Despite dramatic improvements in technology, tools, and processes, many organizations continue to struggle to improve workforce productivity. Gains are hard to come by, in large part, because of the disjointed nature of applications and data that make it hard to connect, streamline, and automate workflows. Generative AI offers tremendous potential to address the gap, assuming that organizations can enable AI agents and applications to securely and efficiently access the right enterprise data.
This episode of the AWS for Software Companies Podcast looks at what it takes to enable seamless cross-application automation and uncover the insights needed to drive productivity. Oliver Myers, worldwide head of business development at Amazon Web Services (AWS), and Spencer Herrick, principal AI product manager at Asana, share interesting takes on addressing end-user productivity challenges with generative AI technology implementations. Their takeaway: In the end, it all comes down to data.
The Problem
Given all the hype around AI, it’s easy to lose track of practical applications that can make a real-world difference. Generative AI and agentic AI hold the promise of helping end users do their jobs faster, better, and more efficiently. Yet many obstacles still exist, despite all the talk about how these technologies can help organizations and employees work smarter, not harder.
Most of the barriers involve the reality of how employees work. Myers points out that end users still face substantial technology overload. The average employee is now using nearly twice as many workplace applications as they did just five years ago—a trend that undoubtedly will continue. These applications can create data silos, making it harder for employees to quickly find and access the information they need to complete their work.
In fact, AWS internal research indicates that the typical worker spends 53% of their time on “work about work,” rather than on skills development, business strategy, and innovation. This “facilitation overhead” is choking productivity and dampening the employee experience. Workers jump from app to app, and this context-switching creates delays. Often, they forget where they saw a useful piece of information, even in a search they did just minutes earlier, forcing them to retrace their steps across highly fragmented enterprise systems and navigate problematic silos.
The Solution
Amazon Q Business was developed by AWS as a generative AI assistant to provide a higher level of workforce productivity. But as a managed service, it didn’t necessarily fit neatly into an independent software vendor’s (ISV) business model.
ISVs, however, saw how Amazon Q made it easy to leverage their data from multiple sources with agents and plug-ins. So AWS worked with key ISVs such as Asana—a leading supplier of workforce management tools and platforms—to develop a solution that worked best for them. The goal was to use Amazon Q as a secure, efficient way to identify, surface, and use data across multiple data sources so it can be applied to accelerate and streamline workflows.
Herrick notes that the root cause of employee frustration in today’s multi-application enterprise environment is the disconnection of tools and teams. This disconnect creates a fragmented workplace, making it harder than it should be to find specific data in different applications and achieve objectives.
Asana collaborated with AWS on developing Q Index features that enable ISVs to access Q Business data via API calls to create better, more productive end-user experiences. Using a single-pane-of-glass console, ISVs can easily manage all their data within their increasingly complex set of tools and applications
This development took place in two phases. First, Asana leveraged generative AI to create focused solutions for narrow, individual problems, such as delivering smart status updates on projects. Quickly after that, however, Asana moved to the next phase—letting the technology do even more to enhance end-user productivity. In this phase of development and rollout, Asana targeted the ability to treat generative AI not just as a chatbot or agent, but as a virtual teammate that could deliver extensive contextual knowledge.
This tool—AI Studio—was launched as a no-code workflow automation builder within the Asana platform. It helps organizations do the work employees don’t want to do, making it easy to build smart workflows tailored to the organization’s processes. The integration with Q Index allows AI Studio to access cross-application context beyond Asana’s boundaries. End users get insightful answers to complex questions—even those that require accessing multiple data sources—without switching applications. Organizations can use AI Studio workflows to automate feature-request processing across Asana, Google Drive, Slack, email, and more. This partnership eliminates silos while maintaining enterprise security and permission controls to drive new levels of productivity.
The Result
Thanks to the collaboration between AWS and ISVs like Asana, organizations can quickly reap the benefits of using generative AI to improve workforce output and efficiency. ISVs can use Amazon Q Business to eliminate silos, surface data in context, and identify insights that enable swift action. The integration of Amazon Q Business and enterprise tools like Asana creates a tightly connected ecosystem, enabling true cross-application AI automation and insights that drive improved business outcomes in alignment with enterprise priorities. To get started, visit Amazon Q Business.
