March 19, 2026
Many mid-market leaders feel confident about how AI is showing up in their organizations. This article takes a closer look at what sits beneath that confidence. Based on findings from The Business of AI 2026, it looks at how organizations describe progress, where AI is being put to work, and why momentum can become harder to maintain as adoption scales. The focus shifts from early efficiency gains to the more practical questions leaders are now working through as AI begins to influence competition, decisions, and priorities.
There’s a growing sense of confidence in how Canadian mid-market leaders talk about AI. Not the hype-driven kind, but the quieter confidence that comes from seeing it deliver. Teams are working faster. Routine tasks take less time. Information moves more easily across organizations. For many businesses, AI feels established enough to rely on. And that confidence is well earned. But it’s also incomplete.
When leaders describe progress with AI, how are they defining it? Are those signals capturing strategic adoption across the organization, or are they largely reflecting early gains tied to productivity? And would the people on the ground such as employees and technical teams, describe their maturity the same way?
Those questions are what surfaced most clearly in the MNP Digital Report: The Business of AI 2026. Looking across 250 responses from business leaders across Canada, it becomes clear that progress is being defined by how confident organizations feel, how far they believe they have come, and how prepared they are for what comes next. So, where do businesses really sit on the AI maturity curve?
Respondents from the survey often described AI adoption in confident terms. Nearly half of organizations, 48 percent, characterize their AI adoption as operational, and almost double the amount report they’re satisfied with their progress to date (91%) — an overwhelmingly positive finding.
Participants also say, 60 percent of their AI implementation takes the form of a company-wide, platform-based solution, while over half (58%) intend to embed AI directly within their existing SaaS environments.
Internal capability is also taking shape. More than eight in 10 organizations (82%), report having appointed AI builders or champions to support adoption and momentum. Larger-revenue organizations are more likely than smaller ones to have these dedicate roles in place, suggesting a step-up in investment.
Yet when asked to look more closely at maturity, those assessments become more restrained. Only 17 percent of organizations describe their AI use as systemic, and just four percent believe it’s transformational, in terms of acting as a strategic differentiator.
The contrast is telling. Many organizations are investing, deploying, and scaling AI in practical ways. Far fewer feel they have reached a point where AI is reshaping how the business operates or creates real enterprise value. What looks like optimistic progress actually has many stages of maturity underneath.
Quick takeaways:
One way to understand how organizations define progress is to look at where AI is being used.
For most organizations, AI is showing up in ways that are immediately useful. According to the report findings, 66 percent of organizations are applying AI to improve productivity and operational efficiency today, and 61 percent plan to extend those efforts further. These are the moments where AI earns trust quickly, by helping teams move through work with less friction and fewer manual steps.
In many cases, that uplift comes from practical applications such as summarizing meeting notes, drafting emails, or accelerating document analysis. These gains are real, and build early headway, though they remain modest at this stage of value creation.
The tools themselves represent that practicality. Almost half of organizations, 49 percent, rely on a mix of custom-built and off-the-shelf solutions. Rather than waiting for a single, perfect system, many are choosing what fits into existing workflows to deliver now.
What stands out is not what organizations are doing, but what they are doing it for. Productivity-led use cases dominate, not because they carry the greatest strategic weight, but because they’re the easiest place to start. They offer visible results without a complete structural overhaul.
That creates a subtle tension. When AI is primarily used to make work faster, it can be difficult to tell whether the organization is simply accelerating existing processes or beginning to change how decisions are made and value is created. Over time, that gap becomes harder to ignore.
What separates incremental improvement from meaningful transformation isn’t the tool itself, but how deeply it changes the way work flows. Replacing a single manual step with AI may reduce effort. Redesigning the entire process around real-time insight can change how decisions are made, how customers are served, and how the business competes.
As AI use expands, leaders are confronted with a clear question. Which of these use cases deserve continued investment, and which are helping teams work more efficiently without moving the business in a new direction? The answers count, because it shapes whether AI remains a helpful tool or becomes something more enduring.
Quick takeaways:
For all the confidence around adoption and use, progress with AI isn’t frictionless. More often than not, obstacles show up as a collection of pressure that compounds over time.
Many teams are learning while moving, often without clear guidance on how AI should be used, governed, or scaled responsibly. Most organizations cited limitations in AI skills or understanding as a barrier (78%), a reminder that access to tools doesn’t automatically translate into capability.
As AI becomes more embedded, technical concerns also rise. Cyber security, data integrity, and integration challenges are each cited by 40 percent of organizations, reflecting the growing complexity of connecting AI to core systems and sensitive information. The more central AI becomes, the harder it is to treat those risks as secondary considerations.
People-related concerns add another layer. Close to one-third of organizations, 32 percent, point to employee resistance or fear of job displacement as a challenge. Under half (41%) also say uncertainty about which AI solutions will deliver real value makes it harder to move forward in a rapidly changing landscape. As teams are asked to adapt, leaders are being inundated with options, opinions, and promises, often without clear indicators about what fits their organization. That combination can stall momentum, making it harder to build trust on either side of the equation.
AI is also beginning to change how work is learned and developed. As routine tasks become automated, early-career employees may have fewer opportunities to build experience through repetition. That shift invites leaders to think more intentionally about how judgement, context, and hands-on learning develop alongside AI.
Budget constraints also influence how organizations prioritize security, integration, and long-term architecture. But despite the people-related reservations, and skill and knowledge barriers intent has not faded. Almost three-quarters, 74 percent of respondents reporting they expect to increase their investment in AI, signaling a desire to move ahead with greater clarity and control.
It sheds light on why momentum can build quickly in some respects, yet slow in others. Many organizations have already set AI in motion. What determines how far it goes is not the technology itself, but whether teams have the skills, structure, and confidence to carry it forward.
Quick takeaways:
Most organizations say they are seeing returns from AI. More than 90 percent report having realized some level of return, and 92 percent say they can measure that ROI. But when leaders describe those returns, the focus narrows.
Close to six in 10 organizations, 59 percent, characterize their returns as modest. Only 16 percent report seeing significant gains. In practice, most value is coming from the same places, such as productivity improvements, efficiency gains, and cost reduction. These outcomes are tangible and relatively easy to recognize. They show up in faster turnaround, reduced effort, and smoother workflows.
What shows up less often is a direct connection between AI and growth, innovation, or structural change. Fewer organizations point to AI as a driver of new revenue, differentiated offerings, or fundamentally different ways of operating. That gap doesn’t suggest AI is underperforming. It suggests productivity is likely the primary driver of value right now.
With productivity gains being visible quickly, strategic impact can take longer, becoming overshadowed somewhat. It requires a clearer line of sight between AI investment and broader business goals. Without that link, returns may level off. In those cases, AI continues to function, but its contribution stays aligned to specific tasks rather than broader outcomes.
Quick takeaways:
There’s no argument that early gains have helped relieve pressure on teams that were already stretched. That part is real. What has replaced it is a new set of questions. Where does additional value actually come from? How much is enough to stay competitive? And how do you know when AI is contributing to momentum versus quietly levelling the playing field for everyone else?
Those questions are harder, and they are less clear than the productivity wins. They sit at the strategic intersection of growth, competition, and decision-making. They surface when leaders start looking past efficiency and toward differentiation, resilience, and long-term advantage.
This is where many organizations find themselves now. Not stuck, and not behind, but facing a different kind of complexity than they anticipated at the start. One where progress isn’t measured by adoption alone, but by whether AI is helping the business transform.
The next phase of adoption will come down to alignment. Taking stock of what is working, where uncertainty remains, and what the business really needs AI to support. That kind of clarity doesn’t arrive all at once. But builds gradually, through decisions that feel grounded rather than rushed.
And for organizations in the mid-market, that is a very real place to be.
Jon is a Partner with MNP’s Digital Services team. Jon leads the Client Services teams in MNP’s Digital practice, helping clients drive value and transformation from their digital investments. Collectively, the firm’s Client Services teams support all aspects of business development for MNP’s Digital practice, including marketing, sales enablement, revenue operations, client service sales, strategic partnerships and proposal management.
As Director of AI and Machine Learning at MNP Digital, Vishen focuses on leading a team that helps businesses leverage AI to unlock the power of their data. His goal is to design and deliver AI and machine learning solutions that drive innovation, automate processes, and enable smarter decision-making.
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