Both Big Tech and U.S. lawmakers intend to spend tens of billions of dollars over the next few years to further develop and deploy AI models. But as more companies launch new artificial intelligence products, do these products actually deliver the results and momentum they're looking for?
Rich Lesser, BCG Global Chairman, speaks with Catalyst about the transition technology companies need to make to move their AI projects to “tangible” outcomes.
“And the challenge is that if you take a 'thousand flowers bloom' approach, it doesn't lead to any real outcomes at scale. So companies are asking themselves what are the key outcomes that can drive meaningful initiatives? We're increasingly focused on overall business performance,” says Lesser.
For more expert insights and the latest market trends, click here to watch the full episode of Catalyst.
This post was written by luke carberry morgan.
video transcript
The hype continues this earnings season, as tech giants fighting to win the AI arms race report huge capital spending in their latest financial results, increasing spending on Microsoft's technology.
Now, a bipartisan group of senators led by Chuck Schumer is recommending that Congress spend at least $32 billion over the next three years to develop its own artificial intelligence.
So what is the next big catalyst for this technology?
BC G Global Chair rich and not rich, join this discussion now and discuss.
Thank you for joining us.
So, we're always talking about how companies talk about AI and their bottom line and make it sound good, but it's not always clear how they're actually leveraging AI.
So, can you tell me how you see companies that are actually starting to leverage AI to increase efficiency and what the numbers are?
So I think we're in a really exciting transition period right now. During his first year or so, so many companies were just focused on understanding technology and what they could do with it.
I think more and more companies are now realizing that there is a huge opportunity here.
And the challenge is that taking the 1,000-flower approach doesn't lead to any real results at scale.
So we're seeing companies increasingly focus on what key outcomes they can drive to meaningfully drive overall business performance.
To give you some examples in the world of pharmaceutical research and development, we're actually seeing people using it to create new molecules. These molecules may be just as effective, but they are actually safer molecules. With regulatory processes full of paperwork and reporting and protocol creation taking up to 80% of the time, claims processing times typically take a week or more, but turnaround time can be reduced to hours or days. It will be shortened. Improve customer service, free up customer service agents' time to focus on customers, and spend less time reading and memorizing manuals. All of these are business dependent.
You have a truly tangible opportunity to drive both greater customer value and greater financial impact.
And what CEOs need to communicate to their employees and public company shareholders in terms of why this investment in AI makes sense is a very exciting one for high-net-worth companies. It's starting to become a thing.
What do people, investors want to see, what are the key questions that need to be answered at this stage of the cycle.
So we're not there to tell investors that companies are doing generative AI just to do nifty pilots; I would like to argue that there is a need to emphasize that there is a need to meaningfully improve outcomes. This often means transforming entire business functions and processes.
Sometimes it's important to create an entirely new business model to drive sales growth, and they're going to learn, but they're going to learn by doing, not by writing a bunch of papers. Become.
And I think that's what investors want.
And within a relatively short amount of time, you should see tangible business results.
I, along with my employees, see that as an essential part of the journey.
In fact, BC G has this framework that we talk about all the time.
1020 70 10% of the work on algorithms.
20% of work is done on digital and data platforms.
70% is about HR process organization, leadership culture, everything from the realization of very interesting features to the actual implementation.
And there's a lot of effort being put into reskilling.
There's a lot of work being done to change the way people view and work with technology, and the way employees collaborate across departments.
Because if we stop looking at AI through a very narrow lens and look at it more broadly, we can actually create huge value.
And this requires a huge investment in human resources.
And we're finding that companies that take the lead in AI actually get higher scores from glassed-out employees, and that workplaces that care more about their employees and want to invest in their skills do. , you'll find that it's actually seen as a more positive place to work. Not just your current job, but also any jobs you might have in the future.
So there are different messages, but both are important messages for investors and employees.
Well, I'm interested in this affluent population because the survey results include a variety of consumer sentiment surrounding AI. Were you surprised that consumers were a little more positive than you expected?
Something to get everyone talking.
Is this scary elephant in the room going to steal everyone's jobs?
I think we all wear three hats.
We are consumers, citizens, employees, the majority of Americans, and in some cases, students.
It depends on what age we are talking about.
But if you're a consumer, or frankly a student, you're going to look at this and say, information should be more easily available and information should be able to navigate my world.
It should help you figure out what's important and what's not.
As an employee, I think you have mixed feelings.
These are the skills you want to learn because of where the world is heading.
But, understandably, I will be concerned about whether I will ultimately be able to replace my role or job, or whether I will have the skills I need to acquire.
And of course, as a public, we have concerns about misinformation and risks to critical infrastructure and other things.
So I think we each wear multiple hats, and I don't think it's surprising that people look at this world of AI differently, depending on which hat they're wearing. Depending on the meaning, a lot of it becomes richer or less, and it's always great to talk to you.
BC G Global Chairman, thank you very much for joining us this morning. I am very grateful to you.
