With AI becoming a more critical component of work, many enterprise leaders are now grappling with how to deploy it, encourage adoption, and understand how to use it to drive meaningful outcomes. In fact, data from Asana’s Work Innovation Lab reveals that almost half of knowledge workers in the UK use generative AI weekly—a significant increase from just 29% nine months prior. This rapid uptick in AI adoption demonstrates how optimistic workers are that AI can help drive efficiencies.
But that also means more pressure on leaders to implement AI solutions that help meet company objectives, increase revenue, and fuel productivity – 90% of IT leaders even say that investing in AI technology will be critical to navigating future business challenges.
Harnessing AI’s potential is more than a technological challenge. It’s a critical strategic priority that businesses have to get right in order to compete in today’s market. With this in mind, I’m sharing my tips as an AI product leader to help executives build a foundation for AI now, guide and inspire teams along the way, and start driving results for the future.
Head of Product at Asana.
1. Develop AI principles that inform everything you do
AI’s potential is profound, but organizations that don’t have a strategic approach risk just producing more content, more tasks, and more work as siloed teams onboard disparate AI tools. To combat this disconnect, it’s important for leaders to design a strategic approach across the organization, outlining how AI can be used, how it should be used, and ensuring that teams are informed. When you have a clear set of AI principles, it’s easier to determine how and what to move forward.
There are a few important things to consider when designing AI principles. First, incorporate people into everything you do – we call it the human-centered approach to AI. AI should enhance people’s natural capabilities and help them achieve their goals – not replace them. When mundane work like status updates and research are automated by AI, people have the capacity to do higher impact work that moves the needle. Second, with any AI your organization uses, ensure that people are the ultimate decision makers, with an understanding of how AI informs any recommendations, content, and strategy. Next, prioritize transparency. Whether it’s your teams or your customers, anyone should be able to easily understand how AI is used in your organization. And finally, take AI safety and security seriously, and make sure to balance speed of AI deployment with responsible usage.
AI has the power to make work better, more enjoyable, and more productive, so that people can focus on what they do best – creativity, innovation, and strategic thinking. As many as two-thirds of workers who use AI report that they’re more productive, which demonstrates how much more teams can achieve when AI is deployed effectively.
2. Provide AI training and guidance for teams
Many knowledge workers are uncertain and anxious about the role AI will play at work, and how it will impact their jobs – one in three employees worry that AI is going to replace human workers. It’s important for leaders to make teams comfortable using AI so that they can focus on the high impact work that drives results, not on busywork that can easily be automated and streamlined by AI.
Right now, AI training is one of the most important career opportunities that leaders can offer their teams, and yet less than one in five UK employees (17%) say their organisations have provided this. Leaders should work to create a safe environment built upon formal training where employees can test and experiment with AI – because that’s the best way to learn.
The most important thing that employers can do to help their teams learn about AI is to let them experiment and tinker. You can’t teach someone the art of what’s possible – they have to have that ah-ha moment to fully grasp AI’s power, as well as its limitations. This kind of exploration also helps people learn how to prompt, re-prompt, and iterate with AI in order to get the best possible outcomes over time.
3. Prioritize data accuracy and structure
Data will unlock AI’s potential to move work forward, but our research shows that accuracy of AI results is the biggest concern that knowledge workers have, with only 39% of IT leaders expressing a high degree of confidence that their organization’s internal data is AI-ready. Without accurate, reliable data, organizations won’t be able to use AI to deliver critical results.
Leaders must prioritize improving data quality in order to pilot and test how AI can best help them drive outcomes. That means that product leaders, data scientists, and chief architects need to work closely together to ensure the data informing any AI platforms they use is structured, reliable, and accurate – not the piles of work-in-progress documents, emails with contradictory updates, and siloed pings in messaging apps that so many teams operate with.
And while data about the work within your organization should be accurate, it’s equally important that it’s clearly connected to the people doing the work, cross-functional teams, and your organization’s higher-level goals.
Set your organization up for the future with AI
It’s easy to get intimidated by AI, so I always encourage other leaders to just get started. It can be as simple as testing new AI platforms in the flow of your work, while working your way up to building clear, transparent AI principles for your entire organization. But most importantly, find a way to get started now, experiment with new use cases, create champions across your organization, and keep building from there.
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