0:00:03 > 0:00:07What if the way we understand the world is wrong?
0:00:08 > 0:00:12What if it's not politicians and world events that shape our lives,
0:00:12 > 0:00:14but business deals?
0:00:14 > 0:00:17It's natural for people to fight against change,
0:00:17 > 0:00:21but change is coming, and we need to adapt to it.
0:00:22 > 0:00:27Deals made in secret, high up in a boardroom, over a drink,
0:00:27 > 0:00:29or in a car in the dead of night.
0:00:29 > 0:00:32We were trying to protect people from excess medicine.
0:00:32 > 0:00:34- You couldn't. - Yeah, we failed.
0:00:35 > 0:00:39Our every waking hour has been transformed by these deals,
0:00:39 > 0:00:43reprogramming us to think and behave in a different way.
0:00:43 > 0:00:46While we're competing with these large monolithic banks that are very
0:00:46 > 0:00:48bureaucratic, we can run circles around them all day.
0:00:48 > 0:00:51This is the story of these deals.
0:00:51 > 0:00:53These ideas were ahead of their time.
0:00:53 > 0:00:56Online payments can change the world.
0:00:56 > 0:01:01Who made them, why, and what do they mean for our future?
0:01:04 > 0:01:08In this series, I'm going to look at three key aspects of our lives,
0:01:08 > 0:01:13and how they've been revolutionised by these deals.
0:01:13 > 0:01:16Who wants to kill cash, and why?
0:01:16 > 0:01:19Our vision is to have a cashless society.
0:01:19 > 0:01:23Card payments are the best way to pay and the best way to be paid.
0:01:23 > 0:01:26Why do we medicate every aspect of our lives?
0:01:26 > 0:01:30This notion of the pursuit of happiness, it's not realistic!
0:01:30 > 0:01:33It gets all of us to be on drugs!
0:01:33 > 0:01:36But in this film, I'll be looking at why we're trapped in a culture
0:01:36 > 0:01:39of endless work.
0:01:39 > 0:01:41It is over, dude. It is gone.
0:01:42 > 0:01:45If you can't do it, artificial intelligence can.
0:01:46 > 0:01:50How did work go from something we do to who we are?
0:01:50 > 0:01:53It's not a person, it's a performer, right?
0:01:53 > 0:01:56I'm sorry, it's not a person?! It is a person!
0:01:56 > 0:01:58I'm a person sitting in front of you.
0:01:58 > 0:02:03These are the deals that made you work harder, made your pay lower...
0:02:03 > 0:02:07These are very fine decisions, and sometimes you get them wrong.
0:02:07 > 0:02:10..and might one day replace you with a robot.
0:02:16 > 0:02:18PHONE CHIMES
0:02:23 > 0:02:29This is what 12% of us are going to do tonight, and every night -
0:02:29 > 0:02:33wake up at 3:00 am, and check our work e-mail.
0:02:34 > 0:02:38A few hours later, 51% of us are going to spend more time
0:02:38 > 0:02:41checking our work e-mail than eating breakfast.
0:02:47 > 0:02:5230% of jobs in the UK could be automated within the next 15 years.
0:02:54 > 0:02:57As intelligent machines begin their march on the labour market,
0:02:57 > 0:03:01how long will it be before they overhaul our economy completely?
0:03:05 > 0:03:07This is the bigger picture -
0:03:07 > 0:03:11how WE became robots, automated our lives
0:03:11 > 0:03:16at the very moment that robots learnt to become human.
0:03:28 > 0:03:31To understand how we got here,
0:03:31 > 0:03:35we need to go back to the '70s, to a deal that would make us loyal
0:03:35 > 0:03:38to our workplace, that would make us become our jobs,
0:03:38 > 0:03:42and our jobs to eventually become our lives.
0:03:53 > 0:03:55Summer 1975.
0:03:55 > 0:03:59America is in recession, losing jobs at an unprecedented rate.
0:04:04 > 0:04:08Four men set themselves the task of rebuilding American enterprise.
0:04:10 > 0:04:15They meet in downtown San Francisco in an office on the 48th floor.
0:04:16 > 0:04:20Their meeting would change the language of American business.
0:04:24 > 0:04:26One of them was Tom Peters,
0:04:26 > 0:04:29a larger-than-life management consultant,
0:04:29 > 0:04:32who would go on to become a celebrity life coach.
0:04:32 > 0:04:34Huge news!
0:04:34 > 0:04:36Another was Richard Pascale,
0:04:36 > 0:04:40a quietly spoken academic in the field of management science.
0:04:41 > 0:04:45This unlikely double act, who were both working as consultants
0:04:45 > 0:04:47for McKinsey, a management consultancy firm,
0:04:47 > 0:04:52made a deal to work together to create a new management philosophy,
0:04:52 > 0:04:55and one country in particular seemed to show the way.
0:04:58 > 0:05:01It is obvious that the United States economy is not in good shape.
0:05:01 > 0:05:04But while our industry is floundering,
0:05:04 > 0:05:07the Japanese are reporting record profits.
0:05:07 > 0:05:13The Americans were getting hammered by the Japanese in a lot of things,
0:05:13 > 0:05:16but the one that hurt our ego was the automobile market.
0:05:16 > 0:05:21To grossly oversimplify, to the point of not being obviously the
0:05:21 > 0:05:24whole story, the Japanese were beating us on fundamentally what
0:05:24 > 0:05:27we had, very sexy cars, except theirs, actually,
0:05:27 > 0:05:30when you turned the ignition key, they started.
0:05:30 > 0:05:34- Right.- And our quality was highly questionable.
0:05:34 > 0:05:37# There's something inside you... #
0:05:37 > 0:05:41Japan has gone from being a producer of trash to a nation synonymous
0:05:41 > 0:05:42with quality and reliability.
0:05:44 > 0:05:48So this was a moment where the Japanese motor industry was making
0:05:48 > 0:05:51cars that were of quality, they were reliable.
0:05:51 > 0:05:53- Absolutely.- And the American car industry was going...
0:05:53 > 0:05:57Absolutely, and, you know, to be realistic about it, first of all,
0:05:57 > 0:06:00you're attacking us in the car industry, and that doesn't feel good
0:06:00 > 0:06:07to you and me, and secondly, for God's sakes, it was 1977.
0:06:07 > 0:06:10We're only 30 years beyond... Well, what was it?
0:06:10 > 0:06:11From the war.
0:06:11 > 0:06:17Yeah, and everybody's dad or grandad fought in World War II.
0:06:17 > 0:06:19So, you know, we had just beaten these buggers,
0:06:19 > 0:06:21and, "What the hell are they doing?" you know?
0:06:21 > 0:06:24So the whole thing about, "We won the war, but they've won the peace"?
0:06:24 > 0:06:27Absolutely. Precisely so.
0:06:29 > 0:06:32But it was about far more than how to build a better car.
0:06:34 > 0:06:36What else could America learn from the Japanese?
0:06:37 > 0:06:39For Richard Pascale,
0:06:39 > 0:06:42the answer lay in the unique Japanese business culture.
0:06:43 > 0:06:46Everyone feels this incredible sense of commitment.
0:06:46 > 0:06:49You don't come in to hang out and lay back.
0:06:49 > 0:06:52You're in there like you would on a team.
0:06:52 > 0:06:54You're expected to play and hold up your end of the deal.
0:06:54 > 0:06:59So that quality of commitment and energy just resonated with me
0:06:59 > 0:07:01in a very deep and profound way.
0:07:01 > 0:07:05The companies care not only about their workers' job performance,
0:07:05 > 0:07:07but about their health, their family life,
0:07:07 > 0:07:10and their ideas for doing things better.
0:07:10 > 0:07:13Good ideas can win a Canon camera worker a paid vacation
0:07:13 > 0:07:15and a cash bonus.
0:07:15 > 0:07:18This attitude seemed to derive from the values set
0:07:18 > 0:07:21by the companies' bosses.
0:07:21 > 0:07:23Management philosophy was just astonishing.
0:07:23 > 0:07:27You had aspirations that allowed people in their work
0:07:27 > 0:07:30to have a meaningful sense of purpose.
0:07:32 > 0:07:36We felt strongly that this overarching sense of what a company
0:07:36 > 0:07:40is about and the meaning it makes seems to make a difference.
0:07:40 > 0:07:43Together, they arrived at a system of management with what they called
0:07:43 > 0:07:46shared values at its heart.
0:07:46 > 0:07:51What we came up with was a way of taking the best of the
0:07:51 > 0:07:56Japanese ideas and keeping them coupled to...
0:07:56 > 0:07:59God, I hate terms like this because they're such gross
0:07:59 > 0:08:02over-generalisation, but America's innovative spirit.
0:08:04 > 0:08:09Peter and Pascale's meeting of minds appealed to business leaders,
0:08:09 > 0:08:12humbled by Japanese manufacturing prowess.
0:08:12 > 0:08:16From now on, the company wouldn't just give employees jobs to do,
0:08:16 > 0:08:20it would set all-encompassing values for them to follow.
0:08:20 > 0:08:23This was the key to reshaping their businesses.
0:08:25 > 0:08:28Both went on to publish books based on their ideas
0:08:28 > 0:08:30within a year of each other.
0:08:30 > 0:08:33Pascale's The Art Of Japanese Management was published in 1981.
0:08:35 > 0:08:41That shared value insight turned out to be both very useful,
0:08:41 > 0:08:46but as well, at the time, extremely provocative.
0:08:46 > 0:08:49Hard to imagine today, with the whole industry of vision
0:08:49 > 0:08:53and values being created in the last 10 or 20 years,
0:08:53 > 0:08:56in which everyone has their little plaque on the wall
0:08:56 > 0:08:59that states what they aspire to be.
0:08:59 > 0:09:02But at the time when we started talking about this,
0:09:02 > 0:09:07I remember truly violent reactions from audiences.
0:09:07 > 0:09:08What did they say you were trying to do?
0:09:08 > 0:09:11Well, they saw it as brainwashing.
0:09:11 > 0:09:15They saw it as paternalistic, they found it extremely offensive,
0:09:15 > 0:09:20intrusive. Placing companies in a role that was out of their scope
0:09:20 > 0:09:22and inappropriate for a commercial enterprise.
0:09:23 > 0:09:27But while Pascale's book didn't capture the public's imagination,
0:09:27 > 0:09:31Tom Peters took the same ideas and brought them to millions
0:09:31 > 0:09:35with his book called In Search Of Excellence.
0:09:35 > 0:09:37I don't really want to completely cheapen it
0:09:37 > 0:09:39and say I was the populariser,
0:09:39 > 0:09:41but I was the populariser.
0:09:41 > 0:09:45But you were, actually, redefining how a workplace should be.
0:09:45 > 0:09:49Yeah. There is a real intellectual hard...
0:09:49 > 0:09:52- Of course.- ..no baloney, intellectual history for this stuff.
0:09:52 > 0:09:54- Right. - But nobody paid any attention to it.
0:09:54 > 0:09:59- Right.- And first of all we got lucky with labels like Excellence and we
0:09:59 > 0:10:03got lucky with 10% unemployment and we got lucky by being
0:10:03 > 0:10:06caught with our pants down and cars that didn't work.
0:10:06 > 0:10:12And so I will completely acknowledge that this new look at organisations
0:10:12 > 0:10:15came together with insane timing.
0:10:15 > 0:10:18And there was nothing wrong with what we did,
0:10:18 > 0:10:19it was a pretty good piece of work,
0:10:19 > 0:10:23but the timing was a gift from the gods.
0:10:25 > 0:10:29Tom Peters had put a management book on the bestseller list,
0:10:29 > 0:10:31selling in excess of five million copies.
0:10:31 > 0:10:34It was a modern business blockbuster,
0:10:34 > 0:10:37but most importantly, it made management ideas
0:10:37 > 0:10:40a serious topic of conversation in organisations.
0:10:40 > 0:10:45Its thinking was adopted by companies around the world.
0:10:45 > 0:10:47What Peters did was he made culture popular.
0:10:47 > 0:10:52He had initially created this very clever thing for McKinsey called the
0:10:52 > 0:10:587S Model, where he identified the soft and hard drivers of
0:10:58 > 0:11:03organisation culture and how they pulled themselves together to create
0:11:03 > 0:11:06a set of shared values that enabled the delivery of strategy.
0:11:06 > 0:11:09And he... It was a very smart piece of thinking.
0:11:09 > 0:11:11And it enabled leaders, for the first time,
0:11:11 > 0:11:14to get their hands on something and say,
0:11:14 > 0:11:16"so if we change this process over here,
0:11:16 > 0:11:19"or if we change our reward mechanism over here,
0:11:19 > 0:11:21"what we should see is a different outcome, or a different...
0:11:21 > 0:11:24"Driven by a different set of behaviours".
0:11:24 > 0:11:28Before Peters met Pascale, the idea of your work
0:11:28 > 0:11:31having a higher meaning would have seemed ludicrous.
0:11:31 > 0:11:36But as their thinking caught on, our work culture changed.
0:11:36 > 0:11:42We were asked to see work as having a higher purpose, not just a job.
0:11:42 > 0:11:46Work could and would offer us fulfilment.
0:11:49 > 0:11:52As the idea of shared values spread,
0:11:52 > 0:11:55workplaces around the world became more than just workplaces.
0:12:00 > 0:12:02They had mission statements.
0:12:02 > 0:12:05They had values that the workers signed up to.
0:12:06 > 0:12:09Good morning, everybody. Welcome to Services To Fruit.
0:12:09 > 0:12:11For those of you who are new,
0:12:11 > 0:12:15Services To Fruit is our opportunity to recognise unsung heroes.
0:12:15 > 0:12:17People who have gone the extra mile,
0:12:17 > 0:12:20people who have truly lived the values.
0:12:20 > 0:12:23This month's Lady Of The Sash is Katrina.
0:12:30 > 0:12:32At Innocent, makers of fruit smoothies,
0:12:32 > 0:12:36they go further than most to create a unique company culture.
0:12:37 > 0:12:40Using team bonding techniques, inspired from cult films,
0:12:40 > 0:12:43like Bill And Ted's Excellent Adventure,
0:12:43 > 0:12:45they see their workforce as a family.
0:12:49 > 0:12:50One of my favourite things is this.
0:12:50 > 0:12:53So this is... This is our baby photo wall.
0:12:53 > 0:12:56When you join Innocent, we will put your baby photo up.
0:12:56 > 0:12:58The idea being that you can see...
0:12:58 > 0:13:01"This is who I work with, everybody is human, everyone is a baby,
0:13:01 > 0:13:04- "everyone has a job to do". - Tim, is your Chief Exec here?
0:13:04 > 0:13:07Yeah, Douglas, our chief squeezer, he's actually just down the bottom.
0:13:07 > 0:13:09Not at the top, trying to look down on everyone else.
0:13:09 > 0:13:13We're one big team, and you're not just here to do that job.
0:13:13 > 0:13:16- Yeah.- You're here to make the world a better place.
0:13:16 > 0:13:19Do you think someone cynical, diffident and cynical like myself,
0:13:19 > 0:13:21would be able to fit in?
0:13:21 > 0:13:24Because I'm the sort of person that would come and if I was interviewed,
0:13:24 > 0:13:26I'd be like, "Oh, I'm not going to buy into all this stuff."
0:13:26 > 0:13:29Do you see...? I'm just interested in the kind of personality that...
0:13:29 > 0:13:32You know, would someone like myself be accepted?
0:13:32 > 0:13:36We have people that have been here a long time, 10, 15, 16, 17 years.
0:13:36 > 0:13:39And they don't come for beers every Friday and they don't get involved
0:13:39 > 0:13:43in all the parties, but they are still about what Innocent are about.
0:13:43 > 0:13:46But what if you become too dedicated to your job?
0:13:46 > 0:13:49If you have truly drunk in the company values,
0:13:49 > 0:13:52then sooner or later, is there a risk that you become your job?
0:13:53 > 0:13:55And if you are your job,
0:13:55 > 0:13:59what happens when the working day gets longer and longer?
0:14:01 > 0:14:05In Japan, shared values have always gone hand in hand
0:14:05 > 0:14:09with a long hours culture, and what they call karoshi -
0:14:09 > 0:14:12death from overwork.
0:14:12 > 0:14:14In terms of this new workplace,
0:14:14 > 0:14:19this new really highly competitive poster book, '80s workplace,
0:14:19 > 0:14:21it's totally redefined.
0:14:21 > 0:14:24And what it is, is that you have to give your all to the company
0:14:24 > 0:14:26if you're going to survive.
0:14:26 > 0:14:29If you're going to keep your job, it means being...
0:14:29 > 0:14:31You know, policing your own productivity.
0:14:31 > 0:14:36Yeah, I mean, if I work my one ass off to help the ten people who work
0:14:36 > 0:14:40for me be better prepared for the workplace of tomorrow,
0:14:40 > 0:14:45they will work their ten asses off to make me look like a superstar.
0:14:48 > 0:14:51Innocent have a novel way of curbing long hours working culture.
0:14:53 > 0:14:55So this here is our last leaver pulls lever.
0:14:55 > 0:14:58So at the end of the working day,
0:14:58 > 0:15:01the last person to leave the floor will make an announcement at around
0:15:01 > 0:15:038:00, 8:30, and they will shut this off,
0:15:03 > 0:15:07and it basically stops all non-essential electrical equipment.
0:15:07 > 0:15:11The idea being that that last person is telling everyone, "Come on, guys,
0:15:11 > 0:15:13"we've had enough, let's go."
0:15:13 > 0:15:17But not keeping a lid on your working hours can take its toll.
0:15:23 > 0:15:27There are more than 100,000 strokes in the UK each year.
0:15:27 > 0:15:29OK, and again.
0:15:29 > 0:15:33It's the fourth single leading cause of death in the country.
0:15:35 > 0:15:37- Fingers are opening, lovely. - Turn your head.
0:15:37 > 0:15:40Most of it is age-related, but a study by University College London,
0:15:40 > 0:15:42published in The Lancet,
0:15:42 > 0:15:45showed a link to long working hours and a higher risk of stroke.
0:15:45 > 0:15:48This patient thinks the stress of running his business
0:15:48 > 0:15:50played a part in his.
0:15:50 > 0:15:55Reflecting on it, I suppose it was the stress of work.
0:15:55 > 0:15:56Yeah.
0:15:56 > 0:16:01I have an international business, and I'm flying around the world.
0:16:01 > 0:16:02It takes a toll.
0:16:02 > 0:16:05And when you look back, is there one thing that you think,
0:16:05 > 0:16:07"Oh, yeah, you know, I was really worried about that"?
0:16:07 > 0:16:09Or do you think it was just an accumulation of stress?
0:16:09 > 0:16:12I suppose it must have been an accumulation.
0:16:12 > 0:16:16There's no question, I think, in my mind that the kind of job that
0:16:16 > 0:16:19Mr Mehta's doing, often working 12, 18 hours a day
0:16:19 > 0:16:23- for nearly 20, 30 years...- Yeah. - ..that was a contributing factor.
0:16:23 > 0:16:26- And a seven-day week.- Seven days a week, yeah. You never turn off.
0:16:26 > 0:16:29- No. - That's the thing, yeah.
0:16:29 > 0:16:31Ten years ago, I had a stroke.
0:16:31 > 0:16:34A bit like you, it came out of the blue,
0:16:34 > 0:16:39and in the ward I was in, everyone who'd had a stroke
0:16:39 > 0:16:44was either in their late 30s, or in their 40s, in that ward.
0:16:44 > 0:16:49And the common factor amongst all of us was stress.
0:16:49 > 0:16:53Everyone had a highly stressed job, whether it be rubbish collection,
0:16:53 > 0:16:55or a city broker or a lawyer, you know,
0:16:55 > 0:16:59everyone was in there, and they all had high stress jobs, including me.
0:16:59 > 0:17:03I can tell you when I come into the room, when he's not looking,
0:17:03 > 0:17:08he's on his iPad answering e-mails before I came into the room.
0:17:08 > 0:17:12And that's whilst he's had a stroke within the last six weeks,
0:17:12 > 0:17:14and yet, he's just promised us -
0:17:14 > 0:17:16and this is not the first time he's promised, by the way -
0:17:16 > 0:17:19that he will reduce his working hours.
0:17:19 > 0:17:24Quite frankly, the e-mails and mobile phones don't help.
0:17:24 > 0:17:26- Because you never switch off. - No.
0:17:26 > 0:17:28- You can't escape that. - It's impossible.
0:17:30 > 0:17:34Peters, Pascale and their co-writers would foster the idea of
0:17:34 > 0:17:37employee devotion to their employers.
0:17:37 > 0:17:41But then, over time, our bosses began to persuade themselves
0:17:41 > 0:17:45that they didn't need to treat us with the same devotion.
0:17:45 > 0:17:50Two decades after Pascale and Peters met, another deal would encourage
0:17:50 > 0:17:55companies to hire and fire us at will, no matter how devoted we were.
0:18:07 > 0:18:121997 - the world of business is changing fast.
0:18:14 > 0:18:19In America, traditional blue chip companies were losing talented staff
0:18:19 > 0:18:21to tech upstarts from Silicon Valley.
0:18:26 > 0:18:31They needed a solution, and they paid Management consultants McKinsey
0:18:31 > 0:18:33to provide it.
0:18:33 > 0:18:36Helen Handfield-Jones and her colleagues were convinced that
0:18:36 > 0:18:41companies were failing because they were employing the wrong people.
0:18:41 > 0:18:44It was in the '90s with the dot-com boom, big companies all over
0:18:44 > 0:18:47the place that used to get the cream of the crop
0:18:47 > 0:18:51from their recruiting, you know, the IBMs, the GEs,
0:18:51 > 0:18:53were losing talent to dot-com start-ups,
0:18:53 > 0:18:55which they'd never faced before.
0:18:57 > 0:19:01To solve this problem, McKinsey sold a strategy to their clients.
0:19:01 > 0:19:04They called it the war for talent.
0:19:04 > 0:19:08Using practical examples from companies such as General Electric
0:19:08 > 0:19:13and Enron, the authors outlined five imperatives that every leader,
0:19:13 > 0:19:18from CEO to unit manager, must act on to build a stronger talent pool.
0:19:18 > 0:19:21It completely redefined the relationship
0:19:21 > 0:19:24between a company and its workers.
0:19:24 > 0:19:29Talent is the sum total of your skills and ability, values,
0:19:29 > 0:19:33character that you bring to your work role.
0:19:33 > 0:19:35Having great talent in the organisation is critical
0:19:35 > 0:19:37to the business success.
0:19:37 > 0:19:39Now, it sounds like motherhood and apple pie, right?
0:19:39 > 0:19:41Everybody would say that.
0:19:41 > 0:19:44But there's some roles that require just a very unique set of skills
0:19:44 > 0:19:48that doesn't come along easily, and isn't trainable.
0:19:48 > 0:19:51For McKinsey, the most important thing in any company was
0:19:51 > 0:19:55having the right employees. The good people needed to be kept,
0:19:55 > 0:19:58and underperforming staff let go.
0:19:58 > 0:20:03You categorised employees into A, B and C.
0:20:04 > 0:20:08And A were people who were super-talented and successful,
0:20:08 > 0:20:11- and you just allow them to get on with it.- Mm-hm.
0:20:11 > 0:20:14And B were people who were kind of coasting along...
0:20:14 > 0:20:16Doing OK, doing a good job.
0:20:16 > 0:20:18Doing OK, but we're not sure whether to keep them.
0:20:18 > 0:20:22- Yeah.- And then C were people that you sack.
0:20:22 > 0:20:23OK. All of that is mischaracterised.
0:20:23 > 0:20:25Let me start that again.
0:20:25 > 0:20:27That's the hardest part of talent management is dealing with
0:20:27 > 0:20:29low performers. The concept is that...
0:20:29 > 0:20:33Look hard at your low performers, have a clear eyed view,
0:20:33 > 0:20:37assess their performance rigorously like you do everybody else,
0:20:37 > 0:20:38and make a decision. That's what it means.
0:20:38 > 0:20:42So you didn't say that the C person should just be sacked?
0:20:42 > 0:20:44It's not a person, it's a performer.
0:20:44 > 0:20:47- Right?- Oh, sorry. So it's not a human being?
0:20:47 > 0:20:48No. No, no.
0:20:48 > 0:20:51- No, what I'm saying is... - It's not a person?! It IS a person.
0:20:51 > 0:20:54I'm a person sitting in front of you,
0:20:54 > 0:20:55being interviewed by my boss.
0:20:55 > 0:20:58- Yeah, yeah.- And they're deciding whether I should be sacked or kept.
0:20:58 > 0:21:01- Yes, but it...- I'm a person. - Of course you're a person.
0:21:01 > 0:21:03But you said I wasn't a person. You said I was a performer.
0:21:03 > 0:21:06But you also used the word a moment earlier that said you assess
0:21:06 > 0:21:08whether people were TALENTED or not by A, B or C.
0:21:08 > 0:21:12I said, "No, that's not right." We assess people on their performance.
0:21:12 > 0:21:16- Right.- Are they performing at a high level, an OK level,
0:21:16 > 0:21:20- or a poor level?- Right.- Below acceptable level of performance.
0:21:20 > 0:21:22They're toxic to the organisation, they're not growing
0:21:22 > 0:21:25their businesses, if that's what you need them to do,
0:21:25 > 0:21:27they're poor people managers, whatever it is,
0:21:27 > 0:21:29whatever dimension of performance -
0:21:29 > 0:21:32this is not acceptable performance for a leader of our company.
0:21:36 > 0:21:38In the late 1990s and early 2000s,
0:21:38 > 0:21:41the war for talent would become hugely influential.
0:21:43 > 0:21:47As companies did everything possible to keep their supposedly A-grade
0:21:47 > 0:21:52employees, there was a massive increase in executive pay.
0:21:52 > 0:21:57So if you go back 20, 25 years, the ratio, say in the UK,
0:21:57 > 0:22:01across most of Europe, between a CEO's pay
0:22:01 > 0:22:05and the average earner in an organisation, was about 20-1.
0:22:06 > 0:22:11And something happened in the late '80s through the '90s to the
0:22:11 > 0:22:16early 2000s which saw Chief Executives getting increases
0:22:16 > 0:22:18of 100-300% over that period of time.
0:22:18 > 0:22:22The flames were fanned by the war-for-talent thinking.
0:22:22 > 0:22:27Do you think there's a danger you create a kind of inherent inequality
0:22:27 > 0:22:31within an organisation because just by dividing people into A, B and C,
0:22:31 > 0:22:33you create resentment, you breed anxiety?
0:22:33 > 0:22:36Well, no, again, you make an assumption that's not right.
0:22:36 > 0:22:40We do not tell people, "Oh, by the way, you're a B, or you're a B plus,
0:22:40 > 0:22:42"or you're an A, whatever. "You have potential..."
0:22:42 > 0:22:45We don't tell people that. It's not about stamping letter grades
0:22:45 > 0:22:49on people's foreheads or having Crown Princes walk around
0:22:49 > 0:22:50with crowns on their heads.
0:22:50 > 0:22:53It's about giving each person the feedback that's most helpful and
0:22:53 > 0:22:57appropriate to them, about what they can do to perform better,
0:22:57 > 0:23:00and how their careers might unfold in this organisation.
0:23:00 > 0:23:03So, Helen, has one of the legacies of the war for talent been,
0:23:03 > 0:23:07in a way, the idea that we all need to be talented now in the workplace?
0:23:07 > 0:23:09Absolutely, yeah.
0:23:09 > 0:23:14These companies have lessened their commitment, long-term, to people.
0:23:14 > 0:23:16In the old days, you hired somebody full-time,
0:23:16 > 0:23:19and you hired them forever. And people thought,
0:23:19 > 0:23:22"I have a job with one company, I stay there forever."
0:23:22 > 0:23:23Right? That's long gone.
0:23:23 > 0:23:27There's no free lunches any more, right? For anybody.
0:23:29 > 0:23:32The world of work had changed significantly since
0:23:32 > 0:23:35Peters and Pascale's vision of shared values -
0:23:35 > 0:23:39being loyal to the workplace and your boss being loyal to you.
0:23:40 > 0:23:43The importance of shared values
0:23:43 > 0:23:46shouldn't be underestimated as a concept.
0:23:46 > 0:23:52It's probably the lasting huge thought that is still with us and
0:23:52 > 0:23:56should keep going with us because, of course, it was shared values that
0:23:56 > 0:23:59we got wrong in the '90s and the early 2000s with the war for talent
0:23:59 > 0:24:02and the L'Oreal generation, the shared value became, "Me".
0:24:04 > 0:24:06It didn't become a greater creation of wealth.
0:24:06 > 0:24:11It didn't become doing the right thing, and we lost the sort of
0:24:11 > 0:24:15Aristotle version of a virtuous or a good leader.
0:24:19 > 0:24:22The ideas in the war for talent would affect us all.
0:24:23 > 0:24:29It told businesses worldwide that it was OK to hire and fire us.
0:24:29 > 0:24:32Today, the average worker has six jobs
0:24:32 > 0:24:35over the course of his or her life.
0:24:35 > 0:24:39New graduates expect to have 20.
0:24:39 > 0:24:41The job for life is over.
0:24:43 > 0:24:47And it was the tech firms, whose rise prompted the war for talent
0:24:47 > 0:24:51in the first place, who took the idea to its logical extreme.
0:24:52 > 0:24:55At the beginning of the shift, I get my phone out,
0:24:55 > 0:24:57log in, and set myself as available, and off I go.
0:24:59 > 0:25:02A world without long-term employees,
0:25:02 > 0:25:06where even the very idea of staff is redundant.
0:25:07 > 0:25:10I normally do Monday to Friday lunch shifts,
0:25:10 > 0:25:1410-2, and a couple evenings on top of that.
0:25:17 > 0:25:21Meg Brown is a casual worker, part of the so-called gig economy.
0:25:21 > 0:25:25Instead of a regular wage, she gets paid for the gigs she does,
0:25:25 > 0:25:29delivering restaurant food to customers' front doors.
0:25:29 > 0:25:32It's estimated that five million people globally are employed
0:25:32 > 0:25:37this way, managed by a computer algorithm, and not by a person.
0:25:37 > 0:25:40What kind of guarantees of work do you have?
0:25:40 > 0:25:42You'll wait for the orders to come in.
0:25:42 > 0:25:47- Right.- There'll usually be a peak-time of the meal times.
0:25:47 > 0:25:48- Right, yeah. - So it really varies.
0:25:53 > 0:25:56Anything could change at any moment.
0:25:56 > 0:26:00I don't know what's happening on the other end.
0:26:00 > 0:26:03I don't know if business is slow,
0:26:03 > 0:26:07or it's just because I'm not being assigned a job.
0:26:07 > 0:26:11I'm vulnerable, and at the mercy of the algorithm,
0:26:11 > 0:26:15and whoever's at control on the other end.
0:26:15 > 0:26:18Half the time, you don't get to see a human being and you don't know
0:26:18 > 0:26:21who... What kind of personalities you're interacting with,
0:26:21 > 0:26:23or if you are interacting with any personality.
0:26:23 > 0:26:29It does feel like I'm missing something in that human-contact way
0:26:29 > 0:26:31of just interacting with a screen.
0:26:31 > 0:26:33- There's no human being doing it, it's the algorithms.- Yeah.
0:26:33 > 0:26:36We keep talking... I mean, this film is all about the future of work,
0:26:36 > 0:26:39but actually the algorithms are deciding how you work now,
0:26:39 > 0:26:41and whether you are even employed or not.
0:26:41 > 0:26:44Yeah. It makes me feel a bit dispensable.
0:26:46 > 0:26:47Algorithms are the new boss.
0:26:49 > 0:26:53Algorithms telling them where to go, to do the service.
0:26:53 > 0:26:57Algorithms determining the price of the service.
0:26:58 > 0:27:01The algorithm can also sack you these days.
0:27:01 > 0:27:04So there's quite a lot of platforms. There's a rating system of points
0:27:04 > 0:27:08or stars, and if you fall below a certain threshold,
0:27:08 > 0:27:10you get terminated automatically.
0:27:10 > 0:27:13You might just try and log on one day to your app,
0:27:13 > 0:27:16and it simply no longer says that you've got an active account.
0:27:17 > 0:27:21Algorithm management has become algorithm rebellion.
0:27:21 > 0:27:25Gig economy workers torn between the idea of being your own boss
0:27:25 > 0:27:28while being controlled by the smartphones in their pockets are
0:27:28 > 0:27:31arguing for the same employment rights as everyone else.
0:27:31 > 0:27:34An independent review is currently looking into this.
0:27:35 > 0:27:38The way that this type of work is sold,
0:27:38 > 0:27:41whether it be Deliveroo or any company that does this,
0:27:41 > 0:27:44is that it's flexibility, that it gives...
0:27:44 > 0:27:47And that, you know, this is a great way to work, this is the future.
0:27:47 > 0:27:50The flexibility is overplayed massively.
0:27:50 > 0:27:54No guarantee of minimum wage, let alone the living wage.
0:27:54 > 0:27:56It's a really insecure way to live.
0:27:56 > 0:28:00It's hard to plan your life on this kind of wages,
0:28:00 > 0:28:03and this kind of nature of work.
0:28:06 > 0:28:11The war for talent was a critical driver of corporate performance,
0:28:11 > 0:28:14but employers have since realised that technology
0:28:14 > 0:28:16can make that process ruthlessly efficient.
0:28:19 > 0:28:22And today, that insight has spread far beyond companies
0:28:22 > 0:28:27like Deliveroo and Uber. Soon, everyone could be monitored,
0:28:27 > 0:28:30including those working in professional office jobs.
0:28:32 > 0:28:35In Boston, Massachusetts, there's one company ensuring
0:28:35 > 0:28:38none of us can escape the computer's all-seeing eye.
0:28:40 > 0:28:44Their technology allows companies to monitor the productivity of its
0:28:44 > 0:28:48staff and employees to monitor their own workplace performance
0:28:48 > 0:28:52through technology embedded into a card you wear around your neck.
0:28:53 > 0:28:56They are already selling their services to companies like
0:28:56 > 0:28:58Deloitte and Bank of America.
0:28:58 > 0:29:02This measures with Bluetooth and accelerometer,
0:29:02 > 0:29:04a microphone and a GPS,
0:29:04 > 0:29:08it understands my activity level, it understands where I am.
0:29:08 > 0:29:12Am I at a desk? Am I on a different floor? Am I in a conference room?
0:29:12 > 0:29:16It understands only two things about speech -
0:29:16 > 0:29:19am I talking or not talking?
0:29:19 > 0:29:24Combining all of this across my day in the job,
0:29:24 > 0:29:26it understands how I actually get work done.
0:29:26 > 0:29:29And when you combine that with all the digital signatures that we are
0:29:29 > 0:29:32also patterning, it directly translates into, "Am I happy?
0:29:32 > 0:29:34"Am I loyal to the organisation?
0:29:34 > 0:29:37"And as a company, are we productive and are we profitable?"
0:29:37 > 0:29:40You mentioned the word "happiness" there, which intrigues me.
0:29:40 > 0:29:43So how can you measure someone's happiness through the data?
0:29:43 > 0:29:46With all of these different signatures that we are picking up
0:29:46 > 0:29:49on, we can interpret stress levels.
0:29:50 > 0:29:53As a manager, I can understand if any particular of my employees are
0:29:53 > 0:29:57being overworked, overstressed,
0:29:57 > 0:30:01and are translating to lack of happiness and lack of productivity.
0:30:03 > 0:30:08The data collected on staff can also monitor communication patterns.
0:30:08 > 0:30:11The thicker lines suggest more communication,
0:30:11 > 0:30:13the thinner lines, less.
0:30:13 > 0:30:16It monitors whether certain staff are dominating situations
0:30:16 > 0:30:19or working collaboratively.
0:30:19 > 0:30:20So who's the central person...?
0:30:22 > 0:30:25- Yeah.- Being the size of organisation we are, we might have insight into
0:30:25 > 0:30:28who that is, but what this is a perfect example of
0:30:28 > 0:30:31is what we all describe as bottleneck.
0:30:31 > 0:30:33A single person in our organisation
0:30:33 > 0:30:35has unintentionally been a bottleneck,
0:30:35 > 0:30:40so what's been happening the last month that has drawn this behaviour?
0:30:40 > 0:30:42And what can we do going forward
0:30:42 > 0:30:45to mitigate or eliminate that bottleneck behaviour?
0:30:45 > 0:30:47But you see, you know...
0:30:47 > 0:30:50You said you've got quite a fair idea of who that person is.
0:30:50 > 0:30:53- Oh, absolutely.- So you don't need... In a way, the anonymity
0:30:53 > 0:30:55doesn't matter because, just through the data,
0:30:55 > 0:30:58you can work out who that person was because you know your employees
0:30:58 > 0:31:00and, therefore, you put two and two together.
0:31:00 > 0:31:02- In this case.- Yeah.
0:31:02 > 0:31:06- In this case, but it's usually only for those anomalies.- Yeah.
0:31:06 > 0:31:09I suppose the anonymity thing seems a really key element of it
0:31:09 > 0:31:11because I think that's the bit that would frighten people -
0:31:11 > 0:31:15the idea that, "Oh, they're going to drill down into me personally,
0:31:15 > 0:31:18"and my data, and that's going to expose me within the company."
0:31:18 > 0:31:20So...
0:31:20 > 0:31:23there must be a temptation, if a company comes along and says,
0:31:23 > 0:31:25"Can you provide this service?"
0:31:25 > 0:31:28Would you say "yes" or would you say "No, we can't do that"?
0:31:28 > 0:31:34It violates our privacy policy, which starts and is rooted in
0:31:34 > 0:31:39personal privacy and personal ownership of your behavioural data.
0:31:39 > 0:31:43And you can do good for the organisation and maintain
0:31:43 > 0:31:48that simple truth by exposing the patterns of the individual and from
0:31:48 > 0:31:50a top-down perspective exposing those patterns anonymously
0:31:50 > 0:31:52about the individual,
0:31:52 > 0:31:56- but exposing those patterns from a management perspective down.- Right.
0:31:56 > 0:31:58So there is a line to be drawn, really, and that's the line, really,
0:31:58 > 0:32:01- the line of privacy. - And you draw it in the beginning.
0:32:01 > 0:32:03Amazing. It's a staggering level, isn't it?
0:32:03 > 0:32:06I'm just kind of... It does really blow your mind,
0:32:06 > 0:32:11the level to which you are able to analyse every single aspect of
0:32:11 > 0:32:13what you're doing at work. It's mind-blowing.
0:32:13 > 0:32:16It's already happening. Your manager is coming in and saying,
0:32:16 > 0:32:20"I think you're blocking everybody else's communication
0:32:20 > 0:32:22"and you're forcing, you know,
0:32:22 > 0:32:25"one office to communicate with the other office through you".
0:32:25 > 0:32:28The ability to capture that, analyse it,
0:32:28 > 0:32:31that computer science really hasn't existed at scale
0:32:31 > 0:32:33except for the last handful of years.
0:32:36 > 0:32:40I have never seen an office where people are wearing a badge
0:32:40 > 0:32:44that collects all the information on every single thing you do,
0:32:44 > 0:32:47from the moment you walk in to the moment you leave.
0:32:47 > 0:32:54So, basically, this little box tells them who's come into the room.
0:32:56 > 0:32:59It's just... It's just unbelievable.
0:32:59 > 0:33:02And what it is, what's really extraordinary about it is it's
0:33:02 > 0:33:06given to you, so you can then police your own productivity
0:33:06 > 0:33:10because we ourselves are our harshest critics,
0:33:10 > 0:33:13and they know that. That's the genius of this stuff.
0:33:18 > 0:33:21But how soon before technology doesn't just enable staff to work
0:33:21 > 0:33:25more effectively, but takes over completely?
0:33:28 > 0:33:30Doctors, accountants, even lawyers,
0:33:30 > 0:33:32are starting to see their professions change.
0:33:34 > 0:33:36And it's all thanks to a deal made
0:33:36 > 0:33:39in a sleepy town in upstate New York.
0:33:41 > 0:33:47# Silver coins that jingle jangle
0:33:47 > 0:33:48# Fancy shoes...#
0:33:48 > 0:33:52In 2004, a man called Charles Lickel came to this steakhouse
0:33:52 > 0:33:55with his colleagues after a hard day's work.
0:33:56 > 0:34:01Lickel was head of computing system software at tech giant IBM.
0:34:05 > 0:34:09As they ate, at 7:00 on the dot, something strange happened.
0:34:09 > 0:34:11APPLAUSE
0:34:15 > 0:34:18All of the diners leapt up from their seats
0:34:18 > 0:34:20and rushed out of the room.
0:34:20 > 0:34:24The TV above the bar had just started showing that night's episode
0:34:24 > 0:34:27of the long-running TV quiz show Jeopardy!
0:34:27 > 0:34:30This is Jeopardy!
0:34:31 > 0:34:33Here are today's contestants.
0:34:33 > 0:34:37A risk analytics manager from Irvine, California,
0:34:37 > 0:34:38Patrick Fernandez.
0:34:38 > 0:34:44Reigning champion, Ken Jennings, was on a record-breaking winning streak.
0:34:44 > 0:34:47Everyone in the restaurant, everyone in America,
0:34:47 > 0:34:50was desperate to find out if he could keep it going.
0:34:50 > 0:34:53Lickel, his colleagues, and the nation, were gripped.
0:34:53 > 0:34:56Let's go to Ken Jennings now. He selected Matthew and Andrew.
0:34:56 > 0:34:59And he risked 5,800, that's right.
0:34:59 > 0:35:00APPLAUSE
0:35:00 > 0:35:0429,000 is what you get today and it brings your total to...
0:35:04 > 0:35:07The unstoppable Ken Jennings did it again that night.
0:35:07 > 0:35:10He seemed more machine than human.
0:35:10 > 0:35:14At that moment, standing at that bar, Lickel had an epiphany.
0:35:14 > 0:35:16He suddenly saw the perfect challenge -
0:35:16 > 0:35:19to invent a computer programme that could beat a human
0:35:19 > 0:35:21at the world's most popular quiz show.
0:35:23 > 0:35:27IBM would do a deal with the makers of Jeopardy!
0:35:27 > 0:35:29They would create artificial intelligence that could
0:35:29 > 0:35:32beat the game's greatest players,
0:35:32 > 0:35:36a machine that could understand human language,
0:35:36 > 0:35:40access hordes of information, and answer questions.
0:35:42 > 0:35:45The man tasked to create it was David Ferrucci.
0:35:47 > 0:35:50When I heard about Jeopardy!, I said, "We've got to do this".
0:35:50 > 0:35:52This is exactly the kind of thing we've been working on for years.
0:35:52 > 0:35:55Of course, the problem was, most people were saying,
0:35:55 > 0:35:57"No, this is impossible. You're going to embarrass IBM,
0:35:57 > 0:36:00"essentially. You're going to get on television, this is pure folly,
0:36:00 > 0:36:02"you're going to fall on your face,
0:36:02 > 0:36:04"and you're going to embarrass the whole company."
0:36:04 > 0:36:07We had these meetings with all these executives around big
0:36:07 > 0:36:08conference tables, and they would say,
0:36:08 > 0:36:11"Dave, you're mucking with the IBM brand. You need to win.
0:36:13 > 0:36:16"Whatever you want, just tell us what you need.
0:36:16 > 0:36:18"Tell us what you need to win."
0:36:18 > 0:36:21IBM already had Deep Blue, which had beaten Garry Kasparov at chess.
0:36:21 > 0:36:24So how was this challenge going to be different?
0:36:24 > 0:36:25Jeopardy! can ask about anything,
0:36:25 > 0:36:28- whereas chess, you always knew what you were dealing with.- Yeah.
0:36:28 > 0:36:30I mean, there's a bunch of pieces, they move in certain ways.
0:36:30 > 0:36:33There's a finite set of moves. You knew what you were dealing with.
0:36:33 > 0:36:36This space was wholly unconstrained. The only thing we had was the prior
0:36:36 > 0:36:38data, in other words, answers had to come from places like
0:36:38 > 0:36:41Wikipedia, dictionaries, novels, plays, the Bible,
0:36:41 > 0:36:44wherever this stuff could come from. So you had that content base,
0:36:44 > 0:36:48but to take that question and figure out what that question was asking,
0:36:48 > 0:36:51and you don't want to buzz in and be wrong.
0:36:51 > 0:36:53So you had to be able to compute your confidence that you were going
0:36:53 > 0:36:55- to be right. - You must have needed to do some
0:36:55 > 0:36:57trial runs, so what were the trial runs like?
0:36:57 > 0:37:00Were there any glitches or anything?
0:37:00 > 0:37:01In the beginning, it was terrible.
0:37:01 > 0:37:05I mean, it would make ridiculous, ridiculous mistakes.
0:37:05 > 0:37:10And at that time, Watson took two hours to answer a single question,
0:37:10 > 0:37:12so we actually couldn't play it live.
0:37:12 > 0:37:14It would be a very boring, long game.
0:37:14 > 0:37:16This went on for a couple of years,
0:37:16 > 0:37:20until we got to a point where Watson could answer a question in
0:37:20 > 0:37:22three seconds, between two and three seconds.
0:37:22 > 0:37:26- Whoa! - And that was fast enough to play.
0:37:26 > 0:37:29The day you go into the studio, Dave, what are you thinking?
0:37:29 > 0:37:31As a scientist, I was going in saying, "I wish I could just
0:37:31 > 0:37:34"publish the practice games," because if you just set up
0:37:34 > 0:37:37the AI challenge, you know, we did it.
0:37:37 > 0:37:40We had a very competitive Jeopardy! player that can beat the best humans
0:37:40 > 0:37:43at something that no-one thought was even possible.
0:37:43 > 0:37:46Developed and programmed especially for this moment,
0:37:46 > 0:37:48ladies and gentlemen, this is Watson.
0:37:48 > 0:37:50APPLAUSE
0:37:50 > 0:37:52This was going to come down to one game.
0:37:52 > 0:37:55And if we go out there and we lose that one game, that was going to
0:37:55 > 0:37:57be on television, it would look like the team had lost.
0:37:57 > 0:38:01And that was just a painful thing to think about because, I mean,
0:38:01 > 0:38:04I was in there day-to-day, trying to make this thing happen.
0:38:04 > 0:38:06- Watson?- Who is CS Lewis?
0:38:06 > 0:38:09- Yes.- Who is Sir Christopher Wren?
0:38:09 > 0:38:11- You are right.- What is Picasso?
0:38:11 > 0:38:13- No, sorry.- What is narcolepsy?
0:38:13 > 0:38:19You are right and, with that, you move to 36,681.
0:38:19 > 0:38:21- APPLAUSE - A big, big lead.
0:38:21 > 0:38:23When Ken saw Watson get the daily double -
0:38:23 > 0:38:26Ken knows Jeopardy! really well, and he could do math -
0:38:26 > 0:38:28and he was like, "That's it, I lost".
0:38:28 > 0:38:33Even though you were only 32% sure of your response, you are correct.
0:38:37 > 0:38:41Final Jeopardy!, Watson still would have won by a dollar...
0:38:41 > 0:38:42That's...!
0:38:42 > 0:38:46Which probably would have had a very different effect.
0:38:46 > 0:38:49IBM's future is resting on 1!
0:38:49 > 0:38:531! Isn't that fantastic?
0:38:53 > 0:38:55We weren't just building a game machine.
0:38:55 > 0:38:59This was a machine that was taught to process language more effectively
0:38:59 > 0:39:05than any other machine before, and that was a great result for us.
0:39:05 > 0:39:07And I think that got people saying, "Hey, what else can this do?
0:39:07 > 0:39:10"Can this kind of intelligence help us do things?"
0:39:13 > 0:39:16Watson's ability to understand natural language is unprecedented
0:39:16 > 0:39:20and IBM are now selling the software worldwide.
0:39:20 > 0:39:23As a result, it's poised to take a whole host of jobs
0:39:23 > 0:39:26previously thought off-limits to machines.
0:39:26 > 0:39:28It's being used to do the work of lawyers,
0:39:28 > 0:39:31to calculate insurance policies,
0:39:31 > 0:39:33and even to provide technology-enhanced
0:39:33 > 0:39:35patient care in hospitals.
0:39:37 > 0:39:40We're working with IBM Watson to actually create a platform that we
0:39:40 > 0:39:43can answer children's questions when they want to have them answered.
0:39:43 > 0:39:46People talk about bedside manner, but can you create a digital
0:39:46 > 0:39:49bedside manner? So that's the vision, is to have an actual
0:39:49 > 0:39:53artificial intelligence built into how we engage with our children.
0:39:53 > 0:39:56Right, now, what I was wanting to have a go at was to see if I can try
0:39:56 > 0:40:00and train this computer to ask questions that you have.
0:40:00 > 0:40:04But to do that, I need to do what questions you want to know about
0:40:04 > 0:40:06and also if you think the answers are any good.
0:40:06 > 0:40:08Because I'm training it at the moment, because I can't be here
0:40:08 > 0:40:11all the time, because I have to go from patient to patient.
0:40:11 > 0:40:14But especially for the easier questions, if you just ask this,
0:40:14 > 0:40:17and then we can spend more time talking about important things
0:40:17 > 0:40:19that you want to know about.
0:40:19 > 0:40:22Is it going to hurt?
0:40:22 > 0:40:25I understand that you might be worried about having an operation.
0:40:25 > 0:40:28That's OK. During the operation, you won't feel anything,
0:40:28 > 0:40:30as we will give you an anaesthetic.
0:40:30 > 0:40:34Watson is being used at this Children's Hospital in Liverpool
0:40:34 > 0:40:38to help answer patients' questions on everything from the hospital menu
0:40:38 > 0:40:41to details of their treatment.
0:40:41 > 0:40:44How long does it take to fall asleep?
0:40:44 > 0:40:49The project is part of a wider big data research collaboration between
0:40:49 > 0:40:52IBM and the Government's Science and Technology Facilities Council,
0:40:52 > 0:40:56worth £315 million.
0:40:56 > 0:40:58If successful, and rolled out nationally,
0:40:58 > 0:41:01it could generate savings for the NHS.
0:41:01 > 0:41:04What have you attended for? Well, you were having your appendix
0:41:04 > 0:41:07taken out, weren't you? Nope, it's not got that right.
0:41:07 > 0:41:11OK. So I'm going to train it by saying that was the wrong answer.
0:41:11 > 0:41:14OK, so we'll give that one an unhappy face.
0:41:14 > 0:41:17Every so often, it gives you the wrong answer and you kind of go,
0:41:17 > 0:41:19"No, that's the wrong thing to say". And then it learns from that,
0:41:19 > 0:41:22and it starts to progress, so that's what it's like at the start.
0:41:22 > 0:41:25So who knows what it'll be like in five or ten years' time?
0:41:27 > 0:41:31Jobs that require empathy were for years considered the last frontier
0:41:31 > 0:41:34for automation, but no longer.
0:41:34 > 0:41:39Now we have algorithms that are able to substitute for cognitive work
0:41:39 > 0:41:43in that, in the previous realm, we had seen machines only able to
0:41:43 > 0:41:49replace manual work. Now it is no longer clear what it is that humans
0:41:49 > 0:41:52are fundamentally better at than machines.
0:41:53 > 0:41:57And among all the jobs that we are slowly losing to machines,
0:41:57 > 0:42:00one stands out - teaching.
0:42:02 > 0:42:05Children can now be educated by computers
0:42:05 > 0:42:07in schools that look like workplaces.
0:42:14 > 0:42:191990 - two political scientists secure a grant from a
0:42:19 > 0:42:23conservative American think tank to carry out a major survey
0:42:23 > 0:42:25into the US education system.
0:42:27 > 0:42:31They wanted to understand why, after more than a quarter century of
0:42:31 > 0:42:35costly education reform, the nation's schools have proven
0:42:35 > 0:42:37so resistant to change.
0:42:37 > 0:42:39The key to the problem, they concluded,
0:42:39 > 0:42:41were the powerful teaching unions.
0:42:44 > 0:42:46Terry Moe was one of those political scientists
0:42:46 > 0:42:50and has since written extensively on the politics of American education.
0:42:52 > 0:42:56What you want are schools and school systems that have incentives to
0:42:56 > 0:43:02create the best possible schools and organisations and
0:43:02 > 0:43:04education environments for children.
0:43:04 > 0:43:09And if it turns out that certain teaching methods,
0:43:09 > 0:43:11certain approaches to learning, are best,
0:43:11 > 0:43:13those are the ones that should be adopted.
0:43:13 > 0:43:17But if powerful groups have
0:43:17 > 0:43:23embraced methods and approaches that don't do that,
0:43:23 > 0:43:25- they're still going to do it.- Right.
0:43:25 > 0:43:28They are still going to impose these things on children because
0:43:28 > 0:43:31they believe them and they're powerful enough to entrench them
0:43:31 > 0:43:36in the schools. Teachers, I think, on average, love kids, you know,
0:43:36 > 0:43:39they want schools that are really productive.
0:43:39 > 0:43:43However, they join unions to protect their jobs.
0:43:43 > 0:43:47But you know, bigger picture, what's the point of the school system?
0:43:47 > 0:43:51It's to educate children. That's the ONLY point of the school system.
0:43:51 > 0:43:54It was never set up in order to provide jobs to adults.
0:43:55 > 0:44:00Moe decided the only alternative to internal politics in teaching was
0:44:00 > 0:44:05to open up the education system to market forces of supply and demand,
0:44:05 > 0:44:08an argument he set out in a book which would go on to influence
0:44:08 > 0:44:11the British education system, too.
0:44:11 > 0:44:14The book was published, we had the Blair government come in,
0:44:14 > 0:44:17New Labour in the '90s, you're hailed as prophets.
0:44:17 > 0:44:20Revolutionising... And we get the academy system,
0:44:20 > 0:44:21which is...
0:44:21 > 0:44:26Which is basically an attempt to put your ideas into practise.
0:44:26 > 0:44:28So, how did you feel that went?
0:44:28 > 0:44:31Did you see it as an endorsement of your ideas?
0:44:32 > 0:44:36Yes. I mean, I think that, first of all,
0:44:36 > 0:44:40I think we're right in the sense that politics does
0:44:40 > 0:44:45lead to schools that are perversely organised
0:44:45 > 0:44:46and you can expect that to happen.
0:44:46 > 0:44:51# Funky life, I've been told
0:44:51 > 0:44:54# All that glitters is not gold
0:44:54 > 0:44:55# And gold is not reality... #
0:44:55 > 0:44:59What Moe and associate John Chubb concluded was that technology
0:44:59 > 0:45:03is the key to breaking down the politics of education.
0:45:03 > 0:45:07Technology threatens the idea of school as a building,
0:45:07 > 0:45:10with kids and teachers in the same physical place.
0:45:10 > 0:45:14Technology could make traditional teaching roles obsolete.
0:45:14 > 0:45:17In that book, you argue that technology
0:45:17 > 0:45:20is the way to transform the class,
0:45:20 > 0:45:23in every way, to break the teacher unions, and to do it,
0:45:23 > 0:45:26you bring technology, incrementally, into the classroom,
0:45:26 > 0:45:28and that way teachers slowly become marginalised.
0:45:28 > 0:45:29Yeah, in effect.
0:45:31 > 0:45:33So let's go back to the beginning.
0:45:33 > 0:45:37People tend to think of this as, like, greater reliance on computers
0:45:37 > 0:45:39in the classroom, you know.
0:45:39 > 0:45:41But big picture -
0:45:41 > 0:45:44what we're talking about here is the revolution in
0:45:44 > 0:45:50information technology, which is one of the most powerful
0:45:50 > 0:45:54transformative events in the history of the world, you know.
0:45:54 > 0:45:58It sounds like hyperbole, you know, but it is.
0:45:58 > 0:46:03They talk very directly about how you can use technology to kind of
0:46:03 > 0:46:06usurp the power of the teaching unions
0:46:06 > 0:46:10and you can kind of fragment the workforce.
0:46:10 > 0:46:13They also talk about the opening up of data, so if you take the data
0:46:13 > 0:46:16out of the black box that is the classroom,
0:46:16 > 0:46:19and basically out of the teacher's head,
0:46:19 > 0:46:21then you can start to take the power away from teachers.
0:46:21 > 0:46:25You know, data in the hands of a good teacher is a very...
0:46:25 > 0:46:27It can be a powerful tool.
0:46:27 > 0:46:31But if your reason for doing it is to introduce market forces
0:46:31 > 0:46:34and ultimately a consumer market in education products,
0:46:34 > 0:46:37which is where they're heading with this,
0:46:37 > 0:46:41then at the very least we need to have a conversation about this.
0:46:41 > 0:46:45Is this about improving education for children and their prospects for
0:46:45 > 0:46:49the future, or is it about the privatisation of education?
0:46:50 > 0:46:52Listen up real quick, guys.
0:46:52 > 0:46:55Just to let you know, we have about 20 minutes left of class.
0:46:55 > 0:46:57Let me know if you guys have any questions.
0:46:57 > 0:46:59Good job.
0:46:59 > 0:47:02So I want you to try again and call me over before you submit,
0:47:02 > 0:47:03and we'll go through it, OK?
0:47:03 > 0:47:08At this school in San Diego, 75 pupils study at computer terminals
0:47:08 > 0:47:11while being monitored by one teacher.
0:47:13 > 0:47:14Students sitting next to each other
0:47:14 > 0:47:17are rarely learning the same subjects.
0:47:18 > 0:47:22This is Jordan. He's taking the astronomy 101,
0:47:22 > 0:47:24so this is the instructor for astronomy.
0:47:24 > 0:47:26And then next to him, Brendan, he's doing coding.
0:47:26 > 0:47:29- OK.- So he's doing a coding course for the coding academy.
0:47:29 > 0:47:32And then we have principles of astronomy here.
0:47:32 > 0:47:34And how does it differ from old-fashioned teaching?
0:47:34 > 0:47:37So, back in the day, a lot of people just thought about,
0:47:37 > 0:47:39"Oh, yeah, pencil and paper works the best,"
0:47:39 > 0:47:42but now that technology has improved and stuff like that,
0:47:42 > 0:47:44it's really cool to get different education.
0:47:44 > 0:47:47So, Jordan, you want to be an astronomer.
0:47:47 > 0:47:49- Well...- Or maybe not? After watching that.
0:47:49 > 0:47:51I've thought about it, but the class is very interesting,
0:47:51 > 0:47:53so I decided to take it.
0:47:53 > 0:47:58Computers, ironically, can give them a personal expedience.
0:47:58 > 0:48:04One-on-one. You compare that to the standard model of a teacher and
0:48:04 > 0:48:0730 students. What is that? Right, a teacher has to provide
0:48:07 > 0:48:11a standardised curriculum to everybody in the class.
0:48:11 > 0:48:15It's completely inefficient as a way of teaching kids
0:48:15 > 0:48:18and I think most educators think, "Well, there isn't any alternative.
0:48:18 > 0:48:22"That's just what teaching is." But it's not. Not any more.
0:48:22 > 0:48:24This is like a dream come true.
0:48:28 > 0:48:32In this school, learning by computer means you're also assessed
0:48:32 > 0:48:37by computer. Huge amounts of data on students give teachers a record,
0:48:37 > 0:48:41not just of exam results, but of how hard each pupil has been working.
0:48:43 > 0:48:46As we look at our world history classes and our students,
0:48:46 > 0:48:49how are we doing for the progress for this last week?
0:48:49 > 0:48:51Yeah, we're doing pretty good.
0:48:51 > 0:48:53I mean, looking at the data, we had one struggling student.
0:48:53 > 0:48:56You can see it kind of marked right here in negative three.
0:48:56 > 0:48:59And do we have a plan of action for our student?
0:48:59 > 0:49:02We need to e-mail the parent and say "Hey, they're behind in this course.
0:49:02 > 0:49:04"Here's what we're working on."
0:49:04 > 0:49:05Well, if she's behind on other things,
0:49:05 > 0:49:07we'll have her come in on Monday mornings...
0:49:07 > 0:49:10- Yeah, I think that would be helpful. - Days when we don't have class, OK.
0:49:10 > 0:49:12All right, listen up.
0:49:12 > 0:49:14This is exciting. You guys can go.
0:49:14 > 0:49:16See you later.
0:49:16 > 0:49:19Supporters of online classes say they are a cost-effective
0:49:19 > 0:49:22alternative to traditional teaching,
0:49:22 > 0:49:24providing students more flexibility in their learning,
0:49:24 > 0:49:28as well as access to a greater variety of options.
0:49:28 > 0:49:31But a report by the Organisation For Economic Co-operation
0:49:31 > 0:49:35And Development suggested computers do not improve results.
0:49:35 > 0:49:38When you look at the evidence, and there is very little evidence,
0:49:38 > 0:49:42but what evidence there is shows that this stuff doesn't work.
0:49:42 > 0:49:46It doesn't help children to learn. It lowers standards.
0:49:46 > 0:49:51Actually, heavy use of technology in education, the results are dreadful.
0:49:51 > 0:49:54Kids are turning off, they're not engaged in the content,
0:49:54 > 0:49:57they get very used to it, and it's not helping them to learn.
0:49:57 > 0:50:01There is something about the human interaction that helps kids learn.
0:50:01 > 0:50:04So not only is the problem false, I would argue,
0:50:04 > 0:50:08the solution is a false narrative as well.
0:50:10 > 0:50:14But this second Industrial Revolution, the robot revolution,
0:50:14 > 0:50:16won't take all jobs away.
0:50:16 > 0:50:20In fact, some economists believe it could create billions more,
0:50:20 > 0:50:24a society of full employment, but what will those jobs be?
0:50:24 > 0:50:26To get a glimpse into the future,
0:50:26 > 0:50:29you need to look no further than the humble car wash.
0:50:33 > 0:50:3515 years ago,
0:50:35 > 0:50:40automated machines on petrol station forecourts started closing down.
0:50:40 > 0:50:44The Petrol Retailers Association say that, since the mid-noughties,
0:50:44 > 0:50:48the numbers have halved from 9,000 to just over 4,000.
0:50:50 > 0:50:53Instead of using machines at garages,
0:50:53 > 0:50:56we use humans to wash cars again.
0:50:56 > 0:50:58It was an odd regression -
0:50:58 > 0:51:02a shift towards a low pay, low productivity trap.
0:51:03 > 0:51:07This is a story of a political deal and how it ensured that
0:51:07 > 0:51:10we did the hard work, not robots.
0:51:14 > 0:51:19It's 2003, ten new countries are about to join the EU,
0:51:19 > 0:51:21eight of them from the old Eastern Bloc -
0:51:21 > 0:51:24poor countries, by Western standards.
0:51:24 > 0:51:27The Home Secretary at the time, Lord Blunkett,
0:51:27 > 0:51:30is about to sign a deal with the EU.
0:51:30 > 0:51:31The question on the table is,
0:51:31 > 0:51:35how many workers from those countries might come to Britain?
0:51:35 > 0:51:37We were uncertain.
0:51:37 > 0:51:40We were sceptical about whether we could make predictions.
0:51:40 > 0:51:44People were used to the idea of free movement and what we were trying
0:51:44 > 0:51:47to address, was given free movement,
0:51:47 > 0:51:51should we allow these people to work legally, pay National Insurance,
0:51:51 > 0:51:54pay tax, or should they work in the sub-economy?
0:51:57 > 0:52:02Britain had a choice - open up its labour market to everyone,
0:52:02 > 0:52:05or temporarily restrict the numbers.
0:52:05 > 0:52:09The job of estimating how many might come to live and work in the UK
0:52:09 > 0:52:14fell to migration expert Professor Christian Dustmann.
0:52:14 > 0:52:18We started that report early in 2003, where we tried
0:52:18 > 0:52:23to predict the number of people who would migrate after 2004.
0:52:23 > 0:52:27We're talking about all the Eastern European countries joining the EU.
0:52:27 > 0:52:29No-one has any idea what this is going to be.
0:52:29 > 0:52:34So we tried to do the best on the evidence that we have.
0:52:34 > 0:52:39Our number was about 13,000 would come to the UK every year.
0:52:39 > 0:52:44The report, however, was based on a very specific scenario,
0:52:44 > 0:52:47that Germany opens up its labour market
0:52:47 > 0:52:50to Eastern European migrants as well.
0:52:50 > 0:52:53Based on Dustmann's estimates,
0:52:53 > 0:52:56the British government did a deal with the EU -
0:52:56 > 0:52:59Britain decided to allow everyone in,
0:52:59 > 0:53:01but then Germany changed its mind.
0:53:02 > 0:53:06Finally, on the 1st of May, it was the UK,
0:53:06 > 0:53:10Sweden and Ireland among the European -
0:53:10 > 0:53:13existing European countries - which actually opened
0:53:13 > 0:53:18the labour market to those new accession countries,
0:53:18 > 0:53:21and not Germany. So, at that point,
0:53:21 > 0:53:25the scenario we had modelled was, of course, not the scenario any more.
0:53:25 > 0:53:30Germany had opted to temporarily restrict its borders to migrants,
0:53:30 > 0:53:32so they looked to Britain instead.
0:53:32 > 0:53:3513,000 a year didn't come here -
0:53:35 > 0:53:40it was closer to 40,000. Critics asked how the government
0:53:40 > 0:53:43could so badly miscalculate the numbers.
0:53:43 > 0:53:47The 2003 Dustmann report is predicated on one thing -
0:53:47 > 0:53:51Germany opening up in the same way as we're going to open up
0:53:51 > 0:53:53and Germany doesn't. It has...
0:53:53 > 0:53:57It closes down and it says it's going to close down for seven years.
0:53:57 > 0:53:59Look, let me be very straight.
0:53:59 > 0:54:03I've defended the decision, but I was culpable on two fronts.
0:54:03 > 0:54:08One, I didn't really clock the significance of the decision
0:54:08 > 0:54:12that Germany was taking, even though I knew about it.
0:54:12 > 0:54:19And secondly, I was quite prepared, as politicians so often are,
0:54:19 > 0:54:25to use the cover of the statisticians' analysis -
0:54:25 > 0:54:28guess, if you want - to provide us with a comfort zone
0:54:28 > 0:54:32while we were implementing the early part of the registration.
0:54:32 > 0:54:37We were quite happy to ride with the public understanding that this
0:54:37 > 0:54:41wasn't going to be a major influx and, of course, it was.
0:54:44 > 0:54:51At least 850,000 people - around 3% of the UK working age population -
0:54:51 > 0:54:57migrated from Eastern Europe to the UK between 2004 and 2011.
0:54:57 > 0:55:00You were hung out to dry, basically, by politicians.
0:55:00 > 0:55:04They came and said, "You've got this completely wrong.
0:55:04 > 0:55:06"You've wildly underestimated the figures.
0:55:06 > 0:55:11"A massive, catastrophic miscalculation by you".
0:55:11 > 0:55:15Nobody read the report, I think. Not many people read the report.
0:55:15 > 0:55:19It's a Sliding Doors moment, David, isn't it? One way or another.
0:55:19 > 0:55:23- Well...- You said, in a candid way, which is very rare for a politician,
0:55:23 > 0:55:27- "We got it wrong". - Well, we got that part of it wrong.
0:55:27 > 0:55:33But there's no question looking back that it was a very fine line
0:55:33 > 0:55:38as to whether we were reducing future productivity levels,
0:55:38 > 0:55:42whilst actually creating and holding on to very high employment.
0:55:42 > 0:55:46These are very fine decisions and sometimes you get them wrong.
0:55:50 > 0:55:55The effect of EU migration on the UK's labour market is the subject of
0:55:55 > 0:55:58a highly political and frequently polarised debate.
0:55:58 > 0:56:03More than one in five low-paid jobs in the UK is now occupied by a
0:56:03 > 0:56:04low-skilled migrant worker.
0:56:06 > 0:56:09What appeared at the time to be a decision benefiting the economy
0:56:09 > 0:56:15became a political negative with the downturn of the economy post-2007.
0:56:17 > 0:56:21The future for work now in an age of austerity and automation
0:56:21 > 0:56:25will be for us all to find our own opportunities.
0:56:26 > 0:56:29I'm interested in the future of the United Kingdom and United States,
0:56:29 > 0:56:31and I'm interested in the face of robotics and AI as
0:56:31 > 0:56:34how we're going to create jobs. What I'm saying to you is,
0:56:34 > 0:56:37"If you can't do it, artificial intelligence can".
0:56:38 > 0:56:42I look at it as technology providing opportunity.
0:56:42 > 0:56:43Technology giving us...
0:56:45 > 0:56:48..liberating us to do more of what we want, the way we want to do it.
0:56:48 > 0:56:51Of course, there's also the fear side of that, right?
0:56:51 > 0:56:54Which is, "Am I going to lose my job?" We have to think about it.
0:56:54 > 0:56:58We have to participate. You just don't let the machines take over.
0:56:58 > 0:57:01Be responsible in thinking about how you apply this technology,
0:57:01 > 0:57:06how do you faze it incrementally, and so forth.
0:57:06 > 0:57:09This great push towards robots, this is the future,
0:57:09 > 0:57:15and it somehow takes away any agency and any political democratic process
0:57:15 > 0:57:18because this is the imaginings of Silicon Valley.
0:57:18 > 0:57:23Small government, gig economy, the Uberification of jobs -
0:57:23 > 0:57:25that's kind of what they want.
0:57:25 > 0:57:28But I would counter that with, "Well, is it what WE want?"
0:57:30 > 0:57:35And so, the fate of the automated car wash becomes one model of
0:57:35 > 0:57:39how work could go - machines replaced by low paid workers
0:57:39 > 0:57:42motivated only by the fear of unemployment.
0:57:42 > 0:57:46Could this be the future of work?
0:57:46 > 0:57:50Watson, in a control centre, an unblinking eye,
0:57:50 > 0:57:54monitoring how many cars you and I wash in an hour.
0:57:57 > 0:57:59To explore further how digital technologies
0:57:59 > 0:58:04are transforming societies, go to the BBC web page on-screen
0:58:04 > 0:58:07and follow the links to the Open University.