Planview has updated the generative artificial intelligence (AI) copilot it provides to not only create summarization and simplify onboarding to new projects but also analyze metrics in real time to identify opportunities to make workflows more efficient.
The Planview Copilot can be applied across both the portfolio of project management applications and value stream management (VSM) platforms the company provides to make it simpler for organizations to invoke a mix of large language models, AI agents and regenerative augmented generation (RAG) techniques to streamline the management of business processes, says Alan Manuel, group vice president for product management at Planview.
While there may never be such a thing as the perfect plan, AI is helping to make organizations more reactive as new information and events invalidates some of the assumptions that were used to create the original plan, he notes.
Planview reports that over the past six months 45 customers have been participating in a co-development program, dubbed Inner Circles, and that interactions with generative AI tools have increased 400% since the beginning of the year.
The overall goal is to make the data in these applications more accessible to a broader range of end users that have a vested interest in the outcomes being tracked by these applications, he adds. In fact, Planview has created multiple personas using AI technologies that span everything from team leaders to senior executives that previously would have required organizations to craft separate classes of dashboards..
It’s not clear to what degree AI technologies will transform the user interface of applications, but the longer term trend appears to be relying less on conversational interfaces in favor of processing more tasks in the background.. “AI is becoming less visible,” says Manuel.
In general, end users are more interested in seeing the right data at the right time than they are necessarily having to repeatedly engage with an AI agent, notes Manuel.
It’s not likely that AI will replace the need for project managers. Project managers will still need to cross-check AI outputs for accuracy. However, the role could become more strategic as it becomes easier to surface insights. Most organizations still find it difficult to identify dependencies between projects that could result in deadlines being missed. AI technologies, for example, will make it easier for organizations to understand how resources might need to be reallocated to ensure specific outcomes.
Theoretically at least, organizations should see a major increase in productivity if project deadlines are met more often. The challenge historically is that most project management applications required someone to collect and enter manual data that was usually out of date, often a few minutes after it was entered. Thanks to advances in data engineering and AI it’s now possible to automate both the collection and analysis of project management data. The issue now is navigating all the cultural challenges that will inevitably arise as organizations begin to break down the silos that exist between various departments that historically have not tended to work as closely with one another as they should.
