AI Lens
AI news, hot topics, advancements, and discussions about how AI is reshaping business and society.
Your focused view on the emerging hot topics in the Age of A.I.
AI Lens
Season 1 Episode 8: AI at CES 2026: The New Business Reality
AI Lens brings your focused view on the emerging hot topics in the Age of AI!! We provide AI news, hot topics, advancements and discussions about how AI is reshaping business and society.
In Episode 8, we’re diving into the biggest themes, insights, and warnings around AI that emerged from CES 2026, held January 6th through 9th. After attending presentations, panels, and training sessions — including deep‑dive discussions from leading AI researchers — a clear picture emerged: AI isn’t coming. AI is here. And it’s permeating everything.
From consumer tech to robotics, from enterprise strategy to quantum computing, CES 2026 made one thing unmistakably clear: We’re entering a new era where AI is not a feature — it’s the foundation.
Let’s unpack what that means for businesses.
You're listening to AI Lens, your focused view on the emerging hot topics in the age of AI. We provide AI news, hot topics, advancements, and discussions about how AI is reshaping business and society.
SPEAKER_00:Today's episode is a special one. We're diving into the biggest themes, insights, and warnings around AI that emerged from CES 2026, held January 6th through January 9th. After attending presentations, panels, training sessions, including deep dive discussions from leading AI researchers, a clear picture emerged. Sorry.
SPEAKER_01:That's right. AI isn't coming. AI is here, and it's permeating everything from consumer tech to robotics, from enterprise strategy to quantum computing. CES 2026 made one thing unmistakably clear. We're entering a new era where AI is not a feature, it's a foundation.
SPEAKER_00:So, Liz, let's unpack what that means.
SPEAKER_01:All right. Well, first of all, AI is everywhere.
SPEAKER_00:And by everywhere, we mean everywhere. One of the most striking takeaways from CES this year was just how thoroughly AI has woven itself into every category of technology. We're not talking about AI-powered stickers slapped onto products. We're talking about AI as the core operating layer across wearable tech, televisions and home entertainment, smart home ecosystems, automotive interiors in ways you couldn't even imagine, interactive driving experiences, for instance, health and wellness devices, including at-home diagnostics, one of which we'll get into a little bit, spatial computing platforms, energy management systems, accessibility tools, robotics, software and hardware across the board. You get the idea. The reality was unmistakable. AI is no longer a vertical, it's a horizontal layer touching every industry.
SPEAKER_01:And because of that, businesses can't treat AI as a side project or a niche to have. It's become the baseline expectation, the new competitive minimum. Now, let's talk about what we saw there in terms of robotics. It was really the rise of the robotics. There was a massive presence at CES 2026 with robotics, and the shift from robots as tools to robots as adaptive partners is really accelerating. We saw robots with really impressive dexterity. They were capable of manipulating delicate objects. We had machines demonstrating balance and agility that would have seemed impossible a few years ago. We saw industrial robots that learn in real time as they operate in dynamic environments, warehouses, things like that. And we also saw early examples of robots that can adjust workflows based on the context, not just pre-programmed instructions.
SPEAKER_00:So the big theme really was robots are becoming learners, not just doers. This opens the door to entirely new categories of automation, not simply replacing humans, though, but augmenting them in ways that expand what teams can accomplish.
SPEAKER_01:Okay, now why AI initiatives fail.
SPEAKER_00:Yeah, over the past few years, we've we started to understand better how we can leverage AI for the benefits that everybody talks about. But we're also becoming clearer about what some of those risks are and how best to mitigate those. So bottom line is many AI initiatives fail, even at the enterprise level, for typically what are consistent reasons. Things like the lack of clear goal, poor initial assessment or direction when it comes to defining what they want their AI to do, weak executive buy-in, poor rollout of AI tools, lack of employee upskilling, insufficient structure or governance, low employee engagement, and missing guardrails or oversight was a very big one. One we've talked about in prior podcasts as well.
SPEAKER_01:Right. And I think you know, a lot of it is this is a new innovation in business. And when you're thinking about this, it's really difficult to one, make sure you have all the guardrails up because we want it to be implemented in the way it was meant to be implemented. But in addition to that, getting your employees involved in it.
SPEAKER_00:Aaron Powell So a lot of creative ideas and how best to handle that, et cetera. But the bottom line is the message from experts was blunt. AI doesn't fail because the technology is bad. AI fails because the organization isn't properly prepared and ready. So that's a key differentiator. A lot of people lay the blame at AI, but it's not really AI, it's the way the organization handles and rolls out the AI.
SPEAKER_01:Right. We attended quite a few sessions on how businesses should adopt AI. And really you need to be prepared before you just throw it out there.
SPEAKER_00:Now, this is why some speakers emphasize that AI really is a journey. Some people like to look at it as a sprint, other people like to call it a marathon, but really it's a combination of everything. It's really a journey, right? It's not just about speed, okay, in terms of executing your or implementing AI. It's about direction, alignment, and adaptability from you and your staff.
SPEAKER_01:Right. And three general keys to successful AI adoption are tools, skills, and mindset. Across the sessions, those three themes kept resurfacing as the pillars of success for AI transformation were discussed by these experts.
SPEAKER_00:So for tools, for instance, organizations need the right platforms, models, infrastructure, even workflows. But tools alone don't create value, right? So skills would be the second one. Teams must be trained, not just in how to use AI, but in how to think with AI. A very important differentiator there. So I don't have to say, okay, I'm gonna try and play around with the workflow. It's understanding how to reinvent work, leveraging AI, as opposed to just like throwing AI at some existing problem.
SPEAKER_01:And a lot of was discussed about the fear that AI would take over the jobs for employees. And really what most experts thought was that instead the employees need to be retrained to manage the AI. If you think about it, I know like when I was in college, it was before the internet was out there in the public, and I would say most of the jobs that exist today didn't exist. That's another change we're having right now with AI.
SPEAKER_00:So upskilling really isn't optional. It's going to be the differentiator. We'll get into that a little bit more later. The third theme, if you will, your general theme, is mindset. This is a really big one. A mindset that sees AI as a partner and not a threat. A mindset that values experimentation, iteration, and learning. A mindset that understands AI is not magic. It's a capability that requires stewardship. So that goes to that point that Liz just made. Together, these three elements. No, no, exactly. Together, these three elements form the foundation of what I call sustainable AI adoption.
SPEAKER_01:Right. And humans are the differentiator. One of the most powerful themes, like I was saying earlier at CES 2026, was the role of humans in an AI-saturated world. One expert made a comment that really captured the theme. AI lifts the floor, but people lift the ceiling.
SPEAKER_00:Think about that for a moment, right? So AI, again, can raise that baseline productivity, but what you're ultimately going to get out of AI really is going to be dependent on humans and your teams. So AI can automate, accelerate, and optimize, but humans create that key differentiation. They can tailor the AI to unique business contexts. They can provide the judgment that is missing in AI today. They can understand the nuance in ways that only humans can so far. They can manage the murky middle of AI workflows. You know, you have your inputs, your middle, and your outputs. It's that middle where people really get hung up because they don't manage that properly. And they can ensure that outputs align with goals, ethics, and brand identity, all key components.
SPEAKER_01:Right. And that's why companies really need to keep those long-term employees who have all of the information about the data and understand the way the company runs. Those will be the most important employees in training and managing your AI.
SPEAKER_00:Yeah, but there were some stories about like how large enterprises were bragging about how they could lay off all of their experienced employees. Well, the problem with that is you lose all that institutional knowledge. And when it comes to data, for instance, not having that knowledge ends up making you much more vulnerable to the risks that happen when you implement AI. So it's important to make sure that you look at this as more of a symbiotic relationship. Cut costs where you can, certainly, but it's not enough. And we'll explain why later.
SPEAKER_01:Right. You know, if companies use AI only to cut costs or eliminate jobs, they're really entering a race to the bottom. And that's because cutting costs is easy to copy. Differentiation, having that added value, that extra unique quality for your company. That's not.
SPEAKER_00:So the companies that will win are the ones that are going to invest in their people, not simply look to replace them or eliminate them.
SPEAKER_01:Right. There's and something else that came about, there's really a visibility gap when we're talking about inputs, middle, and the outputs.
SPEAKER_00:Yeah, it's a very important concept. So when it comes to AI workflows, every AI system, when you when you make it boil it down to its most basic level, you have your inputs, which are like the data, the prompts, the context. Okay. You have your outputs, which are really the results, the predictions, the recommendations. And everybody tends to really think about inputs and outputs, but it's the middle that's critical. So that middle is really opaque, complex, often misunderstood processes where the model interprets, transforms, and generates those outputs. So the middle is where the risk lives.
SPEAKER_01:Right. And that's why it's important to have oversight with humans after right when that middle part happens. Experts emphasize that proper human oversight is really essential. One, to catch errors, to prevent drift, to get away from the subject, also to maintain alignment, ensure quality, and to protect against unintended consequences.
SPEAKER_00:That drift one, if I can jump in, is an important one too. Like what happens is people get going, they'll see the power of what the AI can do. They start to add on additional stuff, or they'll start trying to fine-tune it in ways that takes you off of what your initial goal was. So make sure that you stay on path. That's a critical component to successful AI implementation and adoption.
SPEAKER_01:Right. And AI isn't dangerous because it's powerful. AI is dangerous when it's unmanaged.
SPEAKER_00:That's a great point. Absolutely.
SPEAKER_01:Right. You know, and having the right infrastructure and global standards and constraints in place is going to be key here.
SPEAKER_00:So when it comes to the infrastructure for AI, so let's back up. Let's talk about it from a broader perspective. AI infrastructure really is scaling at an unprecedented pace. We we saw a lot of seminars that discussed it. But the physical world really risks becoming the bottleneck for AI. I mean, we're hitting constraints in land, power, I'm sure you've heard some of that. Skilled labor in building these out, data center construction timelines. So while hardware and algorithms are becoming more efficient, and we can talk about that in future podcasts, how those are becoming more efficient and more effective, demand is growing at an even faster pace. And so the physical world is where a lot of constraints are going to be as we have to get better at solving for those constraints. You know, and it isn't a local issue either. It's a global challenge that will shape the next decade really of AI development.
SPEAKER_01:Really interesting point. And I know one of John's favorite topics is quantum computers and quantum computing. And that's supposed to be really the next shockwave.
SPEAKER_00:Shockwave is putting it lightly. So one of the most electrifying predictions from CES, in my opinion, came from experts discussing quantum computing. So the consensus is forming that fully realized quantum computing, from what I can tell, is roughly two to three years away from triggering what's going to be a fundamental shift in computing power. We're talking about condensing hundreds of years of classical compute into minutes or seconds. So think about what that's going to do. It's going to absolutely accelerate scientific discovery at unprecedented rates. It's going to unlock solutions to problems currently considered absolutely unsolvable or intractable. This isn't hype, by the way. It really is about preparation. So you have to be prepared for what's coming. So quantum computing won't replace classical computing. It will supercharge the most complex domains. Things like material science, drug discovery, climate modeling, optimization problems, advanced AI training. It's going to take AI to an even more incredible level. And it helps solve some of those problems we talked about with infrastructure because it can handle so much with such far fewer servers and power needs that it could be, you know, just again, a great benefit for the infrastructure as well.
SPEAKER_01:Right. The next wave of breakthroughs actually could arrive much faster than anybody expects.
SPEAKER_00:So when we think about the future of AI and how it's going to be this remarkable technology, yes, it will on its current growth rate. That's exponential in and of itself. But quantum computing is like throwing gas on the fire. It's going to accelerate even faster.
SPEAKER_01:I know one of your favorite sessions, John, was the IBM session and some of the trainings.
SPEAKER_00:Yeah, IBM has gone through some massive changes. We're going to save some of the details for a future podcast, but they had actually an operational person there discussing all that they have accomplished in the past three years. Their goal was to save$2 billion in two years by implementing AI and radically changing the way they did business. They hit$2 billion in productivity gains and savings within one year. Within three years, they hit$4.5 billion, far exceeding the goals that they talked about. So they had a lot of great insight in terms of what made it successful launch for them in terms of adopting AI. And they had some nice interesting insights that we'll share with you on a future podcast.
SPEAKER_01:All right. Well, you know, one thing that I wanted to point out, the most effective use of AI, it's not using it every opportunity you can. It's about using it responsibly and effectively. You want to make sure that you're getting the tool that will most effectively help your business.
SPEAKER_00:It's not about how much AI you're going to use. It's really about how responsibly and effectively you use it. So Yeah.
SPEAKER_01:And one thing became abundantly clear, I thought at least, through uh at CES 2026. We're entering a world where AI is pretty much the default. The winners in this new era won't be the companies with the most AI. That's why I made that point. They'll be the companies with the best people, the clearest strategy, and the strongest mindset.
SPEAKER_00:So remember, AI is not, I'm sorry, AI is a journey, one that requires curiosity, discipline, and vision. And as we move into this next chapter, the partnership between humans and AI will define the ceiling of what's possible.
SPEAKER_01:So thanks for joining us on this special CES 2026 recap on our AI Lens podcast. If you enjoyed this deep dive, share it with somebody who's thinking about the future of AI in their business. Until next time, stay curious, stay informed, and keep your lens focused on the future. Bye, everybody.