For many of us, the way we work is broken. But the combined power of humans and AI offers a potential solution. At The Work Innovation Summit, we explored the potential for AI to transform the way we work. The event highlighted the magic that unfolds when AI—rather than adding to the work pile—alleviates repetitive tasks and allows humans to channel their ingenuity, empathy, and passion into purposeful, meaningful work.
Read on to discover the top takeaways from the event, designed to help you harness the power of humans and AI at work. And don’t miss your chance to tune into the keynote replay at our virtual watch party on 10/26.
AI isn’t a replacement for human abilities—it’s an enhancer.
In his keynote address, Dustin Moskovitz echoed this sentiment, which is also highlighted in Asana’s AI principles. “When all the drudgery is handled by AI, people can focus on using their empathy, their passion, and their ingenuity,” he said. “Rather than replace us, AI can amplify what makes us most human.”
For organizations, that means using AI to automate repetitive, low-value work so teams can focus on complex, creative tasks—the work that’s uniquely human. As Deeksha Hebbar, COO of Sonder, put it during the operations-focused breakout session, AI is all about “removing joyless work.”
IT panelist Cal Henderson, Co-Founder and CTO of Slack, agreed: “AI is pushing the boundaries of what we’re able to automate toward the most complex work. And that leaves humans time to do the most human kind of work.”
The rapid pace of AI advancements has given rise to workforce concerns about security, ethics, and job safety. You can help mitigate these concerns—which likely stem from the fear of the unknown—by addressing doubts head-on. Be upfront and candid with your team about your organization’s plans for AI adoption and utilization to alleviate their uncertainties.
When integrating AI into your tech stack and workflows, it’s crucial to be transparent about tool utilization and decision-making. Sarah Franklin, President and Chairwoman at Salesforce, underscored the importance of centering trust when approaching AI during the marketing panel: “AI first and foremost needs to be approached with trust at the center.”
Build this trust by involving your team in the AI adoption journey. By keeping them in the loop with regular updates and open lines of communication, you’ll foster a culture where they feel safe to embrace the technology, experiment, and learn.
Another important factor to building trust? The AI tools you choose. Choosing an AI tool that’s transparent about the data it pulls from and its decision-making process—a tool that “unpacks its thinking,” as Alex Hood, Asana’s Chief Product Officer, put it—ensures you can trust the data and insights provided.
Another way to encourage AI adoption—and help employees feel more comfortable about your approach—is to develop a safety guidelines framework to ensure responsible use. These guardrails should enable safe AI use, encouraging experimentation while ensuring data protection and compliance.
“You have to thread the needle, because if you block everything, people will find a way,” said Henderson. “So what can you provide? What guidelines can you provide that will allow your people to use the technology in a way that’s as safe as possible while we drive toward more enterprise-ready versions of the technology?”
Ultimately, setting up your “responsible AI framework,” as marketing panelist Catherine LaCour, Chief Marketing Officer at Blackbaud called it, will allow you the freedom to experiment—and to fail—safely. This approach can help you continue to foster a culture of innovation while also mitigating the inherent risks of AI.
With all of the excitement in today’s business landscape around AI, it’s tempting to quickly deploy new technologies. But, according to Henderson, following this rapid pace of adoption could ultimately hurt—not help—your organization.
“There’s so much excitement right now about AI in the C-suite,” he said. “Every customer is breathlessly talking about AI and boards are pushing companies to [invest in AI]. I think there are going to be a lot of organizations that [...] overinvest in things that turn out to not be valuable, or not be valuable to the extent that people think they will be.”
Instead of jumping into AI integration feet first, adopt a phased approach. Test the waters with specific AI applications and use cases designed to drive long-term value. IT panelist Andrew Sopko, former Head of Corporate Technology at Stripe, recommends a product-centric approach to evaluate the benefits and the beneficiaries of the tool, ensuring a meaningful, sustainable impact from your AI investments.
“There’s going to be a lot of false promises out there,” said Sopko. “Taking a slow and cautious approach is going to be valuable.”
There’s no doubt about it—AI offers the ability to automate repetitive processes and reduce tedious work. But the technology’s real value lies in its ability to catalyze transformative change, driving long-term results across your organization.
To harness this, look for use cases that can drive sweeping change and scalable impact, rather than focusing on those that drive incremental benefits—for example, using AI to automate time-consuming, organization-wide data entry tasks across an organization as opposed to using it to sort emails in an individual's inbox. Hebbar refers to these as “efficiency multipliers”—areas where AI can be applied to “improve the efficiency and productivity of our teams and systems more broadly.”
As Zscaler CIO Praniti Lakhwara put it during the IT breakout session, using AI as an efficiency multiplier within your own organization means doing more than just tackling the most “loud and visible problem.”
“Sometimes, the best ROI comes from a medium-value use case that’s repeatable and deployable at scale across the enterprise [versus] a very high-value use case that’s one and done,” she said.
The influx of exciting AI applications, features, and capabilities means that the technology—and all its possibilities—can easily become a distraction, veering teams off the strategic path. Michael Whitaker, Senior Vice President of Strategy Enablement and Innovation Services at ICF, referred to these as “shiny objects”: enticing, but potentially non-impactful aspects of AI.
“You have to fight distraction and stay on top of real, tangible outcomes,” Whitaker said during the operations breakout session.
According to Franklin, the key is aligning your AI efforts with organizational goals. For example, anchor your AI experiments to measurable outcomes and validate their impact through A/B testing. This approach ensures your efforts are based on informed decisions and channeled toward achieving genuine efficiency gains before making large-scale investments or roll-outs.
“Staying focused on those goals and not getting distracted by noise is really important,” she said.
Impactful AI adoption can’t be achieved in a silo. To be successful, it’s crucial that you partner with cross-functional teams to leverage different skill sets and perspectives.
Whitaker advised the creation of “fusion teams”—cross-functional units focused on specific use cases, like exploring generative AI, to drive AI success.
At ICF, these units pooled talent from departments like innovation services, corporate IT, and legal. This interdisciplinary approach enabled alignment on decision-making and governance, while establishing a collaborative rhythm. What’s more, having a specialized team for each use case allowed for quicker movement and eliminated the need for new teams with each new request.
“If you feel like you have enough work that’s going to come in a particular area over time, set up a standing team and bring work to that team,” said Whitaker.
When it comes to AI, experimentation plays a dual role in successful integration and adoption. Firstly, small, controlled AI experiments can help you gauge impact, understand risks, and lay the groundwork for broader adoption. This measured approach was emphasized by Hebbar, who stressed the importance of being thoughtful when "[moving fast] in the context of AI.”
“It's important to be cognizant of risks and start with experimentation in a smaller format where the risks are inherently less—and then scale over time,” she said.
The other benefit of AI experimentation is its ability to change organizational viewpoints of AI. According to LaCour, Blackbaud has been “intentional about bringing the entire company along with us on the [AI experimentation] journey” to mitigate fear.
“Over-communicating and bringing employees along [...] and making them part of experiments has helped reduce fear,” she said.
Integrating AI into your tech stack isn’t just about adopting the latest tools; it’s about enhancing the systems you already have. To maximize value, prioritize strong vendor relationships, which will help you understand how AI can enhance your existing systems and drive innovation.
LaCour echoed this sentiment: “We really put a priority on strong vendor relationships. So we are partnering with our vendors—like Asana—to ask 'what are you doing with AI,' 'how are you innovating,' 'when is the right time for us to adopt it,' and 'what does AI mean for our tech stack, and our innovation, and our processes going forward?’”
“Stick with your stack,” she recommended. “The more you can invest in the stack you have versus just onboarding new tools for the sake of onboarding new tools, the better."
Then, when integrating AI into your workflows, ensure the technology becomes a natural extension of existing processes, rather than an additional step that requires employees to deviate from their normal workflow. Whitaker called out the importance of building AI into workflows in a way that people can’t avoid—literally making AI “part of their team”—to drive adoption on a broader level.
Not all AI is created equal. The key for organizations is to choose AI vendors and features that facilitate smarter, more efficient work.
“Humans figure out what needs to get done, and then teams, individuals, and executives get AI to help them do that better, faster, and without risk,” said Hood. “[AI] is an enabler, but it is not telling us what we need to do. It makes us better at doing the things we know we need to do.”
Asana Intelligence is one such example, offering an array of capabilities to drive clarity, maximize impact, and increase scalability. Features like AI-drafted smart goals and status updates, actionable recommendations, automated workflows, and insightful data analysis help organizations work smarter and hit objectives. By leveraging these capabilities, organizations can harness a more streamlined and intelligent way of managing work—and hit business-critical goals.
Organizations should navigate the journey to broad AI adoption with an eye toward achieving smarter work—which means using AI as a tool to refine processes and inform decisions. “Asana’s AI is designed for one thing—and that is to help humans achieve their goals,” said Hood.
AI is here to stay. It’s not whether you use the technology that will ultimately set your organization apart—it’s how you use it. By thoughtfully selecting AI applications and vendors, focusing on use cases that augment human work, and being deliberate about experimentation and adoption, organizations are poised to unlock greater clarity and drive toward more impactful work—with humans and AI at the center.
Want more takeaways from The Work Innovation Summit? Tune into our virtual watch party on October 26th for a replay of our keynote featuring Asana’s Co-Founder and CEO, Dustin Moskovitz, Asana’s Chief Product Officer, Alex Hood, Mediabrands Global CEO, Jarrod Martin, and more.