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How to Build an AI-First Business Strategy That Actually Gets Used 

Everyone is dabbling with AI now, right? So, it’s not that your team has never heard of AI. Meanwhile, the work keeps piling up.

We have all read the headlines, seen the demos, and perhaps tested a few tools. Some received a strange answer, lost confidence and quietly went back to the old way.

Yet somehow, client notes still need cleaning. Workpapers still take too long. Emails still get rewritten from scratch. Processes still live in people’s heads. The owner still gets dragged into too many questions.

That is the real starting point for an AI-first business strategy.

Not hype.
Not fear.
Not replacing people.

An AI-first business strategy is a practical plan for using AI as part of everyday business operations, not as an occasional experiment. It helps your business identify where AI can improve workflows, reduce manual work, support team capacity and create measurable value, while keeping human judgement and quality control in place.

The issue is not that your business does not know AI matters.The issue is that AI has not yet become part of how work gets done. 


Why AI adoption fails: the behaviour gap 

Most business owners do not need another article telling them AI is coming.

They know.

But the real question is: Why aren’t we achieving 500% - 1000% productivity gains that some Accountants and Business Owners are claiming?

The answer is usually behaviour change or lack of it!

Your people are busy. They are under pressure and already have a lot to embrace right now. So, they trust the way they already work, even if that way is slow. They forget to pause and ask whether AI could help. Or they worry the answer might be wrong, so they avoid using it altogether.

That is especially true in accounting, bookkeeping, advisory and professional services firms where accuracy matters. Team members do not want to risk looking careless. Owners do not want junior staff blindly relying on AI. Managers do not want another system to supervise.

These concerns are reasonable.

But they are not reasons to stand still.

They are reasons to implement AI properly.

Practical AI adoption for business owners is not about forcing the team to use tools they do not trust. It is about building confidence, setting expectations and showing people where AI can safely support the work they already do. 


An AI-first business strategy does not mean AI-only 

One of the biggest fears around AI adoption is over-reliance.

Owners worry that staff will treat AI as the final answer instead of a first draft. They worry technical responses will not be checked. They worry about privacy, data security and the limits of the technology.

Good.

That caution is useful.

An AI-first business strategy should not remove judgement. It should protect judgement by making the rules clear.

AI can draft, summarise, organise and suggest. Your people still review, verify, decide and own the client relationship.

That distinction matters.

Do not tell the team, “Use AI for everything and see what happens.” That creates risk and confusion.

Instead, set guardrails:

  • AI may create first drafts, but a human approves the final version.
  • AI may summarise information, but source material must be checked.
  • AI may suggest a process, but the business decides the standard.
  • AI may help prepare client communication, but tone and accuracy stay human.
  • AI may support technical work, but professional judgement remains non-negotiable.

That is how you reduce fear without pretending the risks do not exist.

The aim is not to make AI responsible for the work. The aim is to make AI useful within a responsible business system. 


Start your AI implementation strategy with workflow, not hype 

Many owners get distracted by impressive AI demonstrations. They see something clever and think, “We should be doing that.”

Maybe one day.

But the first and best use of AI is often much less glamorous.

Start with workflow.

That is where time, money, and energy leak out of the business every week.

For accounting firms, AI may assist with first-draft workpaper checklists, client query emails, meeting summaries, onboarding workflows, internal training notes, review points, procedure documentation and advisory preparation.

It should not replace technical review or professional judgement, but it can reduce the time spent turning messy information into structured work.

For other business owners, practical opportunities may include quoting, proposals, meeting summaries, customer follow-up, operations checklists, staff onboarding, standard operating procedures and management reporting.

The question is simple:

Where are we doing repeated manual work that could be drafted, structured or improved faster with AI?

That is where the practical gains are.

One owner had already been using AI heavily for research and client notes, but recognised the bigger opportunity was workflow. The real time and money savings were likely in the basic recurring work, not the occasional clever use case.

That is the shift many businesses need to make.

AI is not just a research assistant. It can become a workflow assistant.

A good AI implementation strategy starts by finding the repeated work that slows the business down, then testing whether AI can help create a better first draft, faster process or more consistent output.


Treat AI like a new employee 

A useful way to think about AI is this: treat it like a new employee.

Not a senior expert.
Not a magic machine.

A new employee.

If you gave a new employee vague instructions, no examples and no feedback, you would not expect excellent work. You would expect inconsistency.

AI is similar.

The quality of the input matters. The context matters. The examples matter. The review matters.

If your team says, “AI gives average answers,” the first question should not be, “What is wrong with AI?”

The better question is:

“Did we brief it properly for the task?”

That means giving it:

  • the purpose of the task
  • the audience
  • the desired format
  • the tone
  • the source information
  • the rules it must follow
  • examples of good output
  • clear instructions about what not to do

Then review the result and improve the instruction.

The more your team learns how to brief AI properly, the more useful it becomes.

This is where many businesses underestimate the change required. AI productivity improvement does not come from simply opening a tool and hoping for the best. It comes from teaching the team how to use AI as part of a repeatable business process. 


Build the habit of using AI in daily work 

Many businesses are not failing at AI because the technology is too hard.

They are failing because no habit has formed.

The team opens the same document. Writes the same email. Builds the same checklist. Answers the same question. Manually prepares the same type of material.

Then later someone says, “We probably could have used AI for that.”

That is the habit gap.

A practical rule for the team is:

Before starting repeated work manually, pause and ask: could AI help with the first draft?

Not the final answer. The first draft.

That one pause can change the business over time.

You might add the question to team huddles, workflow review meetings or job close-out checklists. Simple works.

The businesses that become AI-first will not be the ones that attend the most webinars. They will be the ones that build the habit of using AI in the ordinary moments of the day.

This is especially important for firms that want better AI business systems, not just scattered tool usage. A system makes the behaviour repeatable. A tool on its own does not.

ABOUT JOHN


John Peterson, founder of Best Practice Group, offers 30+ years of consulting expertise. With a background as a Fortune 500 management consultant, he specialises in strategy, leadership, and M&A, providing practical insights that enable businesses to overcome challenges, accelerate growth, and secure long-term success. His tailored approach empowers leaders to achieve measurable results and sustainable transformations.

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Productising Your Accounting Firm: Stop Being the Bottleneck 

Tired of being the bottleneck in your own firm? You're not alone, and there's a direct fix. Our AI-Enabled Quality Assurance Program is built specifically to solve this problem. Join the priority list now for first access to the next intake.

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A team member asks, “How do you want this done?”

A client wants “just five minutes”.

A job lands back on your desk because nobody else knows the background, the exception, the client history or the way you prefer it handled.

Then the day disappears.

That is the real reason why productising your accounting firm matters. Not because you need prettier packages. Not because you need a nicer brochure. Because too much of the firm is still living inside your head.

If every service depends on your memory, judgment, and intervention, you do not have a scalable accounting practice yet.

You have a capable expert surrounded by unfinished systems.


Productisation does not make you a commodity

Let’s clear up the biggest misunderstanding.

Productising accounting services does not mean making your firm cheap, generic or transactional. It does not strip the care, judgement, or relationship out of the work.

It means defining your services clearly enough that your team can deliver them consistently, and your clients can understand what they are buying.

A commodity firm looks the same as everyone else because it cannot explain its value.

A productised firm stands apart because it can.

It knows what is included. It knows what is excluded. It knows the process. It knows the price range. It knows who does the work. It knows when the owner needs to be involved, and when the owner absolutely does not.

That is not becoming a commodity.

That is business by design, not business by default.


The owner bottleneck is usually undocumented expertise

Most accounting firm owners do not become bottlenecks through laziness or ego.

They become bottlenecks because they are good.

They know the clients. They understand the exceptions. They can spot the issue quickly. They can fix the mess faster than they can explain it.

So they keep fixing. How do I know this? Because that was me too!

The team learns to wait for you to solve it. Clients learn that the principal is the only capable person on the team. And the owner learns to carry more than they should.

Eventually, the whole firm develops one dangerous operating rule:

“When in doubt, send it to the owner.”

That is not a system. That is dependency.

In many firms, the owner is still wearing too many roles. Winning the work. Managing the work. Doing too much of the technical work.

At the start, that is normal. Over time, it becomes the ceiling.

The question is not whether you are working hard. The question is whether the business still needs you in the room to function.


Start with a clear list of what you actually sell

The practical starting point is not a new app, a new hire, or a grand transformation plan.

Start by listing every product and service your firm provides.

Not just “tax”. Break it down properly.

For example:

  • Individual tax returns
  • Company tax returns
  • Trust returns
  • BAS preparation
  • Bookkeeping
  • Payroll
  • SMSF administration
  • Management reporting
  • Business advisory meetings
  • Client onboarding
  • Engagement renewals
  • Fee reviews
  • Client information follow-up

Then add three columns.

  1. Percentage of revenue
    How much of your total fee base does this service represent?
  2. Difficulty to deliver
    Score it from easiest to hardest.
  3. Delegation potential
    Could a trained team member deliver most of this with the right checklist, training and review process?

This changes the whole scaling conversation.

Instead of asking, “Should I employ one person or two?”, you start asking, “Which work should this person be trained to do first?”

And leads to...“and what work do I want to win more of?”

These are much better questions.


Do not delegate the hardest work first

A common mistake is hiring someone and handing them the work the owner most wants to escape.

Usually, that is the messiest work.

Complex advisory. Difficult clients. Poorly scoped jobs. Historical clean-ups. Half-finished files with the context sitting in the principal’s head.

Then the new person struggles, and the owner quietly concludes, “No one can do it like I can.”

Often, that is not the real issue. The real issue is that the work was never made teachable.

Start with lower-complexity, repeatable services first.

Good potential products & services candidates might include:

  • BAS workflow preparation
  • Standard bookkeeping processes
  • Client information request steps
  • Payroll checklists
  • Basic compliance preparation
  • Document follow-up
  • Engagement letter preparation
  • Invoicing and job closure processes

Get those working. Train the team. Add review points. Build confidence. Then move up the complexity ladder.

A useful principle to hold onto here is this:

80% of what I do can be done by somebody else, 100% of the time.

That does not mean your team can replace your judgement overnight. It means most of the work sitting on your desk probably does not need to start there, stay there or come back there every time.

The owner’s role is not to keep proving they can do the work. The owner’s role is to make the work teachable. 


Use AI to build the first draft of your systems

 AI for accountants becomes powerful when you point it at the right problem.

The best starting point is not the exciting client-facing idea. It's boring internal work nobody has had time to document.

Use AI to help create the first draft of:

  • Workflow checklists for accountants
  • Service delivery steps
  • Client onboarding sequences
  • Client information request lists
  • Inclusions and exclusions
  • Training notes
  • Review checklists
  • Pricing tier options
  • Internal quality control steps
  • Client email templates

For example, you might ask an AI tool to draft the subcategories and workflow checklist for a quarterly BAS service.

Then you review it.

You remove what does not fit. You add your standards. You adjust the wording. You make sure it reflects how your firm actually works.

AI gives you the first draft. Leadership turns it into a real system.

That distinction matters. Do not outsource judgement to AI. Use it to reduce the blank-page problem and accelerate the systemising work you have been avoiding.

 

Use a traffic-light test to find AI workflow automation opportunities 

 If your team is unsure where to start, use a simple traffic-light test.

List 10 to 20 recurring tasks in your business. Then mark each one as red, yellow or green.

  • Red: high-value AI opportunity. This is repeated, time-consuming, easy to review and currently slowing the business down.
  • Yellow: possible AI opportunity. This could be useful, but needs more thought, clearer guardrails or better source material.
  • Green: leave it alone for now. This is either too sensitive, too complex, too rare or not worth improving yet.

Start with the red tasks.

Good red-task examples might include:

  • drafting client follow-up emails
  • summarising meetings into action lists
  • preparing workpaper checklists
  • creating onboarding steps
  • writing first-draft procedures
  • assembling training notes

This stops AI adoption becoming vague.

Instead of saying, “We need to use AI more,” you can say, “This week, we are testing AI on these two red tasks.”

That is much easier for a team to act on.

It also makes AI workflow automation more practical. You do not need to automate the whole business. You need to identify the right recurring tasks, improve them one at a time and build confidence through use. 


How to measure ROI from AI in your business 

Pretty outputs do not pay the bills.

Your AI implementation strategy needs a return.

That return does not always need to be complicated. Start with three practical measures.

1. Time saved

Did this reduce the time taken to complete the task?

Even saving three to five hours a week across admin, preparation or review work can be significant.

For example, if a team saves 30 minutes across six recurring weekly tasks, that creates three hours of capacity every week. Over a quarter, that becomes meaningful time that can be redirected into client service, review quality, staff development or advisory work.

2. Rework reduced

Did the output improve consistency?

Are fewer things coming back unclear, incomplete or poorly structured?

AI can be useful when it helps the team start with a clearer draft, a better checklist or a more complete summary. That does not remove review, but it can reduce avoidable back-and-forth.

3. Capacity created

Did the owner, manager or senior team member get time back for higher-value work?

That third measure matters most.

The real competitive advantage is capacity.

Capacity to serve clients faster. Capacity to train staff. Capacity to improve quality. Capacity to develop advisory services. Capacity to make room for new opportunities.

For accounting and advisory firms, this matters because the pressure on compliance work is unlikely to disappear. The firms that use AI well will not simply be faster. They will have more room to think, advise, improve and lead.

AI should help create that space.


A 21-day plan to build an AI-first business strategy 

Do not try to transform the whole business this month.

Start smaller.

Here is a 21-day plan.


Days 1–3: Ask the team one question

“What repeated task should we automate, instead of doing from scratch?”

Capture the answers without judgement.

The aim is not to find the most impressive AI use case. The aim is to find work that is repeated, frustrating and suitable for a better first draft.


Days 4–6: Pick one task

Choose something repeated, low-risk and easy to review.

Avoid the most technical or sensitive task first.

Good starting points might include an internal checklist, meeting summary format, client follow-up email, onboarding step or procedure template.


Days 7–10: Create the first AI-assisted version

Use AI to draft a checklist, template, workflow, summary format or email structure.

Give it context. Provide examples. Tell it what good looks like.

Do not expect the first version to be perfect.


Days 11–14: Review and improve it

Have the people who do the work test it.

Ask:

  • What is missing?
  • What is unclear?
  • What is impractical?
  • What needs to be checked by a person?
  • What instruction would improve the next version?

This is where the business turns AI output into a usable system.


Days 15–18: Use it on real work

Do not leave it as a document in a folder.

Put it into the workflow.

Add it to the checklist, template, project management system or team process where the work actually happens.

This step matters because AI adoption often fails between “good idea” and “daily use”.


Days 19–21: Measure the result

Ask:

  • Did it save time?
  • Did it reduce rework?
  • Did the team feel more confident?
  • Should we keep, improve or discard it?
  • What task should we test next?

Then repeat with the next task.

This is how AI adoption becomes operational.

One task. One workflow. One habit. 


The AI competitive advantage will reward businesses that keep moving 

The AI gap will not be created by one dramatic breakthrough.

It will be created by repetition.

One business keeps testing. Another keeps waiting. One team learns how to brief AI. Another still worries in silence. One owner builds workflow capacity. Another stays trapped in manual review.

One firm uses AI to make space for advisory. Another remains stuck in the same compliance pressure with less margin for error.

You do not need to be perfect.

You do need to move.

Start with the work that is repeated, frustrating and easy to review. Train the team. Keep humans in control. Measure the return. Build the habit.

That is what an AI-first business strategy looks like in real life.

Not noise. Not novelty.

A calmer, more capable business that uses AI to create capacity where it counts.

 

The AI gap is widening. Make sure your firm is on the right side of it.

The AI-Enabled Quality Assurance Program helps firms use practical, ROI-driven AI to reclaim time, lift review quality and push more work down to an empowered team. It combines AI with the fundamentals that make it stick: clearer workflows, stronger systems and better quality assurance.

👉 Join the priority list here for early updates before the full program campaign begins.

Or contact us directly for a complimentary discussion:

📧 team@bestpracticegroup.com.au
📞 1300 274 636

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ABOUT JOHN


John Peterson, founder of Best Practice Group, offers 30+ years of consulting expertise. With a background as a Fortune 500 management consultant, he specialises in strategy, leadership, and M&A, providing practical insights that enable businesses to overcome challenges, accelerate growth, and secure long-term success. His tailored approach empowers leaders to achieve measurable results and sustainable transformations.

CONNECT WITH JOHN

Everything you need to know about business and beyond


Become a BP insider! Sign up for exclusive content, emails & things that John doesn’t share anywhere else.

Get your guide to business mastery today!

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