Panel with: Åsa Tamsons – Executive VP, Ericsson Ellinor Schrewelius – Main, Verdane … [+]
In the business world, the idea of proactive adoption of AI tools is underway. There is often a feeling of urgency that gives management teams by looking at what is happening in a particular sector. But how do they advance?
One of the light points of the Davos summit in January was a panel with Laura Rudas de Palantir and Ellinor Schrewelius from the Verdane investment company, as well as Asa Tamsons by Ericsson and Solveigh Hieronimus, a main partner in Mickinsey.
Here are some of the ideas that came out of this conversation linked to commercial progress with AI at that fascinating time.
The need for speed
Note that “the second best time to start is right now”, Tamsons has suggested that companies that are not working on the integration of AI are about to be left in the dust.
“The progress that would have taken 20 or 30 or 40 years can now be made much faster,” added Schrewleius, citing the power of AI and Quantum IT.
“Speed counts,” said Rudas, “then apply the best technology you can get.”
Reflecting on the energy supply
“Take energy as an example,” said Schrewelius. “There are a lot of (types) progress in progress (with) different sources of energy. There will be massive challenges in the way you distribute and consume and produce them … but also, the computing power will actually allow it to actually work. »»
This sounds true when you look at how AI has an impact on business far beyond the IT vertical. We have seen how, for example, the United States is about to move forward on new nuclear energy initiatives, mainly just to supply data centers. Terrapower is a business player, then there are new government directives such as Advance Act And plans to deploy new nuclear energy capacities.
It will therefore probably be a large part of the discussion with regard to the scaling of commercial applications.
Solve the biggest problem
Speaking of understanding the trade trajectories, the panel discussed the need to look for the most relevant applications to businesses, then apply the right technology and the right teams.
“You must link artificial intelligence that is available with your business data, to generate real -time results that solve your key problems,” said Rudas. “It is not a question of … producing sophisticated (solutions) for your meeting of the board of directors. It is: what is the most difficult and the biggest problem, because you have this unique opportunity with AI, to solve it, and that is what should be. »»
In other words, it is not only a question of obtaining a kind of generic solution or passout of industry analysts as a whole – this has to do with what the particular company really needs And what is most difficult in this particular organization.
It can be linked to the development of products, or it can be linked to customer relations management. It may be something around the acquisition of talents. In any case, this identification process is important. For more inspiration, see This list of less than 50 use cases From Microsoft, to start. (enrich the experiences of employees, fold the curve, etc.)
Create ontologies
Later, Rudas also spoke of the creation of an ontology around the central premise of the application of AI.
It highlighted the use of unstructured (or less structured) data as main.
“The creation of a data foundation that allows collaborations is something I would start with,” she said.
In other words, companies use data aggregation to get what they need, to target their large use cases, then they apply technology and people.
Break things up
The panel also discussed major challenges in the size of a bite.
“This is a leadership challenge,” said Hieronimus.
It was an additional point to remember, for me, of this session: it is logical to map AI initiatives in a modular way, to better understand how to integrate these technologies. Even at the time of the Cloud, analysts (and technological journalists) spoke of how integration is important – so that new technology helps human workers, rather than embarrassing their workflow processes. This deliberate thought and attention will make all the difference.
Stay listening as we further detail the ideas that have come out of recent events of this great year for AI.
