Panel featuring: Åsa Tamsons – Executive VP, Ericsson Ellinor Schrewelius – IR Principal, Verdane … [+]
Across the business world, the idea of proactive adoption of AI tools is taking on. There’s often a sense of urgency that besets leadership teams as they look at what’s happening in a particular sector. But how do they move forward?
One of the bright spots of the Davos summit this January was a panel with Laura Rudas of Palantir and Ellinor Schrewelius of investment firm Verdane, as well as Asa Tamsons of Ericsson and Solveigh Hieronimus, a senior partner at MicKinsey.
Here are some of the insights that came out of that talk related to business progress with AI in this exciting era.
The Need for Speed
Noting that “the second best time to start is right now,” Tamsons suggested that businesses that aren’t working on integrating AI are about to be left in the dust.
“Progress that would’ve taken 20 or 30 or 40 years can now be done much faster,” Schrewleius added, citing the power of both AI and quantum computing.
“Speed matters,” said Rudas, “so apply the best technology you can get.”
Pondering Energy Sourcing
“Take energy as an example,” Schrewelius said. “There are a lot of (types of) progress going on (with) different energy sources. There will be massive challenges in how you distribute and consume and produce them … but also, the computing power will actually enable that to actually work.”
That rings true when you’re looking at how AI impacts business far beyond the IT vertical. We’ve seen how, for example, the U.S. is about to push forward on new nuclear power initiatives, mostly just to feed data centers. TerraPower is one corporate player, and then there are new government directives like the ADVANCE Act and plans to roll out new nuclear power capabilities.
So this will likely be a big part of the discussion when it comes to scaling of business applications.
Solve the Biggest Problem
In talking about understanding business trajectories, the panel discussed the need to look for the most relevant applications to business, and then apply the right technology and the right teams.
“You need to link the artificial intelligence that is available with your enterprise data, to generate real time outcomes that solve your key problems,” Rudas said. “It’s not about … producing fancy (solutions) for your board meeting. It’s: what is the hardest and biggest problem, because you have this unique opportunity with AI, to solve it, and that’s what AI should be for.”
In other words, it’s not just a question of getting some kind of generic or boilerplate solution from industry analysts as a whole – it has to do with what the particular business actually needs, and what is most challenging in that particular organization.
It might be related to product development, or it might be related to customer relationship management. Maybe it’s something around talent acquisition. Whatever it is, that identification process is important. For more inspiration, check out this list of no less then 50 use cases from Microsoft, for starters. (enriching employee experiences, bending the curve, etc.)
Creating Ontologies
Later on, Rudas also talked about creating an ontology around the central premise for AI application.
She pointed to the use of unstructured (or less structured) data as a principal.
“Creating a data foundation that enables collaborations is something I would start with,” she said.
In other words, businesses use data aggregation to get what they need in place, to target their big use cases, and then they apply the technology, and the people.
Breaking Things Up into Steps
The panel also addressed making big challenges into bite-size chunks.
“It’s a leadership challenge,” Hieronimus said.
That was one additional takeaway, to me, from this session: it makes sense to map out AI initiatives in a modular way, to better understand how to integrate these technologies. Even back in the cloud era, analysts (and tech journalists) were talking about how integration is important – so that the new technology helps human workers, rather than hindering their workflow processes. That deliberate thought and care will make all the difference.
Stay tuned as we detail more of the insights that came out of recent events in this big banner year for AI.