Generative AI has moved from discussion to action. This blog revisits the strategic lens through which AI can drive business value, especially for mid-market organizations. Updates from 2025 offer additional context, lessons, and emerging considerations for leaders navigating this evolving space.
Author’s Note – 2025 Update
Since this blog post was originally published in early 2023, the conversation around artificial intelligence (AI) has evolved rapidly. Generative AI applications have spread—integrated into tools like Microsoft Copilot, Salesforce, and everyday workplace platforms. As AI adoption accelerates, leaders are increasingly focused on strategic value, talent readiness, and responsible governance. Updated reflections are provided under each section to highlight how perspectives have shifted in just two years.
The emergence and availability of Generative AI Applications (most notably ChatGPT sparked renewed curiosity about the potential of Artificial Intelligence (AI) in business. The possibilities for automation, decision support, and customer engagement are extensive..
However, media coverage and industry buzz have often made it challenging to differentiate between hype and practical applications of the technology, sometimes obscuring limitations and likely risks. A 2023 article from Harvard Business Review, “The AI Hype Cycle Is Distracting Companies,” cautioned that overstating AI’s “intelligence,” can mislead business decision-makers and distract from meaningful use cases. For mid-sized companies, clarity is key-AI can drive value with aligned with business priorities, implemented with intention, and supported by organizational readiness.
📌[2025 Update]
AI adoption has surged in the past two years. According to KPMG Canada’s Generative AI Adoption Index (August 2024), nearly half of Canadian workers (46%) now use generative AI tools in their jobs—up from 22% the year prior. Among them, 65% use these tools daily, up from 51% in May 2023. While usage is rising quickly, many organizations are still navigating how to align AI adoption with strategic priorities. The focus for leaders is shifting from experimentation to value capture, governance, and execution discipline.
What Is “Artificial Intelligence”?
In this context, AI refers to a range of technologies, including Natural Language Processing (NPL), Machine Vision, Deep Learning and Machine Learning. It is worth noting that the International Organization for Standardization (ISO) defines an AI System as: “…engineered system that generates outputs such as content, forecasts, recommendations, or decisions for a given set of human-defined objectives“ (ISO/IEC 22989).
📌 [2025 Update]
While this definition remains accurate, the conversation has narrowed around large language models (LLMs) and generative AI. Internally, organizations are differentiating between foundational models (e.g. GPT-4) and tailored tools built on top of those models. These distinctions are now essential in determining the right AI path—whether to build, buy, or fine-tune.
Strategy and Business Applications of AI
Business applications for AI technology are vast touching nearly every functional area of an organization – everything from supply chain management, human resources, engineering, IT help desk, customer support, to quality assurance. The opportunity is expansive, but so is the risk of scattered investments.
Given the range of opportunities, directing AI investments through a business strategic lens is important. If not integrated into strategy, AI investments can become fragmented and unfocused, or organizations can fail to act. AI should be viewed as an enabler of transformation—not a side initiative or one-off pilot.
📌 [2025 Update]
AI investments are now being segmented into "embedded AI" (built into existing tools) and "transformational AI" (requiring new workflows and cross-functional collaboration). Leaders are prioritizing initiatives that deliver measurable ROI—whether through efficiency gains, revenue growth, or enhanced customer experiences.
The Current State of AI Adoption
Narrow AI solutions – tools built for a specific purpose - have been utilized by large organizations and part of our lives for many years. Examples range from Netflix’s personalized suggestions to Google Photos’ image recognition. Yet, there is still a significant gap between passive AI exposure and purposeful adoption within businesses.
In 2023, a KPMG survey noted that only 35% of Canadian organizations were actively using AI in their operations, compared with 72% in the US. Even among adopters, many companies recognized that they had only scratched the surface of the potential of these opportunities.
📌 [2025 Update]
AI adoption continues to accelerate across Canadian enterprise organizations. IBM Global AI Adoption Index, released in early 2024, 37% of large Canadian companies (over 1,000 employees) had deployed AI by November 2023—up from 34% earlier that year. While this reflects steady growth, another 48% of Canadian enterprises reported that they were still actively exploring AI, highlighting the evolving nature of adoption.
Top drivers included easier access to AI tools (46%), cost reduction and process automation (46%), and the embedding of AI into standard off-the-shelf applications (34%). However, significant barriers persist, including skills gaps (41%), data complexity (24%), and high implementation costs (24%).
This indicates a maturing phase of adoption—where accessibility is no longer the core challenge, but strategy, governance, and organizational readiness become critical success factors.
Supercharging Strategy with AI
Broadly speaking, AI opportunities fall into one of two buckets:
- One bucket is productivity solutions that help an organization perform general everyday business functions in more automated ways.
-
- Augmenting the IT help desk,
- Pre-screening candidates,
- Running QA testing,
- Automating document classification.
-
These solutions often come bundled within existing software, making them accessible with minimal change management.
- The second bucket of opportunities are the applications of AI that solve core business problems to advance key strategies for the organization.
This bucket likely contains higher return on investment opportunities where business transformation and business differentiation can supercharge strategy. These opportunities demand an understanding of “the art of the possible” in AI, coupled with visionary business strategy. There are many AI technology organizations competing to provide industry specific solutions which business leaders should seek to become familiar with.
📌 [2025 Update]
This distinction is still helpful. However, many vendors now blur the line—offering co-pilot solutions that combine automation with decision intelligence. Industry-specific tools in healthcare, finance, and manufacturing are bridging the gap between efficiency and strategic transformation.
A First-Hand Strategic Example
One organization was struggling to solve a customer retention question central to its strategy. To support this, the organization implemented an AI tool which utilized natural language processing to read hundreds of thousands of responses from customer feedback surveys and interactions. The organization needed to gain deeper insights into customer retention.
The solution - a SaaS (Software as a Service) - came with an enterprise-ready foundational model for customer engagement designed to detect negative and positive sentiment and classify feedback topically. This solution made it possible to implement and gain insights in around three months for a medium-sized non-technology organization that could not curate large data sets or build its own algorithms.
Once deployed, the model could then be tuned to the specific context as organization-specific data was fed into the model. Further to this the insights derived from this tool were integrated into the pre-existing continuous improvement mechanisms to enable thousands of employees with these insights to improve customer satisfaction.
📌 [2025 Update]
Since this example was first implemented, the AI vendor landscape has exploded—with many platforms offering pre-built NLP, sentiment, and feedback analysis features. While access has improved, selecting the right vendor and aligning tools with business context remain critical. Stratford continues to support clients in navigating AI tool selection, ensuring implementations are not only technically sound, but strategically impactful. Read more about our Vendor and Technology Solution Selection Services here.
Data, Data, Data and Readiness
Data is foundational to AI performance. Machine learning models need to be trained using quality data to produce reliable and actionable outputs. Many organizations pursuing digital and AI strategies quickly realize they need a robust data strategy to support these efforts. Many AI projects will incorporate work around data.
Beyond data, leaders will need to consider other areas of readiness to enable the successful adoption of AI technologies. Another key is talent, people, and upskilling. People who understand and utilize AI will be in a better position than those who do not, as will the organizations that employ them.
📌 [2025 Update]
Data continues to be one of the most cited barriers to successful AI adoption. While access to AI tools has improved, organizations often underestimate the complexity of preparing and managing data that AI solutions can use effectively. According to Gartner, 30% of generative AI projects will be abandoned after the proof-of-concept stage by the end of 2025, with poor data quality and lack of readiness cited as key reasons. This reinforces the need for organizations to invest in foundational data strategy—ensuring not just implementation, but long-term impact.
Risk Management and AI
Identifying the most pertinent risks of AI to an organization will allow for their mitigation through various means including education and governance. Common areas of risk to consider include false information based on imperfect data training sets, greater cybersecurity risks due to use of AI in phishing and hacking, and data ownership and intellectual property considerations. It is also important to consider that the risk of inaction while these technologies accelerate can leave organizations behind.
📌 [2025 Update]
The governance conversation has matured considerably. New legislation—such as the EU AI Act and Canada’s proposed AIDA framework—has made AI governance a board-level topic. Risk now includes explainability, auditability, and regulatory compliance. Organizations are responding by building AI risk frameworks akin to traditional risk and compliance structures.
Why AI?
Questions to ask include:
- Where are the current friction points in operations?
- What external pressures are shifting customer expectations?
- Can AI offer a competitive edge in this space?
Clarity on these questions allows for intentional investment—and meaningful impact.
📌 [2025 Update]
As AI becomes embedded in the digital workplace, differentiation will come from strategic application. The question is no longer "should we explore AI?" but rather "how will AI advance our purpose, scale our capabilities, and strengthen our market position?"
Strategic Integration of AI: A 2025 Lens
Reflecting on two years of accelerated adoption, the core insight remains: AI is not a standalone strategy. It is an enabler that must be embedded into strategic planning, operational processes, and leadership development.
The most successful organizations are:
- Aligning AI investments to measurable outcomes
- Building talent capabilities to manage and scale AI use
- Establishing governance models to mitigate risk
Progressive leaders are not just using AI tools—they are evolving their organizations to thrive alongside them.
If you are interested in further exploring the impact and potential of AI as it relates to business operations and strategy, download our white paper “Looking Behind the Curtain: Understanding AI’s Potential and Impact”. Or contact us to explore how Stratford supports AI readiness, strategy, and implementation.
About the Author:
