BBCNEWS — AI Decoded
20260808 133000 UTC
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[00:00:00] Make the connection. MUSIC
[00:00:09] MUSIC
[00:00:12] Welcome. You're with BBC News.
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[00:01:03] This is BBC News. MUSIC
[00:01:15] MUSIC Now Now on BBC News. Join our I decoded
[00:01:18] decoded team as we unpack, explore explore and take a deep dive into
[00:01:21] into the world of artificial intelligence.
[00:01:39] Hello, welcome to AI Decoded.
[00:01:40] We talk about AI and how it's going
[00:01:43] going to change our jobs constantly. constantly. But here's the honest
[00:01:47] honest truth most of us don't yet yet fully understand what that will
[00:01:51] will mean to the future of work.
[00:01:53] work. We know it's coming. Perhaps
[00:01:56] Perhaps faster than many of us expected.
[00:01:59] expected. The question I ask my clients
[00:02:02] clients is if an AI could take over
[00:02:05] over all of your team's tasks, who
[00:02:08] who would you keep and why? That
[00:02:12] That question is strategic and the
[00:02:14] the answer matters to me, not just just intellectually, but because
[00:02:18] because I have two daughters at home.
[00:02:21] home. They're five and nine. And
[00:02:22] And right now, as you can see, they
[00:02:25] they feel invincible. But I keep keep wondering what is the world
[00:02:29] world of work they will step into?
[00:02:32] into? We need to build a future where
[00:02:34] where humans matter more, not less.
[00:02:38] less. It's not about whether AI replaces
[00:02:41] replaces us. Of course there will
[00:02:43] will be some jobs that do go, but but how we redesign the workplace
[00:02:46] workplace around it. What does that that actually look like in practice?
[00:02:50] practice? In this programme, we're we're going to try and make sense sense of all that we've brought together
[00:02:54] together three guests who have talked talked about this subject more than
[00:02:57] than most. Bernard Ma writes about about AI and business for Forbes.
[00:03:01] Forbes. He's advised some of the the world's largest organisations
[00:03:04] organisations on how to use AI effectively.
[00:03:07] effectively. Ella Hoffman is an associate
[00:03:10] associate professor at the Free University University in Amsterdam. She spent
[00:03:13] spent the last two years studying studying what happens when employees
[00:03:16] employees start using AI tools at at work. And of course, our very
[00:03:19] very own colleague Prilocalne, CEO
[00:03:21] CEO of Century Tech, the AI education education company. Welcome to the
[00:03:25] the three of you. Thanks for being being with us. Priya, I'm going to
[00:03:28] to start with you. Let me put to to you the premise of our programme.
[00:03:31] programme. OK. How do we create an
[00:03:33] an AI workplace of the future in
[00:03:36] in which humans still matter? I love.
[00:03:38] love. The fact that. You start our. our. Programme with the most profound
[00:03:42] profound question. Christian. Just Just that's what we're all asking.
[00:03:45] asking. No warming up. OK, well,
[00:03:48] well, look, the way in which we do do this is I think we have to think
[00:03:52] think about look, if everyone has has AI across the board, it is going
[00:03:56] going to be the humans that create create the difference. And so how
[00:03:59] how are we now going to upskill the
[00:04:03] the people within the workforce so
[00:04:04] so that they can thrive in that environment? environment? Now if you think about
[00:04:08] about all of the changes that are are taking place and the companies
[00:04:10] companies that are, you know, seeing seeing changes with AI, they're reporting
[00:04:15] reporting positive changes. What's What's really interesting is those
[00:04:17] those changes take place with tasks, tasks, you know, your role and your
[00:04:20] your job. If you think about it, it, it's about repetitive tasks,
[00:04:24] tasks, automating tasks. But what what it's struggling with still is
[00:04:27] is that sort of strategic direction direction and that judgement. And And so I think companies need to
[00:04:31] to be thinking about, you know, how how much are we actually investing
[00:04:35] investing in innovation? How much much are we investing in our people
[00:04:37] people to have the time to be skilled skilled in this area so that we can can then differentiate ourselves
[00:04:41] ourselves and humans will then still still be able to flourish in an age
[00:04:44] age where we have this sort of AI AI augmented workplace. Bernard,
[00:04:48] Bernard, let's fast forward to 2030,
[00:04:50] 2030, the beginning of the next decade. decade. How widely do you think AI
[00:04:54] AI will have changed the average average job? Help us understand that
[00:04:57] that first of all, and how you think think it will reshape the average
[00:05:00] average workplace. Yes. So this is
[00:05:02] is what I try to help companies understand.
[00:05:04] understand. And what I'm seeing is
[00:05:06] is that AI will completely transform
[00:05:09] transform our jobs. For me, AI is
[00:05:12] is a superpower that will all we we can all use. It's almost like
[00:05:15] like having a genie on your shoulder shoulder that allows you to do pretty
[00:05:19] pretty much anything, or at least least a lot of tasks that previously
[00:05:22] previously you needed someone else else to help you with. And for me,
[00:05:26] me, there are three types of AI that
[00:05:29] that will transform our work in the the future. The first one is generative
[00:05:32] generative AI. So we are used to
[00:05:34] to chatbots like ChatGPT and Claude
[00:05:37] Claude and Gemini, and we use them them to answer questions. And if
[00:05:40] if you think about a financial adviser,
[00:05:42] adviser, for example, they can now
[00:05:44] now offload the data analysis to
[00:05:46] to the chatbot. They can draft reports
[00:05:49] reports and they can hopefully spend
[00:05:50] spend a bit more time building interpersonal
[00:05:54] interpersonal relationships with with their clients. Then we have
[00:05:56] have a genetic AI. So the AI that
[00:05:58] that can do things that can operate
[00:06:02] operate software that can look things things up on the internet, fill out
[00:06:05] out forms and. Do those repetitive repetitive jobs that Priya's talking
[00:06:08] talking about, the jobs that you you do again and again and again again in the same format. Exactly.
[00:06:12] Exactly. And IBM is a good example. example. They have automated a lot
[00:06:15] lot of their HR tasks, all the repetitive
[00:06:18] repetitive tasks. So if you work work in HR today, you actually want
[00:06:21] want to think about how AI is transforming
[00:06:24] transforming the company, how we
[00:06:26] we need to train retrain our people, people, rethink our jobs. And actually
[00:06:29] actually you get bogged down by answering
[00:06:32] answering the same questions again
[00:06:34] again about pension contributions,
[00:06:37] contributions, about vacation time time and so on. So we can give AI
[00:06:40] AI those jobs and these AI's can can now fill out forms. They can
[00:06:44] can search data for you. And then
[00:06:47] then we have physical AI. And this this is another component. So so
[00:06:50] so far we've talked about all the the cognitive work. Physical robots
[00:06:54] robots are getting better as well. well. They're starting to understand understand the physical world much
[00:06:57] much better. We now have the emergence
[00:07:00] emergence of world models. So humanoid humanoid robots for example, they
[00:07:03] they can be trained by simply watching
[00:07:06] watching videos of how we do things things on YouTube. They can learn
[00:07:09] learn from that. Right. And, and,
[00:07:13] and, and all of those will mean that that tasks will change. Right. But
[00:07:17] But you said that if we were to look look at where we are right now, it's
[00:07:21] it's not necessarily that AI is replacing
[00:07:22] replacing the jobs instantly. It's
[00:07:25] It's that AI is hollowing them out.
[00:07:27] out. In what sense? So what AI does,
[00:07:30] does, it will perform the tasks that
[00:07:32] that we actually don't necessarily necessarily like. So one part of
[00:07:36] of the job, the repetitive tasks tasks is I work with a company and
[00:07:39] and their chief AI officer actually actually went into the organisation
[00:07:43] organisation to ask everyone, what what are the things you don't like
[00:07:46] like in your job that you would love
[00:07:47] love the AI to do for you? Because Because we in our jobs, we're not
[00:07:51] not running out of things to do. do. They're not. We will never run
[00:07:55] run out of questions and problems problems to solve. What we get distracted
[00:07:58] distracted by day to day is all the
[00:08:00] the things, the form filling the,
[00:08:04] the, the things that take up our our time. So in a positive sense.
[00:08:07] sense. There could be a freedom that that this creates to focus on more
[00:08:10] more creative parts of the business. business. Exactly what I believe
[00:08:13] believe is that it will make our
[00:08:15] our work more human and actually actually more enjoyable because we
[00:08:18] we don't get distracted by all the the things that we almost shouldn't
[00:08:21] shouldn't waste our amazing human human potential to work on. Well,
[00:08:24] Well, that feels like a good place
[00:08:26] place to bring in Ella because your
[00:08:29] your research Ella, has taken you you to very different companies in
[00:08:32] in the Netherlands and it reveals reveals that employees are already
[00:08:36] already using these large language language models in their work in
[00:08:39] in ways that managers can't easily easily see. In what ways are they
[00:08:43] they using them? Yeah. So we've been
[00:08:46] been studying ChatGPT since 2022.
[00:08:49] 2022. So just after it came out, out, we thought we have to look at at this and we started talking to
[00:08:53] to people already then about how how are you using this in your work?
[00:08:56] work? And we found out that they they were playing around with it,
[00:08:59] it, first of all at home using it it for the silly kind of things that
[00:09:02] that maybe you're familiar with, with, you know, make me look a different
[00:09:04] different way. Write a poem, etc etc but then they started figuring
[00:09:07] figuring out, hey, this could be be useful for work. And so unbeknown
[00:09:11] unbeknown to managers, without kind kind of approval, they started to
[00:09:14] to use this technology for brainstorming brainstorming for search as a kind
[00:09:18] kind of replacement for Google, for for structuring information, polishing
[00:09:22] polishing text, all these different different ways that they were just
[00:09:25] just letting it creep into their their everyday work. And it can sound
[00:09:28] sound kind of innocent in these little
[00:09:29] little parts, but it has bigger consequences.
[00:09:32] consequences. Well like what? Well, Well, what we found, for example,
[00:09:35] example, in our interviews is people people were so happy to not have
[00:09:39] have to bother their colleagues with with questions. So, you know, previously
[00:09:42] previously I'd have to go and ask ask my manager or I'd have to bring bring in my business partner who
[00:09:46] who knows this framework, but now now I don't need them. I can just
[00:09:49] just ask ChatGPT. And so they were were happy to not have to find out
[00:09:53] out where their employees are or or their colleagues. You know how how it is with hybrid working. You
[00:09:57] You never quite sure who's where.
[00:09:59] where. So easy, convenient. Ask ChatGPT.
[00:10:03] ChatGPT. So that organic self-learning self-learning on the job, the exchange
[00:10:05] exchange of information between colleagues colleagues which is creative within
[00:10:09] within an industry. You worry that that that element is being lost.
[00:10:12] lost. Yeah. So we see this as a real
[00:10:15] real challenge for particularly managers managers who might not realise that
[00:10:18] that this is happening, this erosion erosion of the social ties between
[00:10:22] between people. And that's a lot lot of what organisations are for. for. We're meant to share knowledge
[00:10:26] knowledge and learn from one another
[00:10:28] another and also check the quality
[00:10:30] quality and accuracy of what's coming coming out of these interactions. interactions. You see what else is
[00:10:34] is describing there, Priya, is that that this is already happening organically.
[00:10:38] organically. One employee at a time. time. And if we're to start thinking thinking strategically about this
[00:10:42] this rather than asking the question, question, who is it going to replace replace my job? This is the sort
[00:10:45] sort of thing that CEOs and managers managers are going to have to get get a grip of. How is it working
[00:10:49] working to the benefit of the company, company, and are we all using it
[00:10:52] it in the same way, in the same direction? direction? Yeah. Well, I think in
[00:10:54] in the last year I've certainly seen seen a lot of businesses around the
[00:10:58] the world talking about having some some form of plan, some form, some
[00:11:01] some form of policy and a strategy strategy because it's not just the
[00:11:04] the sorts of ways of just using something something to maybe brainstorm instead
[00:11:08] instead of going to a colleague, colleague, you know, you can't be be putting personal data or commercially
[00:11:11] commercially sensitive data into into tools that you have a personal
[00:11:14] personal account for at home. And
[00:11:16] And so there, I think most CEOs over over the last 12 to 18 months have
[00:11:20] have said, right, this AI thing, thing, we have to do something. And
[00:11:23] And what was so interesting is that that because of this programme, actually
[00:11:26] actually quite a few people will will reach out on LinkedIn or write
[00:11:29] write to me or meet me and say, and
[00:11:32] and the CEOs of like 30 to 50 companies, companies, right? So they'll come
[00:11:35] come up to you and they'll say, Priya,
[00:11:37] Priya, we're using AI and because
[00:11:39] because I'm nice, right? I'll smile smile and be really encouraging and
[00:11:42] and say, that's amazing. So in the the back of my head, I'm just thinking, thinking, what are you talking about?
[00:11:46] about? And actually when you dig dig deeper, what you find is they've
[00:11:50] they've bought co-pilot licences licences for all of their staff and
[00:11:53] and they're just finishing off their
[00:11:55] their emails using AI. Whereas I
[00:11:58] I think what we'll start to see because, because, you know, people are savvy
[00:12:01] savvy to this now how come and we we covered this on this programme, programme, Christian very deeply.
[00:12:05] deeply. The MIT report that said said that, you know, 95% of organisations
[00:12:09] organisations aren't getting a return return on investment from AI. And And then there was another report
[00:12:12] report by McKinsey. Actually 1% of of companies are getting return on
[00:12:15] on investment companies are now desperate. desperate. They're saying, how can
[00:12:18] can I be part of the 1%? OK, two
[00:12:21] two fast thoughts before we talk talk about how we adapt to this.
[00:12:24] this. If you want an example of what
[00:12:26] what Priya's talking about there, there, Bernard, the idea that CEOs
[00:12:30] CEOs think they're using it when when really they're not. You only
[00:12:33] only need to go and look at some
[00:12:34] some of the job ads currently on
[00:12:37] on LinkedIn. 100%. And what we're
[00:12:41] we're still seeing is jobs. The job
[00:12:43] job descriptions almost of the last last century as how we've always
[00:12:46] always done jobs instead of thinking,
[00:12:49] thinking, how will AI truly change
[00:12:52] change our jobs? I think you talked
[00:12:55] talked about coding. And coding.
[00:12:58] coding. Now Anthropic's Claude, can
[00:13:00] can perform. They use they use code
[00:13:04] code to automate 80% of their coding
[00:13:05] coding tasks. So the jobs need to
[00:13:09] to change. And what we need to do do is we need to fundamentally rethink
[00:13:12] rethink our jobs and how we as humans
[00:13:15] humans bring the best of what we
[00:13:17] we have is the strategic thinking thinking is the creativity and creative
[00:13:20] creative problem solving, emotional emotional intelligence building connections
[00:13:24] connections with other people. These These things AI can't do and we can
[00:13:28] can bring. And what humans actually actually need to do is they need
[00:13:30] need to be able to delegate effectively
[00:13:33] effectively to the AI. And what I'm
[00:13:35] I'm seeing is that jobs are being being elevated and almost front line
[00:13:39] line employees are now becoming managers
[00:13:44] managers of a set of AI tools that
[00:13:46] that will work for them. And they
[00:13:48] they need to oversee. His employees
[00:13:51] employees almost. Listen, I'm I want
[00:13:54] want to turn to how then we train train for the jobs of the future.
[00:13:57] future. I want to play you this clip. clip. This is the CEO of BlackRock
[00:14:01] BlackRock massive company Larry Fink Fink talking to Simon Jack, our economics
[00:14:04] economics editor this week. Some Some of the things that he says might
[00:14:07] might just surprise you. AI is going going to create enormous amount of
[00:14:11] of jobs. Most people are not focusing focusing on part of the letter that
[00:14:15] that I wrote about how many jobs jobs is going to be creating related
[00:14:17] related to electricians and welders welders and plumbers. So we should
[00:14:20] should be telling our kids to be be electricians rather than lawyers
[00:14:23] lawyers or fund managers. If you
[00:14:27] you think about how the average worker
[00:14:31] worker has been portrayed on television
[00:14:34] television generally the average average plumber had their overalls
[00:14:38] overalls or their pants, you know,
[00:14:41] know, hanging below their waistline. waistline. Overweight. We need to
[00:14:45] to embrace that. Those type of jobs
[00:14:47] jobs are just as good for many people
[00:14:51] people post World War II. We built
[00:14:53] built a foundation of education and
[00:14:56] and we said to all the young people, people, go to college, go to college,
[00:14:59] college, go to college and we probably
[00:15:03] probably overdid it. And so many many people who probably should not
[00:15:06] not have gone into banking or media
[00:15:10] media or law probably should have
[00:15:11] have been a great worker with their
[00:15:14] their hands. And we need to now rebalance
[00:15:19] rebalance that approach. So Ella,
[00:15:23] Ella, he doesn't think that people people should spend an inordinate inordinate amounts, inordinate amounts
[00:15:26] amounts of money on a university university degree. Right. But we've
[00:15:30] we've already established that there's
[00:15:32] there's not a job that is AI proof.
[00:15:35] proof. That's a myth. So how do you
[00:15:38] you skill for a job and a future future that we can't yet define?
[00:15:42] define? Yeah, it's a great question. question. It's one that we bring bring to our students. So I teach
[00:15:45] teach a course within our International International Business Administration Administration programme. And in
[00:15:49] in this course, we specifically ask
[00:15:51] ask who am I in the age of AI? And And this is really something that
[00:15:55] that students also need to be engaged engaged in this conversation. There's
[00:15:58] There's no point in saying don't don't use AI. It's a part of their
[00:16:02] their world. What we need to help help them think about. It's similar
[00:16:04] similar to what Bernard was saying saying is, what do I bring to the
[00:16:07] the table now? And these students students are working with social
[00:16:10] social enterprises. They're solving solving real world problems. Alongside
[00:16:14] Alongside that, they're reflecting reflecting on this question while
[00:16:16] while also fine tuning an AI model model to understand how the technical
[00:16:20] technical side of things works. And And to me, that's what a university
[00:16:23] university education is about. It's It's about having critical conversations,
[00:16:26] conversations, learning the latest latest research on this, but also also building your own opinion and
[00:16:30] and your own perspective and your
[00:16:32] your own path that also suits your
[00:16:34] your ideals towards what the future future should be. This is your world,
[00:16:38] world, I think Priya as well at Century
[00:16:40] Century Tech you're training people
[00:16:42] people for jobs that might not yet
[00:16:44] yet have been defined or even technology
[00:16:47] technology that's not there. So for for parents and for young people
[00:16:50] people who might be watching how how should they think about their
[00:16:54] their education and what they do? do? Yeah, I think AI is definitely
[00:16:57] definitely shine a spotlight on education
[00:16:59] education and the flaws in education education and the fact that if we
[00:17:02] we even talk about school, education, education, college education, how
[00:17:06] how we actually measure what we measure measure in education. So it's the
[00:17:09] the assessment process that actually actually I think is the problem,
[00:17:11] problem, right? People want to train train children and have them study
[00:17:15] study and learn curricula. That's That's very much built towards an
[00:17:18] an end, sort of high stakes assessment. assessment. Now it used to be we we take that for granted. We go to
[00:17:22] to university or we go to college college to further education, college,
[00:17:24] college, school. And it's a currency, currency, right? That's certificate certificate you end up with those
[00:17:28] those grades. That's a currency actually actually means something in the real
[00:17:31] real world. And what Larry Fink is is saying is, you know, does it what
[00:17:33] what you need to do is think about about education and what it's for,
[00:17:36] for, right? We need to give foundational foundational knowledge. I really really am a big believer in that.
[00:17:40] that. And I just don't think we can can just Google everything or search
[00:17:43] search for everything because we we won't be able to develop those those crucial skills and build judgement.
[00:17:47] judgement. So foundational. So that's that's the colour of life. Yeah.
[00:17:50] Yeah. I mean we exist. So we need need that. Foundational knowledge
[00:17:53] knowledge is number one. Number two two then applied right? How does
[00:17:56] does this actually work in the real real world? How do I solve problems? problems? And then the third that
[00:18:00] that I've sort of I like to call
[00:18:03] call learning agility, this ability ability to learn how to learn so
[00:18:06] so that we don't end up with generations, generations, you know, cohorts of
[00:18:10] of students leaving formal education education then thinking, hang on,
[00:18:13] on, that's not what you said it would would be. It's not a conveyor belt
[00:18:16] belt to a job. Now what do I do?
[00:18:18] do? But what about what about Bernard?
[00:18:21] Bernard? Those you'll hear a lot lot of people say, oh, well, my child
[00:18:25] child is studying as an accountant accountant and they're being trained
[00:18:28] trained on spreadsheets and profit profit and loss accounts. But all
[00:18:30] all of that can now be done by AI. AI. But that is the stepping stone
[00:18:34] stone to the next level within the the company. What if we remove the
[00:18:37] the jobs, the lower level jobs that
[00:18:41] that elevators higher up in the company? company? That's a challenge. I mean,
[00:18:44] mean, I have three children, they're they're all just getting ready for for university or in university.
[00:18:48] university. This is something I think think about every single day. I completely
[00:18:52] completely agree with what you just just said. I wrote a book called called Future Skills in which I look
[00:18:56] look at the 20 skills we will need
[00:18:58] need for the future. Three of them them are technically related, so
[00:19:01] so we need to understand AI and what what it can do beyond. This is what
[00:19:05] what makes us truly human is our
[00:19:07] our empathy is our critical thinking
[00:19:10] thinking is our strategic problem problem solving and is our ability
[00:19:13] ability to learn and continuously continuously relearn these things
[00:19:17] things are absolutely vital. So what what we need is companies need to
[00:19:21] to understand that they need employees employees in the future. So they
[00:19:24] they need to create tracks into these
[00:19:27] these organisations that allow young young people to come in. At the moment,
[00:19:30] moment, I almost feel that there's
[00:19:32] there's a wrong emphasis. The emphasis
[00:19:34] emphasis is on driving efficiency efficiency and cutting costs and
[00:19:38] and that's easy. I can look at my my existing processes and what I
[00:19:42] I do and think, OK, AI can almost almost do the jobs of most junior
[00:19:45] junior roles, so I simply cut those those roles. So if I cut those roles,
[00:19:49] roles, I also cut off my future employees
[00:19:52] employees and also the ability to
[00:19:54] to completely rethink how you work work as an organisation. And this
[00:19:58] this is something that I see very very little happening in the real
[00:20:01] real world. I see this in a few AI AI native companies, but most companies
[00:20:05] companies don't get that. And there's there's a really good example of
[00:20:07] of this, Ella, in a very successful
[00:20:10] successful company called Klarna, Klarna, people will be familiar with
[00:20:12] with it. The CEO, they're replaced
[00:20:15] replaced around 700 jobs with a chatbot chatbot powered by OpenAI, massive
[00:20:19] massive cost cutting exercise. And And then that was sometime last year.
[00:20:22] year. And then by December, the company company announced a huge recruitment
[00:20:26] recruitment drive because the work work delivered by AI was of a lower
[00:20:29] lower quality but more importantly, importantly, customers wanted a human
[00:20:32] human in the loop. They wanted that that empathy and that foundational
[00:20:36] foundational knowledge that Priya Priya talked about. That makes so
[00:20:39] so much sense. And these sorts of of moves are motivated by all sorts
[00:20:42] sorts of different incentives in
[00:20:44] in the end. But we do need to understand
[00:20:47] understand that the AI hype can often often lead to decisions that are
[00:20:50] are regretted, and that's why it's it's so important to have a good good technical understanding of what
[00:20:53] what can this stuff actually do. do. And as Bernard saying, really
[00:20:56] really think about the future. We're We're hearing a lot of talk at the
[00:20:59] the moment about diamond shaped organisations. organisations. So you've got a few
[00:21:02] few people at the top, a kind of of heavy middle of experienced workers.
[00:21:06] workers. And then we don't need many many entry level positions. But what
[00:21:10] what does that mean in terms of a a funnel? This doesn't make a whole
[00:21:13] whole lot of sense. If you're thinking thinking about how you're going to to train newcomers. Effectively.
[00:21:16] Effectively. What then? Ella, what
[00:21:18] what if you're a CEO watching this this and you're toying with this
[00:21:22] this idea of cutting your lower level. level. What would be your advice
[00:21:25] advice to them? I think it depends depends on the strategy. Are you
[00:21:28] you looking for a long-term proposition proposition or is this something something that's only oriented towards
[00:21:32] towards the short-term? So those those temporal horizons make all all the difference. And if you're
[00:21:36] you're really looking to grow a culture culture and a company that's going going to remain distinctive, then
[00:21:39] then you do need to pay attention attention to those entry level jobs.
[00:21:43] jobs. Not just that though. How are are entry level people actually going
[00:21:46] going to meaningfully interact with with your more senior level management
[00:21:49] management when no one wants to bother bother each other? Because we've we've all been told we need to be
[00:21:53] be busy and efficient for a long long time, then people are not going going to reach out with their silly
[00:21:56] silly questions. But that's how you you actually build relationships, relationships, find mentors, figure
[00:22:00] figure out how things work. Which Which brings us to an audience question.
[00:22:03] question. I love our audience question. question. They do emails. Keep coming,
[00:22:06] coming, keep coming. So this one
[00:22:08] one is from Saeed al-Marri in Dubai
[00:22:11] Dubai who runs operations across across two organisations and I'm
[00:22:14] I'm paraphrasing his email Ella, Ella, because it's quite a long one,
[00:22:17] one, but essentially he says AI hasn't hasn't just made our team faster.
[00:22:21] faster. It's allowed a small team team to produce the kind of work
[00:22:24] work that they used to require external external agencies and consultants
[00:22:28] consultants to do. So they're bringing bringing stuff in-house that they they would have had to delegate.
[00:22:31] delegate. And he calls that operating
[00:22:34] operating leverage. So there is a
[00:22:36] a man who is fundamentally Bernard Bernard reimagined how his organisation
[00:22:40] organisation works and how AI is is structured. Within it are the
[00:22:43] the CEOs you speak to day to day
[00:22:47] day doing that? No, I think some
[00:22:49] some of them do. And those will be
[00:22:52] be the successful CEOs of the future.
[00:22:56] future. And I think we need to fundamentally
[00:22:59] fundamentally rethink how we do work work and how humans fit into that
[00:23:02] that and what an individual can do. do. I even see this in my own in
[00:23:06] in my own work. So I used to spend
[00:23:08] spend the first hour of my day reading reading my emails. I get pictures
[00:23:11] pictures every day from tech companies companies telling me about all the
[00:23:14] the latest developments and all the
[00:23:16] the case studies, and I get newsletters
[00:23:18] newsletters and I try to sift through through this to understand what I
[00:23:22] I need to focus on. Now I have an
[00:23:24] an AI to do this for me. The AI will
[00:23:26] will read it. The AI already knows knows what I know and then turns
[00:23:30] turns this into a podcast for me. me. So when I go get up in the morning,
[00:23:34] morning, I walk my dog, listen to to the podcast, and then I can come
[00:23:37] come back to my office with a good
[00:23:40] good idea of what I want to do more
[00:23:42] more research on. So I then set another
[00:23:44] another AI AI agent off to do this
[00:23:46] this research for me. While I can
[00:23:49] can think strategically when I can can think about how do I develop
[00:23:53] develop relationships with some of
[00:23:54] of the CEOs I'm working with that
[00:23:58] that is really empowering. And in in the past I would have had to employ
[00:24:01] employ a number of people to do this
[00:24:02] this for me. But in that example
[00:24:05] example that that Saeed has just
[00:24:06] just given is Ella. I mean, his AI
[00:24:11] AI as commendable as it is, is replacing
[00:24:14] replacing the jobs that consultants consultants and agencies would have
[00:24:16] have done so at some position in in the food chain. We have to be
[00:24:20] be pretty blunt about this. There There are jobs going. One thing in
[00:24:24] in the interim is also to look at
[00:24:27] at this and go, does this have a a long-term value proposition? So
[00:24:29] So kind of like the Klarna example.
[00:24:31] example. And here I mean using AI
[00:24:35] AI can kind of cause us to overestimate
[00:24:37] overestimate what we're able to achieve achieve sometimes to our own expertise.
[00:24:40] expertise. Something might look like like it's good quality code or a
[00:24:44] a good quality image or a good quality
[00:24:47] quality website. Will that actually
[00:24:49] actually hold up to the world? And
[00:24:51] And as you mentioned, AI as a genie
[00:24:54] genie and genies are tricksters. tricksters. We call it a spirited
[00:24:58] spirited technology. It comes at at you with all sorts of little quirks
[00:25:01] quirks and random, you know, the the extra fingers these things can
[00:25:04] can get solved, but it takes a trained trained eye to actually detect the
[00:25:08] the spirited quirks of AI. And we
[00:25:10] we need to maintain experts at every every level of this process. So I'm
[00:25:13] I'm happy if people are making it it work for themselves, but it shouldn't shouldn't be seen as something that
[00:25:17] that can be easily replicated. OK, OK, well, since we're talking about
[00:25:21] about efficiency and we're near the the end of the programme, I thought thought I would show you this just
[00:25:25] just to lighten the mood a little, little, which I spotted on X this
[00:25:28] this week. It does appear to me to to be the most important development
[00:25:31] development so far, or the development development with the furthest reaching
[00:25:34] reaching consequences. This is from from a trade show in Switzerland.
[00:25:38] Switzerland. It's a robot that autonomously
[00:25:40] autonomously pours, cooks and folds
[00:25:43] folds the perfect crepe it does.
[00:25:46] does. It's Swiss, it's not French. French. In fact, I'm surprised the
[00:25:49] the French have not yet called an an emergency summit on this. Whether
[00:25:52] Whether it is actually AI or whether whether it's just clever automation,
[00:25:56] automation, I'm not so sure. But
[00:25:57] But it has made me wonder. Priya.
[00:26:00] Priya. You're talking to the person
[00:26:02] person who lives next to you, has
[00:26:05] has befriended and like serves coffee coffee every day to the people who who run the famous crepe stand in
[00:26:09] in London. I draw a line. I'm officially
[00:26:11] officially drawing a line on crepe. crepe. Is there nothing sacrosanct?
[00:26:15] sacrosanct? No. There is. I want want my crepes made by Veronica at
[00:26:19] at the Hampstead Crepes. And I don't don't want Bernabeu. I don't want
[00:26:22] want Bernard's improving bot improving
[00:26:25] improving robot flipping my crepes.
[00:26:27] crepes. I'd love to see a robot peeling peeling off the ceiling. Yeah, yeah,
[00:26:30] yeah, best of luck with that. That's That's where the humour comes in. in. Let me do that for you. Yeah.
[00:26:34] Yeah. If Jo Bailey. Gets her way, way, he'll be able to spot the crepe
[00:26:37] crepe and it will go and scrub it it off for you. Spatial intelligence.
[00:26:40] intelligence. OK, well, next week week on AI Decoded, we're going to
[00:26:44] to explore actually one area of work work that is changing how artificial
[00:26:47] artificial intelligence is transforming transforming the world of advertising
[00:26:51] advertising from big shared campaigns campaigns to messages tailored specifically
[00:26:54] specifically for you. You might have have some thoughts on that. So if
[00:26:57] if you do want to take part, please
[00:26:58] please email us at AI decoded@bbc.co.uk
[00:27:02] decoded@bbc.co.uk and maybe we can can incorporate some of those thoughts
[00:27:05] thoughts in the programme. Thank Thank you very much to Bernard. Thank
[00:27:08] Thank you Ella. Thank you Priya for for your thoughts this week. That's
[00:27:12] That's all we have time for. Just Just a reminder that you can watch
[00:27:14] watch this episode and all our back back catalogue on the AI Decoded
[00:27:18] Decoded YouTube playlist. Have a
[00:27:19] a look at that. And also on the BBC BBC iPlayer. Thanks very much for
[00:27:23] for watching. We'll see you next
[00:27:26] next week.
[00:29:39] To find out why. Join me Laura Bicker
[00:29:42] Bicker here on BBC News. MUSIC
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