The Client Journey Tells You Where the Business Really Is

image

There is a point in every service business where growth still looks busy, but it no longer feels healthy.

The calendar is full. Ads are running. Sales conversations are happening. New clients are arriving. From the outside, it looks like momentum.

Inside the business, something feels off.

You finish the month tired and the client count has hardly moved. Your team has handled more calls, answered more questions, chased more payments, and welcomed more people, yet the result barely reflects the effort. That is usually when I stop looking at the front door and start looking at the back one.

Because more leads will not fix a leaky experience.

Growth hides the problem until it does not

Early on, client loss can be easy to ignore. A few people leave and it does not feel significant. The business is small enough that enthusiasm covers rough edges.

Then the business gets bigger.

The same onboarding gaps become more expensive. The same unclear expectations create more friction. The same missed follow-ups turn into quiet exits. Nobody has to make a dramatic complaint for the damage to be real. Sometimes they just stop engaging, stop replying, stop showing up, and eventually stop paying.

This is where many owners make the obvious move. They spend more to bring more people in.

I understand why. Marketing feels active. Sales feels measurable. Pipeline gives everyone something to discuss in the weekly meeting. But if the client experience is not holding, all you have built is a costly replacement machine.

That is not growth. That is movement.

The first weeks set the whole relationship

I have become more interested in the first part of the client journey than almost any other part of the business.

Not because onboarding is glamorous. It is checklists, expectations, reminders, handovers, notes, meetings, small promises, and boring consistency.

But boring consistency is often where profit is hiding.

The first few weeks teach a client how your business works. They learn whether you are organised. They learn whether they need to chase you. They learn whether the promise they bought is becoming something tangible.

If that early experience is vague, the client starts filling in the gaps themselves. That is dangerous. Their version of the story may not be the one you intended.

This is where Microsoft 365 can do practical work. I would map the first client journey in a shared Word document or Loop workspace, turn the repeatable steps into Planner tasks, keep client context in a Teams channel, and use Copilot to summarise meeting notes, draft follow-up emails in Outlook, and identify action items that slipped.

None of that is magic. That is the point.

The value is not in making onboarding fancy. The value is in making it visible, repeatable, and harder to forget when the business gets busy.

Retention is an operating system

A lot of businesses treat retention as a customer service problem. I think that is too late.

Retention starts when the expectation is first set. It continues when the client understands what happens next. It improves when the team can see the same information, use the same process, and spot weak signals before they become cancellation emails.

You do not need a massive transformation project to start. Pick one client segment. Write down the first six weeks. Decide what each client should receive, what your team must do, what evidence shows progress, and where the handoffs fail.

Then put that into the tools your team already opens every day.

A better onboarding process will not remove every cancellation. Nothing does. But it changes the work. Instead of constantly buying attention from strangers, you start earning confidence from the people who already said yes.

That is the growth I trust more.

Not the noisy kind.

The kind that stays.

The Real Work Is Not Another Tactic

image

Most owners I meet are not short of effort. They are short of room.

Room to think. Room to make better decisions. Room to stop reacting to every urgent customer request, vendor announcement, staff issue, cashflow wobble, and half-finished idea sitting in the inbox.

That is why so many businesses end up looking busy but feeling fragile. The owner keeps tuning the visible parts of the machine. Better campaigns. Better processes. Better meetings. Useful, but none of it fixes the real constraint if the owner is still the bottleneck.

The business usually rises to the level of the person leading it.

That is an uncomfortable sentence. It should be.

The easy work looks productive

It is tempting to stay in work that gives visible proof of progress. Rewrite the website. Change the offer. Hire another person. Buy another app. Create another spreadsheet. Push harder on social. Start another initiative.

I have done versions of this myself. Most business owners have. It feels like discipline because there is activity everywhere.

But activity is not always advancement.

The harder work is looking at the recurring patterns and asking, “Why does this keep happening around me?” Why do the same decisions come back to my desk? Why do I avoid the awkward conversation until it becomes expensive? Why do I keep saying yes to work that does not fit?

That work rarely produces a neat announcement. But it is often where the real advantage is built.

Your operating system matters more than your tactics

I think about this a lot with Copilot and Microsoft 365.

Plenty of organisations are trying to use Copilot as a faster keyboard. Draft an email in Outlook. Summarise a meeting in Teams. Turn notes into a document in Word. All good uses.

But the more interesting use is not speed. It is reflection.

After a difficult client meeting, I can ask Copilot in Teams to help me identify the decisions, risks, and unresolved questions from the transcript. I can put the next actions into Planner, save the working document in SharePoint, and use that as the basis for a better follow-up in Outlook.

That is not just automation. That is a leadership loop.

The value is not that Copilot wrote some sentences for me. The value is that I forced myself to inspect the way I work. What did I miss? What did I delay? What needs to become a rule, not another heroic rescue?

A better business is usually built from better loops.

The real edge compounds quietly

The strongest operators I know are not chasing every new trick. They are harder to knock off balance.

They recover faster from mistakes. They make decisions with less drama. They communicate sooner. They document what matters. They know which work to refuse. They build teams that do not need constant rescue because the thinking has been made visible.

That is difficult to compete with because it is not one tactic someone can copy. It is a collection of habits, standards, judgement, and self-awareness built over time.

You can copy someone’s landing page. You can copy their pricing model. You can copy their tech stack.

You cannot easily copy the way they think under pressure.

That is where I believe owners should spend more attention. Not less work on the business, but better work on the person making the business decisions.

Use the tools. Use Copilot. Use Teams, Outlook, SharePoint, and Planner to create cleaner loops.

But do not confuse the tool with the transformation.

The business changes when the owner changes the way decisions are made, captured, reviewed, and improved.

That is the work most people avoid.

It is also the work that makes you hard to catch.

Comparing LLMs in Copilot services–Round 4 – Cowork (GPT)

image

Round 1 – https://blog.ciaops.com/2026/07/30/comparing-llms-in-copilot-services-round-1-chat/

Winner – Opus

Round 2 – https://blog.ciaops.com/2026/08/08/comparing-llms-in-copilot-services-round-2-researcher/

Winner – Critique

Round 3  – https://blog.ciaops.com/2026/08/11/comparing-llms-in-copilot-services-round-3-cowork-claude/

Winner – Opus 5

I’ve been pitting different LLMs inside M365 Copilot against each other ina world cup style elimination to see which comes out on top. The process involves taking a standard prompt and running it against all options. This prompt creates a multi page document requiring deep research and is quite involved. The results are then compared against each other using SharePoint Copilot and Gemini. Conclusions are then drawn.

This round the battle is between all the models available in Copilot Cowork GPT. Namely, these:

Screenshot 2026-08-12 082720

The other interesting factor here is that all these models as PAYG, so I’ll also give the costs for the same prompt.

I used Grok to evaluate all the results which produced:

https://github.com/directorcia/general/blob/master/Copilot/Comparisons/20260811-Cowork-GPT-Grok-eval.md

In summary, the rankings are:

1. 5.6 Terra

2. 5.6 Sol

3. 5.5

Interestingly, the less powerful model (Terra) produced a better result here.

And the costs of each:


GPT 5.6 Sol = 2,800.50 credits
GPT 5.6 Terra = 260 credits
GPT 5.5 = 1,200 credits

5.6 Terra wins again big here, only costing 260 credits! This is how the ranked costs table so far looks, from most to least expensive:

Claude Fable 5 (Preview) = 3,387 credits
Claude Fable 5 (Copilot)(Preview) = 3,373.8 credits

GPT 5.6 Sol = 2,800.50 credits
Sonnet 5 = 2,509.3 credits
Opus 4.8 = 2,487 credits
Opus 5 = 2,240 credits

GPT 5.5 = 1,200 credits
GPT 5.6 Terra = 260 credits

Given that Terra is so much cheaper I’ll need to spend some more time investigating and verifying that with other requests, given that these tests are simply a one shot prompt to result.

Drum roll. The clear winner for this round is:

GPT 5.6 Terra

With all the preliminaries done we can now get onto comparing the winners of each round together.

Why AI Charges More to Write Than to Read

image

I keep seeing people look at AI pricing pages and assume someone has made a typo.

Input tokens cost one amount. Output tokens cost another. Cached input may cost less again. At first glance that feels odd. A token is a token, surely?

Not really.

The easiest way to think about it is this: reading is cheaper than writing. AI can read a lot of your prompt in parallel. It can process the instructions, pasted document, previous conversation and system rules in large chunks. That is the input side.

Writing the answer is different. The model generates the response one token at a time. Each next word, fragment, line of code or JSON field depends on what came before it. The longer the response, the longer expensive compute is tied up producing it.

That is why output tokens usually cost more. You are not just paying for text. You are paying for generation.

This matters more than most people think

Once you move into agents, automation, Copilot Studio, Azure OpenAI, GitHub Copilot, or anything that runs repeatedly, the economics change quickly.

A short prompt that produces a long report can cost more than a large prompt that produces a tiny answer. That surprises people. They assume the big document is the expensive part. Sometimes it is not. The real cost can be the polished, verbose output they asked the model to create.

Ask an AI system to read a SharePoint policy library and return three risks, and you are probably dealing with an input-heavy, output-light workload. That can be relatively efficient.

Ask it to create a 25-page report, an executive summary, a remediation plan, a Teams post, a client email and a formatted table every time it runs, and you have created an output-heavy workload. That is where the bill starts to move.

The MSP lesson is simple: design the output

This is where MSPs need to stop treating AI as magic and start treating it as infrastructure.

When we built servers, we cared about CPU, RAM, disk and backup windows. With AI, we need to care about prompts, context, output length, caching and repeatability.

The bad habit is asking for everything every time. “Give me the full report.” “Include all the detail.” “Make it comprehensive.” That sounds harmless until the same workflow runs fifty times across fifty tenants.

A better approach is to be deliberate. Ask for the smallest useful output first. Use summaries where summaries are enough. Generate detailed reports only when there is a reason. Reuse stable instructions and context where caching is available. Put spending limits around anything consumption-based. Review what the agent writes, not just what it reads.

Inside Microsoft 365, this means being clear about the difference between ordinary Copilot use in Word, Excel, Outlook or Teams and consumption-based AI work that may be billed differently. A user drafting an email is one thing. An agent chewing through documents and producing long artefacts all day is another.

This is not a pricing trick

I do not see the input/output price split as some mysterious vendor tax. It reflects how the technology behaves.

The mistake is pretending it does not matter.

AI costs are not just about how many people have a licence. They are about what those people, agents and workflows ask the model to produce. The output is where the hidden weight often sits.

So the practical rule is this: do not just prompt for the result. Design the cost shape of the result.

That might be the difference between AI being a useful business tool and AI becoming the next cloud bill nobody wants to open.

Comparing LLMs in Copilot services–Round 3 – Cowork (Claude)

image

Round 1 – https://blog.ciaops.com/2026/07/30/comparing-llms-in-copilot-services-round-1-chat/

Winner – Opus

Round 2 – https://blog.ciaops.com/2026/08/08/comparing-llms-in-copilot-services-round-2-researcher/

Winner – Critique

I’ve been pitting different LLMs inside M365 Copilot against each other ina world cup style elimination to see which comes out on top. The process involves taking a standard prompt and running it against all options. This prompt creates a multi page document requiring deep research and is quite involved. The results are then compared against each other using SharePoint Copilot and Gemini. Conclusions are then drawn.

This round the battle is between all the models available in Copilot Cowork Claude. Namely, these:

Screenshot 2026-08-11 074851

The other interesting factor here is that all these models as PAYG, so I’ll also give the costs for the same prompt.

To assess all these results together I found Gemini and SharePoint Copilot to both fail completely. Gemini keeps saying that it can’t find all the document, even though I have uploaded and also tried linking. SharePoint Copilot on the other hand starts processing but never gives me a result, no matter how long I wait. I will admit that these files are quite long (20+ pages) and quite complex, so there is a lot to digest. That said I threw them into Grok and those  results are here:

https://github.com/directorcia/general/blob/master/Copilot/Comparisons/20260811-Cowork-Claude-Grok-eval.md

In summary, the ranking where:

1. Opus 5

2. Fable 5 (Preview)

3. Fable 5 (Copilot) (Preview)

4. Opus 4.8

5. Sonnet 5

which all kind of makes empirical sense but still quite subjective I feel. However, for now I’ll swap to using Grok to evaluate these documents as a standard approach.

Now for the costs which don’t factor into the results:

Claude Fable 5 (Preview) = 3,387 credits
Claude Fable 5 (Copilot)(Preview) = 3,373.8 credits
Sonnet 5 = 2,509.3 credits
Opus 4.8 = 2,487 credits
Opus 5 = 2,240 credits


Which, on initial analysis, indicates that costs for all models are in the same kind of range (i.e. at least US$20 per prompt), with Fable 5 models being about 50% more expensive.

Given the analysis and costs, it seems Opus 5 with Cowork, if you are using Claude, is the most cost effective for the best result in Cowork.

So:,

Round 3 winner – Copilot Cowork Claude = Opus 5

Onto the next round.

Is Copilot Listening to Every Meeting? The Reality Is More Complicated

image

Every few weeks someone asks me a variation of the same question.

“Is Microsoft Copilot recording all my meetings?”

The concern is understandable. AI is becoming more capable, meeting summaries appear almost instantly, action items seem to materialise out of nowhere, and people naturally wonder whether Microsoft is quietly capturing everything that happens in Teams. Sometimes the question goes a step further: “Does it do this even if I don’t have a Copilot licence?”

In my experience, the answer is far less dramatic than many people expect.

The concern isn’t really about Copilot. It’s about visibility.

The Difference Between a Meeting and a Recording

I think a lot of confusion comes from people bundling several different technologies together and calling them all “Copilot”.

A Teams meeting can exist without being recorded.

A Teams meeting can be transcribed without being recorded.

A Teams meeting can be recorded and transcribed.

And Copilot may or may not be involved at all.

That’s an important distinction.

Copilot doesn’t magically create information from nowhere. It works with the information available to it. If your organisation has enabled meeting transcription or recording, that’s where much of the content comes from. Copilot can then use that information to answer questions, create summaries, identify action items and help participants catch up.

Without that underlying data, Copilot has far less to work with.

The key point is that recording and transcription settings are administrative decisions. They aren’t automatically triggered simply because somebody owns a Copilot licence.

What About People Without Copilot?

This is where things often become misunderstood.

I’ve spoken with organisations where only a handful of staff have Microsoft 365 Copilot licences, yet meeting transcripts still exist across the business. When employees see AI-generated meeting summaries, the assumption is that Copilot must be capturing everything.

In reality, those organisations may have enabled Teams transcription or recording policies independently of Copilot.

Think about it this way. Your ability to access information and Microsoft’s ability to store information are not the same thing.

Someone without a Copilot licence may still participate in a meeting that is being recorded or transcribed. The meeting data exists because the meeting organiser or organisation allowed that functionality. Whether an individual user has access to Copilot features is a separate licensing decision.

That’s a subtle difference, but an important one.

The Governance Question Most Businesses Miss

What I find interesting is that many organisations ask whether Copilot is recording meetings, but they rarely ask who should have access to the resulting information.

That’s the more important conversation.

If meeting recordings are stored in OneDrive or SharePoint, if transcripts are available through Teams, and if Copilot can surface information that already exists, then governance matters more than fear.

I’ve seen organisations spend hours debating AI risks while leaving years of meeting recordings scattered across Microsoft 365 with inconsistent permissions.

Copilot didn’t create that problem. It simply made the problem more visible.

A useful exercise is to ask Copilot in Teams or Microsoft 365 a question about a recent meeting and then investigate why the answer was possible. The answer usually points back to existing recordings, transcripts, shared files or meeting notes that were already being retained.

AI acts as a spotlight. It doesn’t necessarily create new information.

The Question I’m Watching Closely

As Copilot adoption grows, I think we’ll see a shift in how organisations think about meetings.

For years, many businesses treated meetings as temporary conversations. Once the meeting ended, most people assumed the discussion disappeared into the ether unless someone took notes.

That assumption no longer holds true.

Whether you’re using Copilot, Teams transcription, meeting recordings or a combination of all three, conversations increasingly become searchable organisational knowledge.

The question therefore isn’t whether Copilot is secretly listening to every meeting.

The better question is whether your organisation understands what meeting data is being captured, where it is stored, how long it is retained and who can access it.

That’s where the real risk lives.

And, just as importantly, that’s where the real value of Microsoft 365 Copilot begins.

Comparing LLMs in Copilot services–Round 2 – Researcher

image

Round 1 – https://blog.ciaops.com/2026/07/30/comparing-llms-in-copilot-services-round-1-chat/

Round 1 Winner – Copilot Chat = Opus

This is round 2 of my Copilot LLM battle, with the aim to determine the ‘best’ result from all the AI services and all the available LLMs used with Copilot in Microsoft 365.

The process involves taking a standard prompt and running it against all options. This prompt creates a multi page document requiring deep research and is quite involved. The results are then compared against each other using SharePoint Copilot and Gemini. Conclusions are then drawn.

This round is all the possibilities with Researcher. As before, rathet than duplicate the reports here I have uploaded them to my Githuv repository in markdown format:

SharePoint Copilot assessment:

https://github.com/directorcia/general/blob/master/Copilot/Comparisons/20260630-Researcher.md

Gemini assessment:

https://github.com/directorcia/general/blob/master/Copilot/Comparisons/20260630-Researcher-Gemini-eval.md

Results (out of 10):

Critique – 9.30

Claude – 8.88

Council – 7.83

Auto – 7.63

Notes – SharePoint and Gemini produced very different rankings. I’m sticking with SharePoint’s assessment for consistency. I will also point out that gettign Gemini to do this comparison and produce a simple markdown file with the result was pretty much impossible. It does a really poor job on this simple comparison ask as you can see from the results.

So the Round 2 winner – Copilot Researcher = Critique

Onto Round 3

Copilot said: What People Say, What They Think, and What They Actually Do

image

Many years ago, David Ogilvy made an observation that still rings true today:

“The trouble with market research is that people don’t think what they feel, they don’t say what they think and they don’t do what they say.”

The older I get, the more I see this playing out in technology.

If you spend any time talking to business owners, managers, or end users about artificial intelligence and Microsoft 365 Copilot, you’ll hear all sorts of opinions. Some people tell me they’re excited. Others say they’re cautious. Many claim they’re waiting to see where the technology goes before investing further.

Then I look at what they’re actually doing.

That’s where things get interesting.

Watching Behaviour Tells You More Than Listening to Opinions

Over the last year I’ve had countless conversations with organisations evaluating Microsoft 365 Copilot. The feedback is often remarkably similar.

“We’re not sure we’ll use it enough.”

“Our staff aren’t ready.”

“We don’t have enough use cases yet.”

“We want to be careful before spending money.”

All perfectly reasonable concerns.

Yet when I sit down with those same organisations and look at how people work, I see something different. Staff are already using AI tools on their phones, in their browsers, and in personal accounts. They’re pasting email drafts into consumer AI services. They’re asking AI to summarise documents. They’re using it to help write proposals, prepare presentations, and analyse spreadsheets.

In other words, their behaviour is already telling me they’ve accepted AI. They just haven’t consciously recognised it yet.

The actions are often ahead of the stated position.

The Same Thing Happens Inside Microsoft 365

One of the most revealing exercises is simply watching how people use the tools they already have.

Someone may tell me they don’t have enough information to write a client proposal faster. Then I ask Microsoft 365 Copilot in Word to create a first draft from existing documents stored in SharePoint.

Someone insists meetings take as long as they’ve always taken. Then Copilot in Teams produces a meeting recap, extracts actions, and highlights decisions in a few seconds.

Someone believes they’re spending hours processing email because that’s the nature of modern work. Then Copilot in Outlook starts drafting replies and summarising lengthy email chains.

The surprising part isn’t that the technology works.

The surprising part is how quickly people adapt once they see it working in their own environment.

What they thought they would do is often very different from what they actually end up doing.

Technology Adoption Has Always Been Emotional

I think we sometimes treat technology decisions as purely logical exercises.

They’re not.

People rarely make decisions based solely on facts, specifications, or business cases. Emotion plays a huge role. Fear of change. Fear of looking foolish. Fear of making the wrong investment. Sometimes even fear of becoming less valuable.

That’s why surveys and focus groups often struggle to predict outcomes accurately. People answer based on how they imagine they’ll behave in the future.

Reality arrives later.

The same pattern appears with Microsoft 365 Copilot. Many organisations start cautiously, perhaps with a small pilot group. The initial conversations focus on risks, limitations, and concerns.

A few weeks later the discussion changes.

Users begin sharing prompts with each other. Teams start discovering new ways to use Copilot in meetings. Managers realise they can get through information faster. Gradually the conversation moves from whether they’ll use AI to where they should use it next.

The behaviour changes first. The mindset follows.

Pay Attention to What Happens, Not Just What Is Said

When I’m helping organisations assess readiness for AI, I place far more value on observed behaviour than survey responses.

The question isn’t whether someone says AI will help them.

The question is whether they reach for it when they’re busy.

Do they use Copilot to prepare for a meeting?

Do they ask Copilot in Outlook to help clear their inbox?

Do they use Copilot in Excel when they need answers from a spreadsheet?

Those actions reveal far more than any questionnaire.

David Ogilvy understood something fundamental about human nature. People are often poor predictors of their future behaviour.

As Microsoft 365 Copilot continues to become part of everyday work, I think we’ll see the same pattern repeat itself. The organisations that succeed won’t necessarily be the ones that talk most enthusiastically about AI.

They’ll be the ones that quietly incorporate it into their daily habits.

Because when it comes to technology adoption, what people do has always mattered more than what they say.