“Software is eating the world, but AI is going to eat software.” — Jensen Huang, CEO of NVIDIA
Savvy project managers know that not even close to 100% of projects are successfully completed on time. The real stat is more like 35% — the rest of the time, projects are canceled, delayed, rolled into other projects, rescheduled, etc. Which isn’t bad per se but isn’t the best use of organizational resources either (frustrating!).
There is hope; by 2030, Gartner has predicted that 80% of project management tasks will be handled by AI.
What will the PM jobs of the future look like, after this radical AI transformation takes place?
1. Communication Improvements
Future project managers will have a much easier time communicating with their team members. Whether hybrid or remote, teams have lots of different ways of keeping in touch and providing key status updates, and as AI integrations crop up in the tools they use day to day, the net effect will be more frictionless project communication. In fact, Slack has already rolled out their own AI integration; called “Slack GPT,” it includes an AI platform and features already built out in the popular messaging tool. A busy PM, hopping from channel to channel in between leading standups, will find a lot to love in the app’s ability to summarize conversations — and if circumstances dictate that PMs do have to miss a huddle, they can also direct AI to ‘attend’ in their stead and generate a recap of key points.
AI is also making waves in the larger world of work management and enterprise planning applications. Within Oracle’s Enterprise Resource Planning Cloud and Enterprise Performance Management Cloud solutions, PMs can receive instant status updates and use a smart digital assistant to review and update essential project information. The digital assistant can also be leveraged by workers, making their own time and task updates easier and faster to provide to the PM. This only describes one area of the integration, but Oracle has infused their product offerings with the technology to great effect: “Our pervasive AI strategy delivered via continuous product updates ensures rapid adoption with immediate business results,” reports Rondy Ng, SVP, Oracle Applications Development.
One caveat, here: It’s tempting to wave our magic wands and assign every laborious task to AI, but it’s still not a good idea. Tasks that don’t involve a lot of data, or that are really creative, or that require us to do a lot of ethical or emotional consideration, aren’t good fits for AI. As an example, data storytelling does task us with analyzing and presenting information, but we must do that in a way that appeals to emotion to craft the narrative. If we let AI help with this, a human — probably the PM — will need to review and revise the result before we can share it with stakeholders.
[RELATED: Artificial intelligence needs human judgment to work.]
2. Service Optimizations and Improvements
Internal services make life at any organization easier, but project managers just might get the lion’s share of the benefits when these services receive improvements. Faster, smoother internal operations mean a PM’s questions are more likely to get answered the first time they’re received, as well as improving the quality of the answers they can provide to others. As an example, using AI to analyze the output logs from an organization’s CI/CD tools would allow a project manager the ability to provide more detailed, more accurate predictions when asked about the lengths needed for future coding sprints.
It’s important to note that that example is very narrow, only focused on one project’s particular sprints and time frames. We don’t have to be so limited when using AI, though. Using a similar process, an organization might opt to analyze its support tickets with AI. The insights available from this (otherwise very large and complicated) dataset would allow the business to optimize services based on statistics like the most frequent requests or ticket types, average resolution times/ticket topics, and many more metrics.
In the manufacturing space, AI and ML technologies are already hard at work improving automation at factories worldwide, which is a mission that at least one market leader views as essential. “By simplifying the deployment of AI in industrial use cases,” remarks Dominik Wee, managing director of manufacturing and industrial at Google Cloud, “We’re helping employees augment their critical work on the shop floor.” Google Cloud’s integration with Siemens AI/ML-powered Digital Industries Factory Automation toolset strives to help manufacturers fully scale AI at a global level, unlocking more powerful ways of understanding factory data.
[RELATED: Living on the edge: How edge computing is already a game changer.]
3. Efficiency Improvements
AI systems provide a higher degree of efficiency because they are designed for and operate in our modern world, where integration is key. Any application or tool that doesn’t sync or integrate with any other software or databases might as well be coded on actual paper. Because AI project management tools are smart tools made by smart people, though, they’re written to take advantage of the fact that all of these other tools and databases exist, drawing conclusions from their ability to grasp and process the part as well as perceive the whole. “By using AI-powered data analysis that looks at data from past projects, we’ll be able to predict, with a much higher degree of confidence, how much a project will cost and how long it will take,” explains Tom Davenport, author of ‘The AI Advantage: How to Put the Artificial Intelligence Revolution to Work.’
Risk management is another area where AI delivers complex data to greatly augment human efforts and outputs. The earlier on in a project that potential risks can be spotted and dealt with, the better the project’s chance of success. Seasoned PMs can tally up potential risks quickly, but this ability comes with experience and time. When you haven’t got those options available, though, you can link your PPM tool with an AI-enabled application and request that the AI generate a list of possible risks. While this should never be the only risk management tactic at your disposal, it’s a good starting point for new PMs who want to leave no stone unturned until they gain that essential experience of their own.
Conclusion
While the percentage of projects successfully completed may never reach exactly 100%, artificial intelligence, and machine learning-based technologies will allow project managers and their teams to stack up more wins.
From speeding up key risk management practices to internal service upgrades to easier project communication, AI can and should be leveraged throughout the organization. Once AI gets the busy work out of the way, human PMs are free to focus on what matters most – the project itself.
