State of AI at Work: A Conversation with Rebecca Hinds at Asana Work Innovation Lab

By: Rebecca Hinds, Allie Blaising, Ted Liu 

Artificial intelligence (AI), especially generative AI (gen AI), is rapidly changing how we work. From automating repetitive tasks to providing insights into big data, gen AI is being widely adopted by organizations to make work more efficient and productive. However, many questions remain about how gen AI is being integrated into workflows, the workforce's readiness to adopt AI effectively, and whether such tools truly improve collaboration and productivity. 

Upwork’s User Research team and the Upwork Research Institute recently spoke with Dr. Rebecca Hinds and her colleagues, Dr. Mark Hoffman and Anna James, at Asana’s Work Innovation Lab as part of Upwork’s Reimagining Work — a lecture series designed to provide a forum for expert practitioners and academics to foster the exchange of views on the present and future of work. The Work Innovation Lab is Asana’s internal think tank that conducts cutting-edge, actionable research to help executives navigate the growing changes and challenges of work. In this conversation, we discuss the state of AI at Work and the aforementioned key questions about effectively integrating AI into organizations. 

Ted Liu, Economist at the Upwork Research Institute:

In The Work Innovation Lab’s State of AI at Work report, you describe that while organizations you surveyed have started to adopt AI, only 36% of knowledge workers are using AI weekly and individual contributors are much less likely than executives to have adopted AI. Upwork’s own research has echoed this disconnect between the C-suite and leadership team. Can you provide examples from your study and explain why we see this disconnect?

Rebecca:

Absolutely. In our research, we’ve discovered that the disconnect between AI adoption by organizations and its integration into daily workflows can be attributed to specific gaps: 

The first gap we see is what we call a transparency gap. While 44% of executives believe they have been transparent about their organization's AI plans, only 25% of individual contributors perceive the same level of transparency. This gap suggests a communication breakdown between leadership and employees, where intentions and strategies around AI implementation are not clearly or effectively conveyed to those at the operational level. Transparency breeds trust and, conversely, a lack of transparency breeds a lack of trust, which manifests in lower levels of adoption. 

The second gap we see is what we call a resource gap: There is a notable discrepancy in perceptions of AI training availability. While 25% of executives report that their companies provide AI training for day-to-day work, only 11% of individual contributors agree. Training and learning and development fuels confidence in using the technology and without these resources, employees won’t have confidence in their ability to harness the technology and integrate it into workflows. 

The final gap is what we call an optimism gap. 61% of executives say that AI will help their companies achieve their objectives, while less than half (46%) of individual contributors say the same. A key way that companies can incite greater optimism around the technology is by ensuring that the strategy, purpose, and benefits of AI are communicated clearly and consistently throughout their organization, as well as establishing clear, human-first principles around the use of AI. 

Allie Blaising, Senior User Researcher:

To help organizations adopt AI more effectively, you and your colleagues argue that “moving more slowly” is more optimal than instantaneous adoption. At least in the tech industry, we tend to move fast to adopt new tools. What are the benefits of moving more slowly?

Rebecca:

Moving slowly allows for a more thoughtful and strategic approach to AI adoption. It provides time to assess the organization's specific needs, identify the most suitable AI solutions, and develop a clear roadmap for implementation. This strategic planning ensures that AI is not just adopted for its own sake but is aligned with the organization's broader goals and objectives.

A slower pace enables the organization to establish a set of guiding principles for AI use. We embraced this approach at Asana where we spent time developing a key set of principles around AI before we started to roll out our AI solutions to customers. 

Additionally, by adopting AI more gradually, organizations can invest in thorough training and skill development for employees. This ensures that the workforce is not only technically proficient in using AI tools but also understands the broader implications and applications of AI in their work. We see in our research that effective training reduces resistance and increases user adoption and proficiency.

Finally, gradual adoption allows for an iterative approach where AI systems can be tested, feedback can be gathered, and improvements can be made in real-time. This process ensures that the systems are fine-tuned to meet the specific needs of the organization and its employees.

Allie:

To operationalize this more calibrated approach of adoption, is there a framework you recommend for assessing AI products and scoping before starting execution?

Rebecca:

You should begin by identifying the specific challenges your organization faces that AI could solve, and set clear, measurable objectives for the AI implementation. Next, research the market to find AI solutions that fit your needs, evaluating them based on features, scalability, and compatibility with existing systems. You should critically evaluate the data models of potential AI tools. Your AI is only as powerful as the data underlying it and if tools can only harness a subset of your organization’s data, they’ll have limited effectiveness.

Once you’ve identified one or a short list of AI solutions, conduct a cost-benefit analysis to evaluate the potential return on investment, taking into account both direct and indirect costs and benefits. Run pilot tests or develop a proof of concept to validate your chosen AI solution. You should engage with key stakeholders for their input and feedback along the way, and develop a change management strategy to facilitate a smooth transition and mitigate resistance. The most effective and impactful AI solutions will have broad applications across multiple different functional groups within your organization. Once implemented, continuously monitor the AI solution’s performance against predefined metrics and remain open to feedback for ongoing improvements.

Ted:

Diving a bit deeper into the issue of collaboration, if an organization can effectively adopt AI as you proposed, how will the teams benefit in terms of collaboration (between people) and work innovation? Will the collaboration between people and AI tools resemble a coworker relationship? 

Rebecca:

There are several ways in which teams can benefit in terms of collaboration and work innovation. AI tools can automate routine and time-consuming tasks, freeing up team members to focus on more complex and creative work. This efficiency boost can lead to more productive collaboration, as team members can devote more time to strategic thinking and problem-solving. The ideal collaboration between people and AI will resemble a coworker relationship but one in which the division of labor is driven by humans and with AI working in service of humans, especially to automate tasks and enable them to focus time and effort on more strategic, more creative, and more human skills and activities like motivating their workforce, expressing empathy, and developing strategic plans.  

AI's ability to process and analyze large volumes of data can provide teams with valuable insights, aiding in more informed decision-making. When team members have access to accurate, up-to-date information, they can collaborate more effectively, making decisions that are backed by solid data. At The Work Innovation Lab, we recently (led by Dr. Mark Hoffman) launched our Work Innovation Score, an AI-powered assessment that evaluates a company’s potential for innovation based on how they are collaborating using Asana. Using AI can take the guesswork out of effective collaboration and identify the behaviors driving effective versus ineffective collaboration. For example, one of the consistent findings across several of our research studies is that more collaboration isn’t better – it’s about finding that sweet spot between too little collaboration and too much, ensuring enough of the cross-functional teamwork that’s essential for driving business outcomes but not too much so as to drive collaboration overload.  

Ted:

‍We have been discussing AI as tools to be adopted by organizations. When and how do they become part of workers’ portfolio of skills? Will this skill acquisition benefit high or low-skill workers more?

Rebecca:

In order for AI to become part of workers’ portfolio of skills, training is essential. This can range from training on how to interact with AI systems to more advanced training for developing or managing these systems. This stage is crucial for skill development, as workers learn to use AI as a tool to enhance their work. As workers start to use AI in their daily tasks, they gradually develop the skills to work effectively with these tools. This practical application is a form of on-the-job training, where workers learn by doing.

So far there’s emerging research showing that AI will benefit lower-skilled workers more, at least in the short term. For example, one study found that when customer support agents at a software company were provided with a generative AI-based tool to assist in issue resolution, the most significant productivity gains were observed among less skilled agents and those new to the company. However, it’s difficult and sometimes harmful to draw broad brush conclusions and whether skill acquisition benefits high- or low-skill workers depends on several factors, including training and development, the organizational context, and the specific use case of AI. For higher-skilled workers, for example, AI skills can lead to more advanced career opportunities and greater efficiency in their current roles. It can enhance their existing skills, allowing them to perform more complex analyses or to automate routine aspects of their jobs.

Ted:

‍What is your overall sentiment towards AI adoption and its effect on the future of work?

Rebecca:

At the Work Innovation Lab and Asana, our overall sentiment towards AI adoption and its impact on the future of work is predominantly one of excitement. AI presents numerous opportunities to redefine and enhance how we work, promising to bring about transformative changes across various sectors. In particular, AI opens doors to new levels of innovation. By handling routine tasks, it allows human workers to focus more on creative and strategic aspects of their jobs, fostering an environment where innovative ideas can flourish.

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About the Upwork Research Institute 

The Upwork Research Institute is committed to studying the fundamental shifts in the workforce and providing business leaders with the tools and insights they need to navigate the here and now while preparing their organization for the future. Using our proprietary platform data, global survey research, partnerships, and academic collaborations, we produce evidence-based insights to create the blueprint for the new way of work.

About Reimagining Work 

Reimagining Work is a lecture series designed to provide a forum for expert practitioners and academics to foster the exchange of views on the present and future of work. This forum aims to establish stronger partnerships and knowledge-sharing to address the pressing questions and challenges we face in ushering in a new era of work. Conceived by Allie Blaising and Dido Tsigaridi, with the guidance of VP of UX, Marcelo Marer, this forum launched by Upwork’s User Research team in 2022, and it has already sparked significant partnerships and impact across Upwork teams and initiatives. Looking ahead, the User Research team is excited to partner with the Research Institute in 2024 to expand the series’ external visibility and impact. Collaborators: Christine Costello, Ted Liu. Support: Becca Carne, Kurt Yalcin, Sallie Wormer.

About Dr. Rebecca Hinds

Rebecca Hinds is the Head of The Work Innovation Lab by Asana. She earned her Ph.D. at Stanford University, focusing on the transformation of organizations through emergent technologies and non-traditional work forms. Her research has appeared in publications including Harvard Business Review, New York Times, and the Wall Street Journal. She advises companies on developing remote work, hybrid work, and technology strategies. She is a multi-time founder, creating award-winning businesses that have raised millions in funding.

About Dr. Mark Hoffman 

Mark Hoffman leads collaborative intelligence at the Work Innovation Lab at Asana, where he heads the development of novel AI and VR-based technologies to help Asana’s customers understand the state of collaboration in their organizations and to chart their pathways to innovation.  Before joining Asana, he was a professor of sociology and business at Stanford University where he specialized in network analysis and computational social science. His work has appeared in the American Journal of Sociology and the Proceedings of the National Academy of Science, among other venues.

About Anna James 

Anna James is the Quantitative Research Lead at the Work Innovation Lab at Asana, with a rich career spanning nearly a decade in market research and management consulting. In her current role, she drives quantitative research initiatives that foster innovation and strategic thinking, leveraging her expertise in Behavioral Economics and Marketing Research. Anna's work at Asana's Work Innovation Lab is instrumental in shaping the future of work, addressing organizational challenges, and adapting to the evolving work landscape.

About Asana’s Work Innovation Lab

The Work Innovation Lab is Asana’s internal think tank that conducts actionable research to help executives navigate the evolving challenges of today's work environment. The Work Innovation Lab collaborates with companies all over the world to forge more productive and empowering workplaces. Leveraging its deep relationships with dozens of professors and leading work science experts, the Lab conducts in-depth research using multiple different research methods. Its findings are often featured in top publications, which have included Wall Street Journal, Forbes, Inc., and Harvard Business Review.

About Allie Blaising 

Allie Blaising is a Senior User Experience Researcher at Upwork, where she leads customer research that shapes design and business decisions across multiple verticals, with a recent focus on Generative AI product initiatives. 

About Dr. Ted Liu

Ted Liu is Research Manager at Upwork, where he focuses on how work and skills evolve in relation to technological progress such as artificial intelligence. He received his PhD in economics from the University of California, Santa Cruz.