Artificial intelligence and intelligent automation have moved quickly from emerging technologies to serious business considerations. Nearly half of Canada’s large-company CEOs now believe growth no longer depends on growing headcount. That is one of the headline findings from EY’s latest CEO Outlook Survey, reported by CBC News on October 28, 2026.

The survey involved polling of 1,200 CEOs in 21 countries in August and September 2026. Among Canadian respondents at large companies, 46% said many companies will reach their long-term growth targets with a smaller workforce than they have today.

It is a striking number, and it has sparked plenty of debate about what AI means for Canadian jobs. But for most leadership teams, the more useful question is not whether AI will change how work gets done. It is how to make sound decisions about it now: which business processes represent the strongest opportunities, where automation can deliver measurable value, and how to validate that value before committing to a broader initiative.

Where can AI and intelligent automation create business value?

There may be dozens of processes that look like candidates for automation. Some are repetitive and labour intensive. Others involve moving information between systems, reviewing documents, responding to routine requests, or making decisions based on large amounts of data.

But not every process that can be automated should be automated.

Behind the headline is a practical question every organization should be asking:

Where, specifically, can automation create value for us, and how do we know it will work before we invest?

Too often, AI investments are driven by pressure to keep up rather than by evidence. The pattern is familiar:

• Initiatives that never move beyond the experimental stage
• Automation initiatives launched before the business problem and desired outcome are clearly defined.
• Unclear return on investment, making it hard to justify further spending
• Teams left uncertain about what is changing and why

The research shows the pressure is real. In the spring 2026 Canadian survey, 80% of Canadian CEOs planned to increase AI spending this year, and none planned to cut it. With that much investment on the line, the cost of getting it wrong is significant.

Choosing the wrong starting point can result in a technically successful project that produces little measurable business value. Starting with the solution rather than the business problem can create a similar issue: an organization launches an automation initiative before clearly establishing the process problem, desired outcome and measure of success.

AI and intelligent automation are about people, too

“Smaller workforce” makes for an attention-grabbing headline, but the full picture is more nuanced. For a lot of public sector and even private companies, this is usually less about cutting headcount and more about handling growing backlogs and legacy system bottlenecks with the staff they already have.

In the spring Canadian survey, 46% of CEOs ranked reskilling and upskilling existing employees among their top two expected workforce impacts of AI, and 34% cited redesigning roles to combine human and AI capabilities. Only 4% of surveyed Canadians graded themselves as having an “A” in AI skills, while 75% give themselves a grade of “C” or lower, according to the 2026 TD AI Insights Report this past summer.

The most effective automation programs free people from manual, repetitive work so they can focus on judgement, relationships, and problem-solving. Getting there takes more than technology. It requires a clear understanding of your processes, honest assessment of readiness, and a plan that brings your people along.

What is intelligent automation?

Intelligent automation brings together technologies such as artificial intelligence (AI), machine learning (ML), robotic process automation (RPA), workflow automation and other digital tools to automate business processes. Whereas traditional automation is generally based on predefined rules, intelligent automation can also support more complex or variable processes.

That makes the potential applications broad.

For example, an organization might have opportunities to reduce repetitive administrative work, improve the accuracy of a high-volume process, automate steps within a workflow, or use AI to support activities that previously required people to interpret information.

The harder question is: Which of those opportunities represents the best business case?

How to identify the right opportunities for AI and intelligent automation

One of the biggest mistakes organizations can make is starting with a predetermined solution and then looking for a business problem to justify it.

Instead, intelligent automation initiatives should begin with the business priorities and processes, then examine the data, systems and operating environment needed to support the strongest opportunities. A useful opportunity assessment asks questions such as:

• Where are employees spending significant time on repetitive work?
• Which processes involve high volumes of transactions or information?
• Where do manual steps create delays, errors or unnecessary effort?
• Which processes have the data, systems, access and operating conditions required to support practical automation?
• What measurable outcome would improve if the process were automated?
• How difficult would the opportunity be to implement?
• Is the potential benefit significant enough to justify the investment?

Looking across those factors helps separate an interesting automation idea from a viable business opportunity.

Why a proof of concept matters

Even a strong business case involves assumptions.

A process may appear straightforward until it is tested against real data, existing systems and actual user requirements. Expected efficiencies may look compelling on paper but still need to be demonstrated in practice.

That is where a proof of concept can be valuable.

Rather than asking leadership to commit immediately to a broader automation initiative, a proof of concept allows a carefully scoped use case to be implemented and tested against defined business outcomes. The goal is not simply to demonstrate that the technology works. It is to determine whether the automation can produce the business outcome that justified pursuing it in the first place.

That requires establishing success measures before the proof of concept begins, testing the solution with users, measuring its performance, and comparing the results with the original baseline.

The result is better evidence for the next investment decision.

Moving from an idea to a practical Automation Roadmap

This is the problem HPN’s Intelligent Automation Opportunity Assessment & Proof of Concept Service is designed to address.

Rather than approaching intelligent automation as a large transformation initiative from day one, the engagement provides a structured way to identify opportunities, select a strong candidate, demonstrate its value, and determine what should happen next. It is a structured, outcomes-focused engagement that helps organizations move from curiosity about AI, machine learning, and robotic process automation (RPA) to proven business value, within 90 days.

We start with your business. Our methodology evaluates potential opportunities based on business impact, feasibility, complexity and expected return on investment, helping identify where intelligent automation has a credible case for delivering measurable value.

The service follows five phases:

1. Discovery and readiness assessment: The first step is understanding the organization’s current environment. HPN assesses processes, technology, data and organizational readiness to establish where intelligent automation can realistically be applied and where it is most likely to deliver value.
2. Opportunity identification and prioritization: Potential automation opportunities are identified and assessed based on factors such as business impact, feasibility, complexity and ROI. Rather than producing a collection of possible ideas, the objective is to create a prioritized set of opportunities and identify a strong candidate for the proof of concept.
3. Proof-of-concept design: Once a use case has been selected, HPN defines the proof of concept in detail. The process is mapped, technical and data requirements confirmed, the operating environment is assessed, success measures are established, and implementation is planned. This establishes both the technical feasibility and business definition of what the proof of concept is expected to demonstrate before implementation proceeds.
4. Proof-of-concept implementation: A working automation is then developed and tested in the client’s environment. The methodology includes development and configuration, user testing and feedback, performance measurement, refinement, and knowledge transfer with client stakeholders.
   This is an important distinction too. The engagement does not end with an assessment or a presentation. It puts one of the identified opportunities into practice so its potential value can be evaluated using actual results.
5. Value realization and roadmap development: Finally, the results are compared against the established measures. Benefits and ROI are validated, and the organization receives a practical roadmap for future intelligent automation opportunities.

HPN’s overall engagement is designed to move through this process in approximately 90 days, from initial assessment through the working proof of concept and roadmap.

What do you have at the end?

The objective is not to just leave the organization with another list of automation ideas. At the end of the engagement, clients receive:

• An intelligent automation readiness assessment
• A prioritized portfolio of automation opportunities
• A working proof of concept
• A value realization and ROI report
• A practical roadmap for scaling intelligent automation

Together, these deliverables give leadership something considerably more useful than an abstract discussion about the potential of AI and automation: evidence they can use to make an informed investment decision.

You don’t need to have your automation strategy figured out before you start

In fact, uncertainty about where to begin is one of the problems the assessment is intended to address.

Organizations that rely on manual, repetitive or high-volume processes, are exploring AI or automation but want evidence before making a larger investment, or have completed pilots without establishing a path to scale are among those the service is designed for.

HPN’s approach is business-led rather than product-led. We begin by identifying automation opportunities based on business value, feasibility and measurable outcomes using the client’s available technology environment and requirements. HPN consultants have experience across a variety of industry-leading platforms, including Microsoft Power Automate, UiPath, Automation Anywhere and Blue Prism. Our focus is on identifying the business opportunity first and determining an appropriate automation approach from there, rather than starting with a predetermined product.

Are you ready to explore what AI and intelligent automation could do for you?

The organizations that benefit most from AI will not be the ones that move fastest. They will be the ones that move with evidence. Whether you are just starting to explore automation or looking to scale beyond early pilots, we can help you find the opportunities that matter and prove their value before you invest.

Contact our team today to start your Intelligent Automation Opportunity Assessment & Proof of Concept.

Book A Meeting directly with our Account Executives or fill out the contact form below:

Written by

Ashlee Espenell

Director of Marketing, Community, & Product at Hilltop Partner Network