Asana launches prediction-based AI tools to improve decision making

Work management platform Asana has announced three new features designed to provide greater cross-organizational transparency and improved insights into how employees are collaborating.

The new features have been categorized by Asana as decision intelligence, resource intelligence, and execution intelligence and include a new dashboard that provides greater insights into key metrics, a universal workload tool that uses predictive AI to help team leaders make better resourcing decisions, and new workflow bundles that scale workflow updates across entire organizations.

Fourty-nine percent of employees are experiencing priority overload, while misaligned goals and SaaS sprawl are causing employees to become distracted from the work they should be focused on, according to research undertaken by Asana’s Work Innovation Lab.

As a result, business leaders are now under pressure to deliver improved results while juggling budget and resource constraints, said Alex Hood, chief product officer at Asana.

To address this, Asana has added new executive reporting with portfolio dashboards and roll-ups for key metrics like budget and time will be available to enterprise and business customers, providing leaders with a view of the ROI of their investments, as well as the health and status of strategic initiatives, capacity, and budgets.

The dashboard will help sharpen and speed up decision-making by giving leaders the ability to see how teams are actually working together, highlight strong individuals, and see which teams are working with more velocity than others, Hood said.

Asana is also launching a universal workload tool, in order to help organizations make better resourcing decisions. It provides leaders with a holistic view of team capacity across all projects and programs throughout their entire organization.

In some organizations, certain teams might be decentralized, which makes it difficult to track who is doing what or how quickly a task can be completed, Hood explained. By having the ability to pull workload and resource information across the entire organization, the universal workload tool can make inferences and provide insights and predictions about who will have the ability to work on a new project.

Universal workload also has a calendar integration so team leaders can see if a certain employee has any upcoming vacation and factor that into any recourse decision-making.

The final announcement focuses on execution intelligence and is designed to drive standardization and automation across teams and departments.

Knowledge workers said they could save 4.9 hours per week if their company had improved processes, such as streamlined communications, according to Asana’s latest Anatomy of Work report.

To combat this issue, Asana’s new workflow bundles will allow enterprise customers to scale workflow updates across the entire organization, meaning employees can instantly see and adapt to any changes in company-wide processes or market conditions.

“If you use this bundle, deploy it across 50 of your teams, those bundles will be linked so you can make a change in one workflow and it flows through to everywhere that workflow is being used,” Hood said. “This creates a continuous improvement loop, allowing you to see what’s working, or what’s not, so you can then make tweaks across the whole organization all at once.”

Executive reporting for portfolios is available to customers from today, while workflow bundles and universal workload will be “coming soon.”

Artificial intelligence should help, not hinder, workers

Where other collaboration and work management platforms have been adding generative AI capabilities to their offerings in recent months, the new features unveiled by Asana today represent “pre-AI” era, Hood said, while trying to give a sense of the company’s roadmap.

Asana has chosen to focus more on the prediction aspect of AI rather than its content-generating capabilities because as a company, it wants to leverage the technology where it makes the most sense for its customers, Hood said.

With employees already suffering from priority overload and wasted hours due to poor collaboration, Hood said he was also conscious of generative AI’s capacity to slow down execution and decision-making.

“The danger of the current set of AI tools out there is all this generative content might mean that your plan of record might actually not be right,” he said, noting that the large language models most generative AI tools are trained on sometimes proclaim things as fact when they aren’t actually true.

“When you’ve got 16 applications that are all making it very easy to spam the rest of your team with stuff that might only be like 80% correct, that’s a new challenge,” Hood said.

Focusing on building out AI capabilities is a positive move, said Margo Visitacion, principal analyst at Gartner, noting that in some cases these tools have enabled companies to create a significant amount of data about how they generate value and best leverage AI.

While collaborative Work Management tools in general are getting demand for more sophisticated use cases, Visitacion warned organizations must balance the ease of use with increasingly complex capabilities.

“It will be a challenge to keep the tools very usable while managing more data to make increasingly complex decisions, and thinking differently about work – connecting cross functional teams to provide greater visibility,” Visitacion said. “That will require governance that is more team oriented to help them work together more effectively – that’s the desired outcome – effective action.”