What worked, what didn’t, and why 2027 could separate the businesses experimenting with AI from those actually transforming with it.
A couple of years ago, the big question business owners were asking me was whether they should be using AI.
In 2026, that question became almost irrelevant.
AI is everywhere. It’s in Microsoft 365, customer service platforms, accounting applications, CRM systems, and just about every other piece of software businesses rely on.
Vendors can’t seem to release a product without attaching the letters AI to it.
And businesses have been buying in.
But here’s what I’ve found interesting about 2026.
Despite all the investment, excitement, and experimentation, a surprising number of organizations still struggle to explain what AI has actually done for their business.
Sure, employees are writing emails faster. Marketing teams are generating content. Someone in accounting discovered they can analyze a spreadsheet in seconds.
That’s great.
But has the business become more profitable? Have operating costs decreased? Has customer service improved? Have employees gained meaningful capacity? Has AI created opportunities that didn’t exist before?
Those are much tougher questions.
And they’re exactly the questions business leaders should be asking.
2026 wasn’t just another year of AI breakthroughs. It was the year the conversation started shifting from what AI can do to what AI is actually worth.
As we look toward 2027, I believe we’re approaching another important turning point.
The businesses that succeed with AI won’t necessarily be the ones purchasing the most sophisticated tools.
They’ll be the ones willing to rethink how their businesses operate.
And that requires a lot more than an AI subscription.
1. AI Adoption Exploded. Business Value Didn’t Always Follow.
Let’s start with the numbers.
According to Statistics Canada, 19.2% of Canadian businesses reported using AI to produce goods or deliver services in the 12 months preceding its second-quarter 2026 survey.
That’s more than triple the 6.1% reported in the second quarter of 2024.
And the momentum continues. In the third quarter of 2026, 25.2% of Canadian businesses indicated plans to use AI to produce goods or deliver services within the following 12 months.
That’s significant progress, although these statistics don’t capture every employee independently using ChatGPT or other AI assistants.
AI is clearly moving beyond the early adopters.
But adoption statistics only tell us how many businesses are using the technology. They don’t tell us whether those businesses are getting meaningful results.
And that’s an important distinction.
I’ve seen organizations invest in AI tools without establishing any real objectives beyond encouraging employees to use them.
The result?
Lots of experimentation. Plenty of enthusiasm. Some impressive demonstrations.
But very little understanding of the return on investment.
Think about an employee who uses AI to complete a task in 10 minutes instead of an hour.
That’s a tremendous productivity improvement.
But what happens to those 50 minutes?
If the employee uses that time to deliver additional work, serve more customers, or create something valuable, the business benefits.
If the underlying workflow remains unchanged and the saved time produces no additional value, the financial benefit may be negligible.
Individual productivity is not the same as organizational productivity.
That’s one of the most important lessons businesses should take from 2026.
The objective isn’t to have more employees using AI.
It’s to build a more productive, profitable, and competitive organization because of AI.
2. The AI Projects That Struggled Often Started With the Technology Instead of the Problem
There has been no shortage of enthusiasm for AI projects.
And no shortage of projects that have struggled to deliver what was promised.
Some organizations bought hundreds of AI licences before figuring out what employees would actually use them for.
Others launched impressive pilots that never made it into everyday operations.
And some attempted to automate processes that were fundamentally inefficient to begin with.
Here’s the problem.
Automating a bad process doesn’t necessarily create a good process. Sometimes it just allows you to do the wrong things faster.
Imagine a company where employees manually retrieve information from five different systems, combine it in a spreadsheet, and send it to a manager for review.
You could introduce AI to help prepare that spreadsheet.
And perhaps reduce the preparation time significantly.
But have you solved the underlying problem?
Not really.
The information is still fragmented across five systems. The workflow is still inefficient. And the organization is still dependent on a process that probably should have been redesigned years ago.
A better approach might involve integrating those systems, automating information retrieval, and allowing AI to identify exceptions, generate insights, and prepare recommendations for review.
Now you’re solving a business problem rather than simply making an existing task faster.
There’s another challenge.
AI demonstrations can be incredibly impressive.
Making something work once in a controlled environment is relatively easy compared with making it work reliably, securely, and consistently across an entire organization.
Real business environments contain messy data, undocumented processes, inconsistent decisions, exceptions, security restrictions, and people who don’t always follow the instructions.
That’s where many promising AI projects encounter reality.
The lesson?
Start with the business problem.
Understand the workflow. Identify the bottlenecks. Establish what success looks like.
Then determine whether AI is the right solution.
And remember, sometimes the best solution isn’t AI at all.
3. AI Agents Changed the Conversation From Answering Questions to Getting Things Done
If there was one development in 2026 that I believe deserves particular attention, it’s the evolution of AI agents.
For the past few years, most people have experienced AI through a relatively simple interaction.
You ask a question. AI gives you an answer.
You provide a document. AI summarizes it.
You request an email. AI writes one.
Useful? Absolutely.
But AI agents introduce a fundamentally different capability.
Instead of simply generating information, agents can be designed to interact with applications, retrieve information, follow workflows, and perform sequences of actions.
Consider a finance department.
Today, an accountant might manually download information from several systems, reconcile transactions, investigate discrepancies, and prepare a report.
Now imagine an appropriately configured AI agent that can access authorized systems, retrieve the required information, perform preliminary reconciliation, identify anomalies, and prepare an exception report for human review.
The accountant isn’t simply working faster.
The nature of the work itself is changing.
Or consider a customer service organization where an AI agent retrieves account information, identifies the nature of a request, consults internal documentation, prepares a response, and initiates approved actions within defined limits.
These are examples of what properly designed agentic workflows can enable, not claims that every AI product can reliably perform them today.
And that’s an important distinction.
The technology is improving rapidly, but reliability, oversight, and integration remain significant challenges.
In September 2026, Gartner published research suggesting that much of the tangible value from agentic AI would come from specialized agents designed for specific business processes rather than broad, general-purpose agents.
That makes sense to me.
Businesses don’t necessarily need one magical AI agent that can do everything.
They need reliable solutions that solve specific problems.
But there’s another side to this development that business leaders cannot ignore.
An AI that can answer questions is one thing. An AI that can take actions inside your business is something entirely different.
Which brings us to perhaps the most important lesson of all.
4. AI Security and Governance Are No Longer Optional
Think about the implications of giving an AI agent access to your business systems.
It might be able to read confidential documents, retrieve customer information, interact with financial applications, send communications, or modify records.
Now imagine that agent makes a mistake.
Or misunderstands an instruction.
Or encounters a malicious instruction hidden inside a document, email, or website.
Or has access to information it should never have been permitted to retrieve.
Suddenly, AI security becomes much more than reminding employees not to paste confidential information into ChatGPT.
We’re introducing software that can potentially make decisions and execute actions on our behalf.
And traditional cybersecurity controls weren’t necessarily designed with this level of autonomy in mind.
This is something I’ve been emphasizing in conversations about AI adoption.
You cannot simply deploy AI agents and assume the security controls protecting your employees will automatically protect your business from everything an agent might do.
Organizations need to think carefully about AI identities, permissions, data access, monitoring, audit trails, and approval requirements.
An AI agent shouldn’t have unrestricted access to your financial systems simply because it needs to retrieve a report.
It shouldn’t be able to send sensitive information externally without appropriate controls.
And it certainly shouldn’t be making significant financial or operational decisions without clearly defined authority and oversight.
The principle is straightforward.
Give AI the minimum access necessary to perform its assigned tasks, establish boundaries on what it can do, and ensure there’s a way to monitor, intervene, and recover when something goes wrong.
We also need to address what I consider one of the most overlooked risks: shadow AI.
Employees are discovering AI tools independently, often without involving their IT or security teams.
They may connect those tools to company applications, upload confidential information, or grant access to business data without understanding the implications.
Their intentions are usually good.
They’re simply trying to get their jobs done faster.
But good intentions don’t eliminate risk.
For business leaders, the answer isn’t necessarily to ban everything.
It’s to establish an environment where employees can benefit from AI safely, with clear policies, approved tools, appropriate safeguards, and meaningful oversight.
The organizations that get this right will be better positioned to adopt more advanced AI capabilities with confidence.
5. AI Readiness Turned Out to Be a Business Problem, Not Just a Technology Problem
Here’s something that surprises many business owners.
You can purchase some of the most advanced AI technology available and still achieve disappointing results.
Why?
Because AI doesn’t magically fix the problems already inside your business.
If your information is scattered across disconnected systems, AI may struggle to access what it needs.
If your data is inaccurate or poorly organized, AI may produce equally unreliable results.
If your processes are undocumented, automation becomes more difficult.
If employees don’t understand the technology, adoption suffers.
And if leadership hasn’t established clear objectives, nobody really knows whether the investment is working.
This is why I believe AI readiness needs to be approached much more broadly than simply evaluating software.
It requires looking at five areas:
- Business Processes: Do we understand how work gets done, and which processes are worth improving?
- Data and Integration: Is our information accurate, accessible, and connected across the systems that need it?
- Technology Infrastructure: Do we have the applications, architecture, and capabilities required to support AI?
- Security and Governance: Can we use AI while maintaining appropriate control over our information and operations?
- People and Leadership: Do employees have the skills, support, and direction to adopt new ways of working?
All five matter.
Statistics Canada’s second-quarter 2026 findings showed that 44.4% of businesses using AI had made changes to their training or staffing practices because of the technology.
That’s encouraging, but there’s still an important difference between training employees to use an AI tool and helping an organization change how it works.
For example, teaching someone how to write better prompts may save them time.
Redesigning an entire workflow so information moves automatically between systems, exceptions are identified, and people focus on higher-value decisions can create substantially more value.
The second requires a different level of planning, expertise, and organizational commitment.
AI transformation isn’t simply about making your existing workforce faster. It’s about rethinking what your workforce can accomplish.
And that brings us to 2027.
What I Believe Comes Next: Three Predictions for 2027
Nobody can predict exactly where AI will be twelve months from now.
The technology is evolving too quickly for that.
But based on what we’ve seen, I believe three developments will increasingly shape how Canadian businesses approach AI.
Prediction 1: AI Agents Will Become Part of Everyday Business Operations
I expect we’ll see more AI agents embedded into the applications businesses already use.
Rather than opening a separate chatbot and asking it to perform a task, AI capabilities will increasingly become part of everyday workflows.
Some will retrieve information. Others will coordinate activities between applications. More sophisticated implementations will perform approved actions and handle increasingly complex processes.
Not all of these agents will deliver on their promises.
But the direction is clear.
We’re moving from AI that helps people do work toward AI that can perform portions of the work itself.
That’s an enormous opportunity.
And it will require businesses to become much more deliberate about how they manage technology, information, and risk.
Prediction 2: Businesses Will Stop Celebrating AI Adoption and Start Demanding ROI
I believe the days of considering an AI project successful simply because employees are using it are numbered.
Business leaders will increasingly demand answers to much more practical questions.
How many hours have we saved?
Have we increased capacity without proportionally increasing costs?
Are we serving customers faster?
Have we reduced errors?
Has AI helped us generate additional revenue?
Are we making better decisions?
And perhaps the most important question: Are these benefits greater than the total cost of implementing and operating the solution?
That last question matters because AI costs extend beyond software subscriptions.
There are implementation expenses, integrations, security requirements, ongoing monitoring, employee training, and maintenance.
An AI project that saves $20,000 annually but costs $40,000 to operate isn’t necessarily a success.
The organizations that understand this will approach AI with greater discipline.
They’ll start with measurable business objectives, establish baselines, evaluate results, and expand the initiatives that demonstrate value.
And they’ll be willing to abandon the ones that don’t.
Prediction 3: The Biggest Competitive Advantage Will Come From Redesigning the Business Around AI
This is the prediction I’m most interested in.
I believe the next major competitive divide won’t simply be between businesses that use AI and those that don’t.
It will be between businesses that use AI to make existing processes a little faster and those that fundamentally rethink how their operations should work.
Consider two competing professional services firms.
One equips its employees with AI assistants to help write emails, summarize documents, and prepare reports.
The other examines its entire service delivery model.
It integrates its systems, automates routine information gathering, uses AI to identify anomalies and opportunities, and redesigns workflows so employees can spend more time advising clients.
Both companies are using AI.
But they’re not pursuing the same opportunity.
The first is improving individual productivity.
The second is building the potential for a fundamentally different operating model.
Over time, that difference could become substantial.
This doesn’t mean every organization needs to launch an expensive transformation initiative tomorrow.
In fact, I’d argue the opposite.
Start small. Identify meaningful opportunities. Build the right foundation. Demonstrate value. Then expand.
But start thinking beyond the chatbot.
Because the real opportunity isn’t simply doing yesterday’s work faster.
It’s discovering what your business could accomplish if you were no longer constrained by the way work has always been done.
What Should Canadian Business Leaders Do Right Now?
As you plan for 2027, I would encourage you to resist the temptation to start by asking which AI tools you should purchase next.
Instead, take a serious look at how your organization operates today.
Where are employees spending excessive time on repetitive work?
Where does information get trapped between disconnected applications?
Which processes are creating bottlenecks?
What valuable information do you already possess but aren’t effectively using?
Which business activities could become more scalable, accurate, or profitable with better technology?
And where could AI introduce risks that your existing governance and security controls aren’t prepared to manage?
You don’t need to solve everything at once.
Choose a few opportunities that matter.
Assign ownership. Establish measurable objectives. Make sure the technology and security foundations are in place.
And treat implementation as a business improvement initiative, not simply another IT project.
At Expera IT, this is exactly why we’re expanding our focus beyond traditional managed IT and cybersecurity into helping businesses prepare for, adopt, secure, and operationalize AI.
Because AI strategy, cybersecurity, data management, systems integration, and business transformation can no longer be treated as completely separate conversations.
They’re increasingly interconnected.
And businesses need advisors who understand how those pieces fit together.
Final Thoughts: The AI Frontier Won’t Wait
2026 taught us that having access to AI doesn’t automatically create a competitive advantage.
Buying licences isn’t transformation.
Running pilots isn’t transformation.
And having employees use AI to write emails faster, while useful, is only scratching the surface of what’s possible.
I believe 2027 will increasingly be about turning AI’s potential into real business outcomes.
Some companies will continue experimenting with tools and celebrating small productivity improvements.
Others will begin redesigning their operations, building new capabilities, and discovering ways to deliver value that weren’t previously practical.
The gap between those two approaches could become one of the most important competitive differentiators of the coming years.
2026 was about discovering what AI could do.
2027 needs to be about changing what your business can do because of AI.
The technology is moving quickly.
The opportunities are real.
And the businesses that approach AI with clear objectives, a strong foundation, and the willingness to rethink how they operate will be in the best position to benefit.
The question is no longer whether AI will change how your business operates.
It’s whether you’re going to lead that change or spend the next few years trying to catch up.
Is Your Business Ready for What’s Next?
Expera IT helps Canadian businesses move beyond AI experimentation through strategic technology planning, AI readiness assessments, cybersecurity, governance, systems integration, and practical adoption strategies focused on measurable business results.
