Artificial intelligence is reshaping how organizations operate, and nowhere is this transformation more intense than across the Americas. To help clients navigate this shift, Avanade—the leading Microsoft solutions provider—appointed Gord Mawhinney as President of the Americas, overseeing the company’s largest and most dynamic regional business unit.
A seasoned technology leader with over eight years at Avanade and a track record that includes CEO of Long View Systems and senior roles at IBM, Mawhinney now leads strategy, sales, delivery, and operations across the U.S., Canada, and Brazil. His mandate: accelerate digital modernization and AI adoption for thousands of clients using Microsoft-powered technologies.
Techcouver sat down with Mawhinney to explore how mid-market companies are approaching generative AI, the critical foundations for scalable innovation, and the leadership mindset required to align people, data, and strategy in an AI-first world.
Among mid-market organizations, how is AI adoption evolving in terms of investment priorities?
GM: According to our research (Avanade Trendlines: AI Value Report 2025), over the next 12 months, 60% of organizations will make AI a top IT priority, and 53% expect to increase budgets for generative AI by up to 25%. In fact, over 90% have already accelerated IT modernization and cloud adoption. But I’d caution that while technology considerations are an obvious place to start, fundamentally, people and data principles can’t be ignored.
Getting data fit for purpose can be a major hurdle for mid-market organizations that want to put AI into action, facing concerns such as overcoming the complexity of integrating AI with existing systems and safeguarding data security and privacy.
How can organizations ensure AI adoption is scalable and sustainable, rather than limited to isolated functions?
GM: This is such an important question. Our research shows that when it comes to how AI is being implemented, 81% of respondents are using it in an isolated way and it’s not surprising. It’s easy to find a scenario where AI works and just forget ahead. However, this will lead to a potentially haphazard implementation that scatters AI in various areas with no formal vision or strategy. I can’t stress enough the need for a comprehensive AI approach that aligns with the overall organizational strategy.
This will be the ‘north star’ for leaders to decide where to adopt technology today and how to reinvent for the future. Instead of putting ‘a little bit of AI’ into various areas—which could dilute its impact, overwhelm teams or lead to rework further down the road—it helps to create a cohesive roadmap that delivers economies of scale across functions.
For leaders who are eager to move fast with AI but haven’t yet set a clear strategy, how can they begin laying the groundwork? Which elements are critical for long-term success?
GM: There are many critical elements, but I’ll focus on my top three:
- Understand your objectives. What business challenge(s) are you looking to solve? This will help you identify the most impactful use cases.
- Look for iterative transformation opportunities. AI isn’t a cure for every issue, so don’t overestimate what it can deliver. Otherwise, you’ll be on a path to frustration and potentially project failure. Focus on small, manageable projects that allow for quick wins and gradual scaling.
- Beware of poor data management. Data is the foundation of any AI system. Organizations must build, modernize and maintain robust data pipelines to support generative AI initiatives. Start with a comprehensive data assessment, identifying gaps and ensuring that your data is cleansed, accurate and well-organized.
As companies rethink workforce development, how should they approach AI training to truly prepare employees for this next phase of work?
GM: There’s no doubt that AI will increasingly become a part of everyday work, which will shift workforce dynamics. I alluded earlier to getting people factors right, so let me expand on that.
Understanding the new relationship between workers and AI, including the social and emotional impacts, will be key to building trust. I’ll give you an example from our own experience with Microsoft Copilot. We found that trust in AI increases with training and experience. Employees can be suspicious of leaders’ motivations as they experiment with AI if they don’t feel involved in the process. Empowering all employees to use AI improves adoption and advocacy—both for the individual and the organization.
That’s why we invested in programs such as Disrupt Avanade (for which I’m proud to say we were recognized by the CIO 100 Awards) and our School of AI, to equip all Avanade employees—not just technologists—with valuable skills to navigate generative AI, responsible AI and prompt engineering.
We recommend leaders build a sandbox environment where users can experiment with AI on non-critical tasks to safely explore AI capabilities and collaboration. We also recommend setting up an employee feedback system to foster a culture of open communication and continuous learning. This builds trust among teams, shortens the time from innovation to competitive advantage, and most importantly elevates AI from a passive tool that needs “to be used”, to something that the team wants to use proactively.
The good news is that 79% of organizations in our study plan to grow investment in AI training and fluency, recognizing that people need the knowledge and tools to work alongside AI. Moreover, 77% are focusing on change management to ensure AI supports both new and existing ways of working.
What bigger-picture questions should leadership be addressing to avoid short-term thinking as they increase their use of AI?
GM: Reflecting on our experience with Microsoft 365 Copilot for Sales, AI did indeed reduce workload. But the real benefits were less about time saved and more about freeing up our sellers to do what they love and do best: delighting clients by finding new ways to add value. We believe AI’s real promise lies in this reinvention—not just from shaving time off existing tasks but redefining the productivity equation itself. Again, AI as a coworker—not a tool.
When humans and AI come together in multiplayer teams, a more expansive outlook will open the door to entirely new ways of working. Investing in the employee experience will drive engagement and innovation—which in turn leads to a better customer experience and business outcomes.
Here are some questions to consider in your AI task force discussions:
- How might your workforce embrace AI and confidently increase value for stakeholders?
- What modernizations are needed to ensure your tech systems are flexible and secure enough to make the most of AI advancements?
- How might you deploy AI to align with both human and organizational values?
- How are you addressing the social and emotional impacts of AI?
- Where have you set clear AI risk thresholds and governance structures for each business function?
When building an AI governance framework, how can companies establish safeguards that promote responsible use while keeping innovation moving forward?
GM: The tension is real. There’s a growing concern around balancing an innovative spirit and the ethical use of AI, and yet according to our research just 39% of mid-market organizations have a complete set of guidelines in place for Responsible AI (RAI). This could be due to the pace at which regulations are being established and the ongoing need to reevaluate, update or embed RAI guidelines.
For rapid-yet-safe and measured AI progress, I recommend that leaders assess their risk thresholds: specifically, what is an acceptable risk level? This will differ across business functions, industries and geographies in terms of evolving regulatory requirements and market implications. Assessing your risk threshold will help to set realistic AI boundaries and expectations in alignment with organizational values. Finally, these principles must be built into your existing governance structure – executive committee meetings, the IT governance board agenda, IT policies, performance objectives and more.
With so much riding on AI’s ability to deliver value, which KPIs are proving most effective in capturing its real impact on productivity?
GM: I’d caution leaders not to rely solely on productivity as the ‘hero KPI.’ The reality is that the very definition of productivity needs to adapt. We will face major shifts in how work is done, and even our baselines for what we believe adds value will shift. New metrics will likely replace the more traditional ways we measure productivity. Therefore, take a more holistic approach when evaluating the anticipated value of AI.
Consider these factors: freeing up mental bandwidth allows us to be more imaginative, innovative and curious; a sharper mind promotes sharper decision making; tools that enhance our focus and depth of work ensure that we produce not just more, but better; and tools that amplify team collaboration foster better communication and synergistic results.
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