Latest news with #CXOTalk
Forbes
31-07-2025
- Business
- Forbes
AI Technology Outdated? That's Okay, Just Keep Moving
One can be forgiven for thinking that everyone is mastering artificial intelligence, while they are scrambling to understand it and catch up. Let's face it: no one knows where AI is going to take us. Not a year from now, not even a month from now. Everyone is experimenting and learning. Not even the chief information officer of one of the world's leading technology providers can say with certainty where AI is taking things. And that's okay. Today's AI intelligence platforms and offerings are becoming outdated at a blinding pace. Which raises the question, where should one insert their investments, as there is the risk of pouring money into outdated technology? This typically doesn't sit well with boards, or employees that have to depend on the technology. But the worst strategy is to hold off and wait for the next iteration of the technology, because that day will never come. That's the word from Art Hu, CIO of Lenovo Global, who urged decision-makers to work with the AI they've got, which is more productive than waiting for new versions to come along – no matter how quickly they appear on the scene. Everything is uncertain, so don't count on certainty, he said, speaking with CXOTalk's Michael Krigsman in a recent interview. 'Agility beats certainty as an AI investment strategy,' he said. Accept that the AI technologies chosen today will not remain at the forefront for long. Hu shared Lenovo's own approach to AI investment, which emphasizes "no regret" investments, even if the technology becomes outdated. It's not about perfection – it's about adaptability and agility, he again emphasized. Too often, organizations get caught in 'analysis paralysis,' unable to move forward because they are unsure how the technology will develop. Don't wait for guaranteed outcomes that may never materialize, he said. AI technology 'is advancing at a rapid pace – almost by day or by week or by month,' Hu said. Analysis paralysis sets in because 'the frontier seems to be moving so quickly,' and people don't know how to handle it. The best approach is to keep learning and tolerate ambiguity, he explained. At his company, that translates into AI decisions made by a cross-enterprise executive committee. 'In terms of the investment question and our investment strategy, everyone is expected to engage, and that helps tremendously,' he said. 'There's no need to explain what we're doing or why, and that really helps the team step forward.' An important part of this open AI strategy is to take on the prevailing fear that AI is taking jobs away. This narrative 'portrays workers as passive victims rather than active participants in change,' he said. 'AI automates specific tasks within jobs, but humans still design job roles and define organizational goals.' There's a productivity benefit as well, 'Software engineers who once spent only 10-15% of their time coding now have access to capabilities that previously required specialized designers or prototypers,' he explained. As a result, professionals are freed up to focus on higher-value activities, such as architecture, security, and business outcomes. The key to successfully building an AI partnership is to 'help teams break down their roles into tasks, identify which ones AI can enhance or replace, and restructure positions to focus on distinctly human contributions,' said Hu. It's important to reframe the AI discussion, he continued. 'The point is to not lose momentum and to continue in the face of that uncertainty.' For its part, Lenovo seeks to create 'the environment that invites people in,' Hu related. 'If you want to work in legal, if you're in marketing, if you're in finance, if you're in HR, there's something for you to work with that will help invite you in the door.' That 'pull' results in high levels of interest among employees, and a desire to learn more, he added.
Forbes
28-06-2025
- Business
- Forbes
The Joy Of Prompting: AI Can't Read Minds, So Learn to Spell Things Out
Learn to talk the talk First, the good news: it is now possible to develop programs, illustrations, or extract AI output with plain-spoken English prompting, versus the need to write code in Python, R, or SQL. Now, the reality check: this new form of engaging machines, prompting, requires an ability to know exactly what to ask, and be able to drill down to specific elements. Otherwise, executives and business users will end up either with vague, rehashed, or wrong answers to their queries. This can be very problematic as decision-makers assume AI knows all. Prompting may be the ultimate stage of self-service, no-coding environments, which have been evolving for decades now. Executives and business users can just make plain-English queries against language models, and see relatively fast results, be it reports or applications. It can even deliver via spoken prompts. Now, emerging memory features may help retain prompts for future use and refinement. All good, right? But we need to do prompting right, according to AI expert Nate B. Jones, who was Michael Krigsman's recent guest on CXOTalk. Krigsman teed up the discussion with the significance of prompting, as 'the secret skill that taps into AI's real capabilities, transforming large language models from flashy demos into engines of real-world productivity.' The art of prompting collides with some of the vagueness or inconsistencies of human language, Jones explained. That was the whole purpose of computer languages in the first place – since they offered precise, step-by-step processes. But while LLMs may have more intelligence than standard databases and applications, they aren't mind-readers. 'They are not incredibly reliable yet at inferring your intent if you are not precise about what you mean or want," said Jones. "They don't do that reliably. They guess, and they might guess right, and they might guess wrong.' Then there's time involved in waiting for responses to prompts. Though they may be delivered relatively quickly, end-users may have to prompt over and over again to try to get things right. Awaiting the response to a prompt reminds Jones of the old punch-card days in computing, when programmers had to wait until a job ran before they knew if the instructions on the cards were correct. Now, we end up awaiting prompt results, which could take up to 20 minutes to generate, to see if they worked. Repeated narrowing-down of prompting may work fine for smaller models, but more sophisticated instances of genAI may take up an inordinate amount of time. 'If you give something to a frontline model and it's running for six minutes, eight minutes, 10 minutes, 20 minutes, and it comes back, and you did not clearly specify the scope, you're going to be frustrated," Jones said. There are countless models in the AI space, and determining the best one to direct one's prompts also takes some understanding of the topic, the context, and the model being queried. 'A lot of the art is in figuring out what is this subject, what is my intent, what is the right model for that?" he explained. "And once I have all of that figured out, now how do I craft a prompt and then bring in the context the model needs so it can do a good job for me?' Ultimately, what these models are trying to do 'is just infer from your utterances what they think you mean,' Jones explained. They need to 'figure out where in latent space they can go and get a reasonable pattern match, do some searching across the web. In the case of an inference model, do a lot of that iteratively they can figure out what's best, and then put together something.' Jones speculated that within the next few years, the models will gain so much experience that sharp prompting skills may not be as necessary. But in the meantime, he provides three considerations for developing an effective prompt:
Forbes
31-05-2025
- Business
- Forbes
Building An ‘AI-First' Culture: What Does That Even Mean?
AI-first means people-first Lately, there's been no shortage of talk of managing organizations around 'AI-first' approaches, meaning managers would consider whether AI could do a job, or set of tasks, before humans are brought in. But AI-first goes deeper than that, suggesting an organization's entire culture can be redesigned to incorporate the broad intelligence solutions that AI platforms and tools can offer. How would such an organization look, and is this something a decades-old company could pull off? Cisco Systems, which was founded more than 40 years ago, has been undertaking such a transformation over the past three years across all aspects of its business. This includes transforming 'the way that we build product, the way that our products get used by customers, the way that we actually get jobs done within the company,' said Jeetu Patel, president and chief product officer for Cisco. Even in what is one of the most technology-savvy companies in the world, such an effort will meet resistance, Patel recently explained on a recent episode of Michael Krigsman's CXOTalk. 'It's a cultural shift. It's actually fraught with a level of skepticism." Still, 'If you looked at us a year and a half, two years ago, no one would have really said that Cisco is AI first,' he said. An issue being encountered is 'people have actually been afraid of AI, saying, 'Hey, AI's going to take my job, so I'm not going to go out and use it,' Patel added. 'I actually find that it's less about AI taking your job, it's more about someone that uses AI better than you in their jobs is probably the one who's going to take your job.' Ultimately, 'the dexterity that you need to show in the way in which you do everything with AI is going to be pretty important,' he said. 'We've always felt like there's only going to be two kinds of companies in the world. Ones that are dexterous with the use of AI, and others who really struggle for relevance.' There are three key considerations in building an AI-first culture, Patel explained: Customers are also part of the transformation to an AI-first culture. 'One area that we struggle with is that the pace and rate of change is so fast that communicating that to our customers and having them digest that change is a challenge,' said Patel. 'I don't think we've cracked the code on that.' Customers have a view of Cisco from more than three years back, 'and frankly, it's an entirely different company than what it used to be three years ago,' he added. 'I feel like there's so much coming at people all the time that you have to make sure that you distill it down to a few things that make sense.' For example, AI is accelerating the company's responses to support tickets. It also is helping to reduce overhead costs. On the sales side, AI will help accelerate sales meetings, as well as legal and accounting processes involved with the sale. 'All of those things will have AI as a pretty critical component of it, and I do feel like the sales process is going to change quite materially over the course of the next few years. And you will never be in this position where you go completely blind and unprepared into a conversation because AI can get you prepared within a very, very compressed amount of time on what needs to happen.' What's important now for the new generation that's entering the workforce – as well as existing workers – is not to operate out of fear of AI, Patel advised. 'You have to operate from a place of looking at the possibilities and looking at the opportunities that actually can be unlocked. I would urge people to just have a very different kind of mental model, which is, there's nothing that should stop us from actually being curious about how we might be able to use AI, and this technology is going to get easier and easier and easier, where no longer is technical dexterity going to be an impediment.'
Forbes
01-05-2025
- Business
- Forbes
Could AI Rescue Companies In An Economic Downturn? Think Twice
AI economics: threat or opportunity? getty Could ongoing economic turbulence – or a recession – result in even more widespread adoption of AI and AI agents as a cost-saving move? There's a risk to business leaders thinking in the event of sour times they could simply lay off workers and swap in AI systems to replace them – such a choice could be extremely counter-productive. Cruelty of layoffs aside, the vast majority of businesses simply aren't ready to simply adopt AI on a wholesale basis, said Phil Fersht, CEO of HFS Research, in a recent interview on Michael Krigman's CXO Talk. The problem is, Fersht related, is most companies still don't have the experience, skills, and knowledge to implement AI in a smart and scalable way. Many companies are now 'very aware of what they can do, but they're still yet to have that burning-platform trigger to go do it,' he continued. "My concern is if we plunge into a deep recession, you're gonna see some organizations literally come out and say, 'We're just gonna start relying a lot more on AI, and we're gonna let people go.'" The risk with such a 'weaponized AI,' Fersht explained, is 'how advanced they are with embracing this. Are they prepared to do anything?' In working with many enterprises, Fersht found 'they're not doing a lot.' Only about 15% of executives feel they are truly ready to adopt AI in a positive and intelligent way, his firm has found through surveys. They have 'fairly integrated views of where they're going; they have a strong culture of support, and they're embracing this.' The majority of companies, he related, 'are either still figuring it out or they're not on the path. And this is just going to become more pronounced as we go through the next few months of macroeconomic turbulence.' Fersht emphasized that executives aren't consciously thinking about getting rid of people. 'They're actually thinking about, 'How do we break from the past?' Companies have much data, they don't know what to do with it. They can't join it up. They can't make decisions on it.' Rather, the thinking is breaking away from legacy systems, to start to really build out the new with agentic systems. There is an 'AI-first mindset' that is shaping future hiring and skill-acquisition plans. Companies 'are now insisting before you hire any new staff, you need to show that this work can't be done by AI. We've reached that point quite quickly." The impact on offshoring – previously seen as a way to avoid new hiring – is already tangible, he continued. 'Now, C-suite directives are, 'can we not do this with AI?' The whole point of agents is really this ability for companies to grow and scale in a way that you don't need to keep adding more and more people. You do a lot more with the people you have, and I think that's the positive way to think about this.' For people in business, there's both threat and opportunity. 'If you're in a job where you can be effectively replicated and replaced, you kind of know that, and you need to figure out, 'how do I continue to add value in an enterprise?'' The value for humans comes from collaboration, people skills, and empathy. 'If you can become a great person everybody likes to work with, and you become very thoughtful about what you do, and you start to collaborate beyond your existing area, you become very valuable to your company.'
