The Augmented Enterprise and the resulting consequences

Today’s TextileFuture Newsletter is illuminating the augmented enterprise and the consequences resulting. It is based on a report by Mind Foundry, a portfolio company by the University of Oxford. Some facts might be a “repetition” of the Newsletter of last week, but the thoughts will continue to work in your mind, this is why we publish it. In order to offer a third party opinion, TextileFuture adds a recent feature written by Shelly Palmer, and entitled “AI would not take your Job, People will” with a focus on the workplace situation in the USA.

The item starts here:

This report explores the ongoing relationship between humans and automation, and ways in which machine learning can augment human intelligence, empowering workers to unlock new human possibilities.


Data science as a means, not an end

Digital technology presents huge opportunities for business leaders, but must be viewed as a useful tool, rather than an outcome in itself.

By guest author Oliver Pickup (major contributor, unless specified differently), who is multi-award-winning journalist, who specialises in technology and business and contributes to Times, The Telegraph, Guardian, and Financial Times.

The digital universe is doubling in size every two years, according to International Data Corporation, and, it will reach 44 trillion gigabytes by 2020. Against this backdrop, the pressure is on business leaders to embrace data science and take advantage of the technologies offered by artificial intelligence (AI) and the internet of things (IoT).

Big data and analytics provide a method for converting huge data volumes into next- level insights. However, rather than being a “silver bullet” – and despite the confusing and misleading hype around AI in particular – data science must be viewed as an essential tool to solve business challenges, and not a final outcome in itself.

Data science is hugely valuable if used correctly, but business leaders should take time to determine whether – and why – it should be embraced by their organisation. Clare Barclay, chief operating officer of Microsoft UK, advises firms to focus on small improvements, or “low-hanging fruit”. She says: “If you start thinking of the really big things, you do nothing. Ask yourself: ‘What is the problem I am trying to fix?’”

Manjit Johal agrees. The co-founder and chief technology officer of AVORA, a Lon- don-based company that offers business intelligence and machine learning as a service, acknowledges that it can  be  easy  for firms to get lost in a sea of data when it comes to data science, focusing too much on the algorithm or model and losing sight  of the actual business goal.

“As with any new initiative, it’s always best to start with the end in mind, so that you don’t get distracted along the way,” he says.

The C-suite should neither be overawed by data science nor “think of it as something new”, suggests Wael Elrifai, vice principal for solution engineering, big data analytics and Internet of Things at Hitachi Vantara. Business leaders have always used past information to optimise decision-making and deter- mine the future of their businesses, he says. The point is that these prediction methods are advancing.

“It means that existing business processes can be optimised, and they can predict things they weren’t previously able to,” he says. “Other times, these insights can define entirely new business processes, such as shifting to predictive maintenance rather than fixed scheduling.”

Maureen Norton, global data scientist profession lead at IBM, says business leaders should use data science to generate insights that help them keep up with competitors. However, they must take care not to ignore their most valuable asset: their employees.

We are in an era of business reinvention and organisations are facing unprecedented convergence of technological, social and regulatory forces,” she says. Those unfamiliar with the power of data should read Michael Lewis’s “Moneyball: The Art of Winning an Unfair Game”, she says, or watch the 2011 Brad Pitt film based on the book.

While we hear more about augmented and cognitive enterprises – and the AI technologies that power them – people remain the most important aspect, Ms Norton says.

“Keeping human factors front and centre is an essential part of organisational culture.”

Robots are not going to take all of our jobs. Data science allows man and machine to work together,  with  AI  and  IoT  helping to remove dull, automatable tasks. Still, many workers will have to evolve and upskill to make the most of the mushrooming number of opportunities presented by tech advancements.

To maximise benefit, Ms Barclay recommends educating staff across the organisation on science and associated technologies; this can help catalyse a successful digital transformation. “By involving employees, you’re culturally engaging with change and you are equipping them with new skills.”

Ms Norton emphasises the value of data science as a vehicle for optimising the work- force, not as the ultimate goal.

“New technologies and corporate architecture enhance both client and employee experiences and provide the  insights  to spark creativity and engagement,” she says. “That in turn raises the expectations for personalised services that include human inter- action and empathy, which are attributes that, in 2019, differentiate a company from its competitors.”


Organisations must educate employees on the opportunities presented by data, rather than simply hiring specialists with new-fangled titles.

If data is the new oil, business leaders know they must siphon off value. Yet a woeful lack of knowledge at C-suite level and a lack of skilled workers have fostered an alarming number of unsuccessful – and costly – data science and artificial intelligence (AI) projects, according to experts.

Gartner estimated that in 2017 some 60 % of big data programmes would fail to get beyond the pilot stage. The analyst firm’s Nick Heudecker updated the calculation in November that year, writing in a post on Twitter – since deleted – that it was “too conservative” and the figure was, in fact, closer to 85 per cent. Little surprise, then, that McKinsey has identified the relatively new role of analytics translator as one of the most sought-after positions for data analytics. The firm says that translators play a critical role in bridging the technical expertise of data engineers and data scientists with the operational expertise of frontline managers in marketing, supply chain, manufacturing, risk and other areas.

“Data science should be seen in the business not just as a reactive service but as a source of new ideas that can help drive innovation,” says Jonathan Clarke, statistical modelling man- ager at LexisNexis Risk Solutions. “To do this, it’s vital that the analytics team can clearly describe their function and solutions to the wider business. But this is not an easy task and is a skill that often sits outside the junior scientist’s toolkit,” he says. While translators can fulfil this function, hiring them is often proof, that the data science team is not properly integrated into the business.

Mr Clarke raises an important point: is reg- ularly hiring talent with new-fangled titles to grapple with data the correct approach, or is it in fact myopic?

“It is bureaucratic overkill – if a translator  is needed, then team dynamics have already broken down,” argues Dr Al Hartmann, chief scientist at cloud security software company Ziften. “Role proliferation is not the answer. Keep teams small, agile, and well connected.”

David Gonzalez Martinez, head of big data for Vodafone Business, agrees that employees at every level should be encouraged to analyse data. Given the integral role that data plays in business in today’s economy, everyone needs to understand it, even at a basic level, he says.

“The entire C-suite has to provide support, ensuring that their area is primed to harvest robust data and that stakeholders understand the value of using it in conjunction with analytics and AI.”

Business units need to use this data through- out the organisation to realise the benefits it can deliver, he says.

“Only with this more democratised approach will individual teams begin to adapt their behaviour.”

A long-term strategy focused on distributing data excellence across the organisation is critical to future success, says Miguel Milano, president of Salesforce in Europe, the Middle East and Africa.

While companies could pay a premium to acquire talent, this would be a short-term solution. Even if they are happy to bear the costs and could find people with the right skills, “it will not help in a few years when those new skills are no longer needed”, he says.

“As new roles emerge and skills requirements change, the size of the existing pool of

skilled workers just isn’t going to be big enough to meet demand,” Mr Milano explains. “Companies won’t simply be able to fall back on hiring new employees as they attempt to future- proof their workforce. To address the problem, companies must invest more in enabling their workforce to reskill – starting now.”

Mr Milano heralds the move to democratise data science, with the introduction of pre-built algorithms and open-source machine learning libraries helping “non-experts” handle data and deliver insights. He points to a raft of (often free) online courses on offer from Coursera, Udemy, and Stanford and Harvard universities, among many others.

There is anurgent needfor amassive increase in tech skills among the working population, Mr Milano warns business leaders. Companies cannot solely rely on conventional educational approaches, whether through schooling or arduous qualifications.

“We need to create ways of learning that are accessible to employees  and  that  can  adapt to  a  fast-changing  digital  landscape, instead of  rigid  curricula  and  time-consuming  programmes,” he says. “It’s time to change the way people think about their careers, building an understanding that reskilling is a viable and indeed a necessary option.”

The entire C-suite has to provide support, ensuring that their area is primed to harvest robust data and that stakeholders understand the value of using it in conjunction with analytics and AI, states  David Gonzalez Martinez, Head of big data, Vodafone Business.

An augmented workforce: the AI advantage

Machine learning and AI are making big inroads into the workplace, offering numerous advantages for employees.

If past headlines were to be believed, hordes of us should be out of a job by now. Back in 2013, indeed, one expert claimed in a national newspaper that clerical jobs would have disappeared completely by 2018.

That clearly has not happened. Machine learning and artificial intelligence (AI) are indeed making big inroads into the workplace, but with a very different end result. Staff members are not so much being replaced as augmented, given extra capabilities that enable them to work more efficiently and intelligently than before.

In the field of data science, workers generally spend upwards of 80 % of their time cleaning and preparing data, which can be very manual and repetitive, and only the remaining 20 per cent on the more interesting aspects of model building and analysing results. With the right tools, however, repetitive tasks can be auto- mated, freeing up time for work where human expertise is very valuable. People then get the chance to spend more time on what they really want to do: digging deeper into meaningful data.

Choosing a model

Choosing the right machine learning model is another time-consuming task. It usually means trying out a number of different possibilities, often based on what the individual data scientist knows and prefers. With automated machine learning tools, however, it is possible to leave that selection process to the machine, which not only can do the job better, but in a fraction of the time. The right tools can also make it possible to analyse far more dimensions of data than ever before. Human beings are generally very good at spotting patterns, but where there are multiple variables this ability can only go so far.

Deluged by data

Machine learning can work with hundreds of dimensions. This is where it really becomes pos- sible to see relationships in data that our brains have no chance of understanding, and to extract meaningful insights. Imagine looking at a graph to explore your data. In two dimensions everything makes sense. In three it gets complex, but it’s possible. How do we look at data that has 200 dimensions?

It reminds me of when we were using machine learning to detect the spread of malaria. In a project between Royal Botanic Gardens Kew and Oxford University, called HumBug, we developed a real-time detection system based on a phone app that aims to detect malaria-carrying species of mosquito by the sound they make. This data is combined with information on associated envi- ronmental factors drawn from high-resolution remote imaging: the composition of local vegeta- tion, the distance to water and so on. It’s a huge amount of data, but thanks to machine learning, it can all be analysed to produce detailed, real-time maps of the spread of malaria that can help with the targeting of control programmes.

Endless possibilities

Expanded capabilities like these make the role of the data scientist far more sophisticated than before. Once people get to grips with the possibilities, they come up with novel applications.

Sometimes these are not even work related. One Mind Foundry client has collected over ten years of data on the performance of his children’s foot- ball teams, and has so far only analysed it manually. Now he would like to use our platform to gain predictive insights that can help improve their performance.

While analysing kids’ football scores would not do much for one’s employment prospects, the skills gained from these new tools certainly do. This is probably one reason why we find that so many workers are keen to learn more about how the machine interprets the data and draws patterns from it. We make the processes as transparent as possible, exposing all the parameters, so that the user can see how the decisions are made. As a spin-off from Oxford University, we have access to a great deal of cutting-edge research, which we always include in our training programmes.

There’s a great deal of evidence that, far from stealing people’s jobs, AI and machine learning simply make those jobs more satisfying. In a recent survey by the Chartered Institute of Personnel and Development (CIPD) and PA Consulting, 43 % of workers at two companies using AI felt they were learning new things, while a third said they were doing more interesting tasks. Similarly, a study this spring by the Japan Science and Technology Agency found a clear pat- tern: the more that new information technologies like AI are adopted, the bigger the increase in worker job satisfaction. This really should not be a surprise. When AI is acting as a super-capable assistant that frees you up for more insightful work, why would not you feel satisfied?

Even small organisations can benefit from machine learning. Ask yourself: are your staff wasting time on routine tasks, rather than focusing on what they do best? How much more could they achieve when they are free to spend time on more strategic thinking that drives your business forward?


Taking data science beyond the black box

Automated black box systems can help make sense of vast quantities of data, but there are pitfalls for companies here will be 50 billion devices connected to the internet by 2020, according to Microsoft, while data volumes will be 50 times higher than in 2016. With such colossal – and rising – data resources, the gulf between supply and demand for data scientists and analysts already seems unbridgeable.

“If it is true that data is increasingly changing the world that we live in, it is also true that there is a shortage of individuals to make sense of that data,” says Dr Iain Brown, head of data science for SAS in the UK and Ire- land. “As a result, some businesses are turning to automated black box systems to make informed decisions from complex analytical data, replacing people with machines.”

Wael Elrifai, vice principal for solution engineering, big data analytics and Internet of Things at Hitachi Vantara, says: “The dark arts of data science have been an element of this industry  since  its  inception.  The tricky part is, that some types of data science – deep learning in particular – are virtually impossible to understand in any detail by their very nature, not only to the layperson, but even to the experts.” This means that while it is possible to understand the “what” that artificial intelligence (AI) might present to the user, the “why” remains elusive.

That elusiveness raises many concerns, especially in those fields in which training or learning from past performance can build in biases, around race or gender, for example. “This is a very human problem, and it will take humans to resolve it,” says Mr Elrifai. “And unfortunately, there aren’t any quick-fix magic potions available.”

Organisations that fail to either understand or display the workings of big data programmes face dire consequences, especially since the introduction of the European Union’s General Data Protection Regulation (GDPR) in 2018. Non-compliant businesses could be fined up to EUR 20 million, or 4 % of annual global  turnover  –  whichever is higher.

It is important to bear in mind these potential business-limiting consequences before embracing off-the-shelf AI products or taking shortcuts thanks to the recent push to democratise data science. While algorithms, and open-source machine learning libraries can be used to create automated, “hands-free” AI solutions and produce short-term results, it can be a dangerous game, says Dr Brown.

“Organisations are forced to rely on the black box without understanding the logic behind its decision making, and they could risk reputational damage when things go wrong. GDPR requirements state, that organisations must be able to demonstrate how a decision about a particular customer has been reached, so there must be clear data lineage and explainability to meet compliance requirements.”

Dr Brown says the acronym FATE – fairness, accountability, transparency and explainability – “must be the cornerstone of ethically governed models. Without this, organisations are potentially opening themselves to governance and control risks. Data science should not be seen as a dark art, nor should its produce be completely ‘black box’”.

Paul Henninger, senior managing director at FTI Consulting, says that if data science seems like a black box to an organisation, they have a problem.

“No one should ever do something that comes out of a machine just because the machine is using a fancy algorithm,” he says. “Patterns and probability need to be put into a business context in a way that decision makers, leaders and consumers understand. If a data scientist hasn’t been able to explain how the pattern works, or why it is a pattern, a business will never fully realise the benefits of the change it creates.”

Organisations that move beyond the black box of data science, and show transparently, how their data models are validated, are likely to gain favour with customers and, in turn, boost profits, according to Microsoft UK’s “Maximising the AI Opportunity” report, published in October.

Clare Barclay, chief operating officer at Microsoft UK, encourages business leaders to establish a clear set of ethics, commitments and values around the use of AI.

“In other words, do not ask what AI can do, but what it should be allowed to do,” she put into a business context in a way that decision makers, leaders and consumers understand, she explains. “Not only will this ensure ethically grounded innovation, but it can support your bottom line, too. Microsoft research shows companies that consider what AI ‘should’ do have been shown to outperform those that do not by 9 %.

Evidently, it pays dividends to think outside the black box.

Organisations looking to harness the potential of their data are turning to Mind Foundry’s human augmented machine learning solution to uncover answers to their business questions. Business problem owners use our easy-to-navigate SaaS platform to prepare their data, optimise it for machine learning, generate a model, test it, and then make predictions for business issues such as lead conversion, buyer profile fit, customer churn, credit risk, clinical trial outcomes and more. We co-create an achievable action plan with each customer, prioritising opportunities for applying machine learning to maximise value creation in the shortest time possible.

Mind Foundry is a portfolio company of the University of Oxford, founded in 2015 by Stephen Roberts and Mike Osborne, two of the greatest minds in machine learning research and development. Investors include Oxford Sciences Innovation, the

Oxford Technology and Innovations Fund, the University of Oxford Innovation Fund and Parkwalk Advisors.

AI would not take your Job, People will

By guest author Shelly Palmer, Business Advisor and Technology Consultant

Machine intelligence, also known as artificial intelligence (AI) is going to have both an awesome and an unfortunate impact on our posterity. Let us explore one possible way AI may impact the future of work, and how it may dramatically change how we train our workforce.

The Graphic Arts Department (A Metaphor for Every Department)

A brand manager needs an advertisement. So, the brand manager sends a brief to the senior art director (in-house or at an agency) and asks for something amazing to be created. On or before the deadline, the brand manager and the art director meet to review the work. The brand manager is presented with three approaches, and after a number of meetings, a number of revisions, and revelations, they agree on a final product.

This is a process that has repeated itself for more than a century, and AI is not going to stop it (today).

After getting approvals from senior management, the art director must execute the work and deliver all of the versions and variations required. These might include a full complement of IAB (Interactive Advertising Bureau) standard ad units for the web, graphics suitable for a video graphics package, newspaper ads (in several sizes), a 30-sheet billboard, a digital billboard in several dozen aspect ratios, full-page and double truck print ads (bleed and no-bleed), and a stylebook so that the package designers and the promotion agency can access the new look and get a feel for product design and in-store signage. A list of deliverables can have hundreds of variations, each requiring subtle compositional changes, revised typography, and resizing of images.

Today, the senior art director hands this project to a group of junior art directors and graphic artists. This work is not tough, it is just tedious, and there is a lot of it. Anywhere from a few hours to a few days to a few weeks later (depending on the length of the deliverables list), the junior art directors submit their finished work to the senior art director for final approval.

The senior art director makes some subtle changes to the deliverables that are not quite right, and the junior art directors are taught why the changes had to be made. Of course, some of the work is perfect, and it takes only a second for the senior director to approve those. All in, a major campaign might take a 10-person art department a week or so to deliver. That’s today.

The AI-assisted Graphic Artist

Now, let us imagine the same process in a slightly different way. In this scenario, the senior art director has an AI co-worker (an AI system designed and trained to version graphic artwork). Instead of harnessing a team of junior art directors to build the deliverables, the senior art director clicks a button and the AI co-worker builds every required deliverable, in seconds.

Unlike handing the work to a junior team and waiting hours, days, or weeks, the work is ready immediately for review. The senior art director will still have to page through each version to give it final approval, and might even have to tweak a few of the versions to get them just right. But, the basic work, the work of 10 junior art directors, will be eliminated – and so will their jobs.

From a fiscal management point of view, the senior art director’s productivity has increased exponentially. The ROI is easy to calculate: remove 10 junior people from payroll who earn between ¼ and ½ of the wages their supervisor earns. The unit will enjoy a three- to five-fold reduction in annual payroll expense, maybe more. This is an excellent path to value creation (for the shareholders).

Some things would not change for a While

The process between the brand manager and the senior art director will remain unchanged for a while. Creativity and collaboration are human traits that have a magical quality. Hits are a mystery. And while this may not always be true, today we rely on inspired, talented, uncompromising humans to create groundbreaking graphic art.

But what about the junior art directors? They will be deprived of the mentorship, remediation, and education an apprentice requires to become a master. In a world filled with senior art directors who are augmented with AI coworkers, junior art directors need not apply. BTW, in this scenario the senior art director doesn’t need to know anything about AI or computer programming; the AI system requires only a click of a button.

You get were this Is Going

Multiply this scenario by every department where productivity can be increased with human/machine partnerships. It is easy to imagine millions of jobs simply being eliminated. Many of them will be people who trained a lifetime to achieve their positions. As each new purpose-built AI system comes online, the work for less productive humans will evaporate practically overnight.

Why This Will Happen

Almost every CEO will insist on using AI to push productivity to its limits. The competitive advantage is too great (if you are first), and, if you are unfortunate enough to be a follower (fast or otherwise), an AI-augmented workforce will become table stakes. Nothing is going to stop this.

What we really need to Figure Out

For me, the most devastating aspect of this future vision is what happens to the farm team. Paying your dues started in the Gilded Age. I would not be where I am were it not for a series of amazing mentors, role models, and masters whom I have been lucky enough to work for. For all practical purposes, my capabilities are directly attributable to my teachers (academic and professional). If our hypothetical senior art director does not need junior art directors, or if the corporation will not allow the hiring of humans who are not expert AI co-workers, how will the junior art directors learn enough to become senior?

The Logical Conclusion

If you follow this hypothesis to its logical conclusion, at some point, the senior art directors are replaced by AI systems that have seen enough awesome graphic art to create artwork that is “good enough” from scratch. To be fair, the vast majority of work product created by journeymen across all disciplines is not at a “masterpiece” level.

I like to think that the full replacement of creative workers and production workers is still far away, but I know differently. Look at the cab drivers around the world protesting ride-sharing services. They are fighting for their livelihoods. What kind of world will we live in if their actions are mimicked by displaced workers in every field, from every walk of life?

What You Can Do about It

There is only one thing you can do about this. You must become the very best AI coworker you can become. No matter how smart you think you are, there are things a well-trained AI system can do better than you can. It can look at more data, it can see the world differently, it can augment your capabilities.

How do you become a best-in-class AI coworker? Start by learning to use the existing tools, inject yourself in the process, and become a lifelong student of your art and the AI tools at the cutting edge of your profession.

This approach may not save your human co-workers, but it will position you to survive and prosper in our exponentially changing world.

Shelly Palmer is a business advisor and technology consultant. He helps Fortune 500 companies with digital transformation, media and marketing. Named LinkedIn’s Top Voice in Technology, he is the co-host of “Think About This with Shelly Palmer & Ross Martin” on the Westwood One Podcast Network. He covers tech and business for Good Day New York, writes a weekly column for Adweek, is a regular commentator on CNN and CNBC, and writes a popular daily business blog.

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