extensibility is not just about plugins

All of the options and considerations above relate to plugin development, but this sits alongside the alternate path of bringing data into Copilot for Microsoft 365 using Graph Connectors. In that approach, the data you integrate is indexed (stored in the semantic index which sits behind Copilot) rather than simply being available via a read/write call-out of some kind. Graph Connectors bring other advantages such as making the data available in Microsoft 365 search, Viva Topics, Context IQ and even being used for content recommendations in Microsoft 365, but if you're working with data at scale you'll to purchase additional Graph Connectors index quota (you get 500 items for free per E5 or Copilot license). Microsoft's article Choose your extensibility path expands on these considerations.

Zooming out from plugin development in a different way, it's worth considering Copilot Studio as a whole since it's not just about Copilot for M365 plugins.

Copilot Studio - varying audiences, varying outputs

Copilot Studio can be slightly confusing in the Microsoft AI space because it's used to create different solutions and experiences - essentially including Copilot for Microsoft 365 plugins and standalone Copilots built with low-code. Many people recognise it as "what used to be Power Virtual Agents", but between licensing variations and what can be created there's a bit more to it than that. I posted the following on LinkedIn which summarises the two major usages of Copilot Studio:

That hopefully gives a sense of things from a Copilot Studio lens. Some of this is made clear from this table in the Power Platform licensing guide - see the image below, and note specifically that the output formats and available channels are different between the two paths, and that Copilot plugins using Power Platform approaches can use Standard, Premium and Custom connectors:

Summary

Extending Copilot for Microsoft 365 with plugins can be a great way to derive additional value by integrating systems, and opens the door to the possiblilty of Copilot becoming a universal interface for all of the apps and platforms an employee works with. The next few years could see significant changes to the employee experience in this regard, at least for organisations making the investment in Copilot for Microsoft 365. Other forms of Copilot can also be created - standalone Copilots (as Microsoft refer to them) are often focused on a particular business domain or use case, and can reach a broader audience because they surface in different places and don't rely on Copilot for Microsoft 365 licenses. Both experiences are created by makers/developers in Copilot Studio, but there are licensing and reach differences - and today, some things aren't quite in place (early 2024) because we're still in a preview phase for plugin development. No doubt the path will get smoothed out as we go through 2024.

References

• Copilot extensibility prerequisites - https://learn.microsoft.com/en-us/microsoft-365-copilot/extensibility/prerequisites

• Extensibility options for Microsoft Copilot for Microsoft 365 - https://learn.microsoft.com/en-us/microsoft-365-copilot/extensibility/decision-guide

Copilot for Microsoft 365 - the surprising truth about time savings and ROI

Now that Copilot for Microsoft 365 can be purchased by anyone (with no minimum license count), organisations are starting to think about it more seriously as they form AI strategies and budgets. Looking across Microsoft's family of Copilots, some are free, some are licensed, some are general, and some are targeted as specific personas - but most would agree it's Copilot for Microsoft 365 which stands as the principal Copilot for workplace use. We could be moving from an era where countless hours go into creating and consuming information in very manual ways to a new era where generative AI is doing more of the work - to write that report, create that presentation, or write the words to respond to that e-mail. Those are creation examples, but when AI can summarise, identify key points, generate follow-up actions, and even identify areas of accord and discord, so many of the consumption-based subtasks we do also become more optimised and accelerated too. With the most expensive price tag, Copilot for Microsoft 365 is also the one where the decision-making for the investment is most complex.

Something I spoke about in a recent talk (at the European SharePoint, Microsoft 365 and Azure Conference) that some people latched onto is how surprising the numbers are in terms of time-savings needed for Copilot to pay for itself. The license is a significant investment of course at $30 or £24.70 per user per month with a 12 month commitment - whichever way you slice it, that's an expensive proposition given that the list price of E3, the entire productivity suite for enterprise users, is $36 or £33.10 per user per month. When seen as a "AI bolt-on" the cost of Copilot is indisputably high, and perhaps unsurprisingly when Microsoft announced pricing the common response seemed to be "far too expensive, it doesn't make if it costs nearly as much as the entire suite". There are lots of ways to look at this, but despite the similarities (both Microsoft offerings, both related to productivity, both additive to each other) a direct comparison of one vs. the other actually doesn't make too much sense to me personally - the propositions and value provided are so different. 

Principles for a Copilot value case

Before we look at the numbers, let's agree on three things:

• The value case for Copilot for Microsoft 365 shouldn't hinge on time savings and an easily-modelled financial equation alone - there are other benefits which are less easily quantified which constitute value. Every organisation will need to form their view on this, but I list examples later in the article.
• Time savings have to result in genuine gains - whether it's five minutes or five hours, most organisations will take the view that any time saved needs to translate into real value. In other words, there's no ROI if the organisation doesn't benefit from the additional time, perhaps because the time benefit goes exclusively to the employee instead or it simply doesn't go on productive work
• Basing the model on known or anticipated use cases doesn't work - you can try to predict use cases ahead of time, but the reality for both Copilot and generative AI on the whole is that the business will find benefit in unpredicted ways. Better to pilot the technology in some way and see what happens
  
  

A simple ROI model

The calculator below models the break-even point for Copilot for Microsoft, based on time-savings alone, for three different salaries. I'll go into details below but it uses UK parameters for employer tax and so on to arrive at the "true" total cost of an employee per day, but the currency used is Euros since that's where most of my readers are.

The real point of course, is that it doesn't take five hours or even two hours per month saved for Copilot to pay for itself. So long as we can stand behind them, the time savings required are quite minimal:

As the salary increases even less time needs to be saved of course - but even on the lowest salary we're talking only 36 minutes per month. This is the surprise to many people.

Some detail on the calculation:

• The calculator uses UK parameters, in fact the true cost per day of an employee in our company (Advania UK). This means the current employer tax rate (National Insurance in the UK) and a 4% pension contribution, but excluding other aspects sometimes modelled such as an allocation for office space etc. 
• I haven't looked, but I suspect these percentages won't vary wildly across countries enough to skew the overall model dramatically
• Although the tax parameters used etc. are UK, the currency used is Euros to reflect my readership
• The Excel is linked below if you want to download and amend

Download the file here: Copilot for Microsoft 365 breakeven - Excel calculator

If using or pointing people to a web page works better for you, Dan Toft has taken this and created a great online calculator which also allows you to edit the parameters - ROI Calculator (dan-toft.dk).  You'll need to be able to calculate the total cost from a base salary yourself (i.e. calculations for employer taxes and pensions aren't built in), but it depends what you need. Excellent work Dan.

 

The wider value case for Copilot for Microsoft 365

I mentioned earlier that measuring the ROI for Copilot based on time savings alone is only part of the picture, that it's not just about productivity gains and time efficiencies. I used this slide in my talk to expand the discussion to some of the wider benefits:

There's obviously quite a lot wrapped up into those four bullet points and they're not quantitative benefits you would model into a licensing business case, but the positive impact on the employee experience and output needs to be considered somewhere. I feel there's a reduction in cognitive load, and switching between different contexts and tasks becomes easier and less painful. When creating content, regardless of how good you are as a writer your output can be improved - Copilot will often bring in a point you hadn't considered or simply articulate the message better than you would. That's not to say that Copilot outputs are always ready to go or it's able to do the work for you - that would be lovely, but more often than I iterate with further prompts and/or add/edit/delete to what Copilot has created. Nevertheless, I'm faster on quite a few tasks and Copilot's ability to research and bring in facts or approaches others have used helps on the quality front.

As referenced in the last bullet, one area Copilot is particularly helpful in is comms and e-mail. There's drafting e-mails of course, and being able to ask for a summary of my inbox in the last week and pull out any actions I need to take is extremely powerful - even if I scan through the mails myself too, I'll spend less time doing so. Using the "Summary by Copilot" feature to summarise the main points of a long individual e-mail or thread works very well too - picking up context and making a judgement at speed on any action/response makes moving through your inbox simpler and quicker. Finally, the "Coaching by Copilot" capability in Outlook is surprisingly useful too - there's an initial novelty of having your communications and tone judged at first but we all write mails at speed often without considering enough quite how it might read on the other side, and it has enabled me to catch a couple of sub-optimal communications. I'm a senior leader in our company, and perhaps Copilot is right to catch me on phrases such as (real example here) "it seems bonkers to me that...." and suggest some wording that might be more appropriate! You can take it or leave it depending on the context (and there's an argument that some authenticity often comes from our from-the-hip communications), but having things pointed out at least prompts the reflection and supports the choice.

All in all, there are lots of benefits beyond the hard numbers. It's also an interesting thought that as employees increasingly expect a high-fidelity employee experience and toolset that allows them to give their best, Copilot for Microsoft 365 does make a clear statement on that and perhaps becomes a differentiating factor between organisations and teams battling for talent.

License strategies for Copilot for Microsoft 365

So, do you license everyone or just a subset? Start big or with a small, focused pilot? No single answers on this of course, and it's going to be fascinating to watch how the Copilot era unfolds and how organisations approach the investment and benefits realisation. I won't share all the guidance we're giving to clients, but starting with a pilot covering different user types and personas makes sense for any significant investment in new technology like this - and that's how most 1000+ seat organisations I've spoken to are starting. With regards to coverage, clearly the investment required for Copilot is dramatically different if licensing 10-20% of the business compared to 100% - and this differential obviously scales to big numbers for the largest organisations. To license all users at list price for 30,000 employees would cost over £10m, and no less than £33.6m for 100,000 employees. No doubt those are hypothetical scenarios since such a deal would be heavily negotiated as part of an Enterprise Agreement (and/or perhaps in exchange for a high-profile case study - look out for those soon), but it is illuminating to consider the difference between licensing some and licensing all.

My feeling is that most orgs will structure the case based on licensing a subset of users according to persona/role and the value conferred. This makes sense because no-one would argue that all roles will benefit from Copilot and gen AI to the same extent, and licensing users who simply aren't adopting the tool heavily doesn't make sense either. 

Along with the information governance/data security piece (something we've spent a lot of time on developing services for), the different aspects of licensing mean that there can be a few steps on the Copilot for Microsoft 365 journey for most companies who are buying in. Hopefully this post on some of the economics has been useful though.