Tag Archives: STEM

Future of Digital Transformation

Senior management have to come to grips with the fact that digital transformation is not an event but rather the operating environment of 21st century business.

Like music, photos, TV, and data, once something becomes digital it becomes a consumable and moves from the domain of the specialized expert to a public commodity. As with Blockbuster, Borders, Capital Records and newspapers, businesses based on non-digital product are the hand-crafted hobbies of the 21st century.  Craft markets will exist into the future, but they are generally not profitable and rather a labor of love.

Changing the way we work

Here’s the kicker. Digital transformation is now looking at not just the things we sell, which includes services, by the way, but how we do business. From crowd funding to network marketing to blockchain (how Bitcoin works), the basic principles of how we have traditionally gone about business are changing.

Crowd funding, where a population at large is directly involved in the creation of products, also has ramifications for invention and design. Brainstorming on steroids. Network marketing has wiped out traditional sales channels from cold calling and direct mail to bricks and mortar retailing. And blockchain has the capability to render capital-intensive industries obsolete. What Bitcoin did to money, people are now looking to use to undermine energy, insurance and infrastructure oligarchies. One day, blockchain may even be capable of fixing our political system.

See also: 4 Rules for Digital Transformation  

Understanding Digital Transformation

What really is digital transformation? Gartner, a leading authority on such things, defines digital transformation as “to leverage digital technologies that enable the innovation of their entire business or elements of their business and operating models.

So innovating is not just what we do, but how we do it, our “operating models.” In my last article, “Misunderstanding Innovation,” I wrote on how innovation is not invention but rather the application of invention as a solution to a practical need. As such, innovation is the backbone of digital transformation, just as audit is to compliance or controls are to risk.

Digital Transformation as an Operating Model

Back to my opening statement that digital transformation should not be thought of as an event but rather an operating environment, just as industrialization in the 18th century was not a single event but a period of continual transformation. From the introduction of the weaving loom through production lines to mass production, the transformation fed change that has continued for 200 years.

Senior management have to stop thinking of digital transformation as a passing fad, and embrace the fact that the world has changed.  As in the 18th and 19th centuries, change will drive change, and as the management in those times developed process management models (see, PDCA is NOT Best Practice) to drive the development of automated production, so, too, managers now have to develop transformation models to take account that disruption and innovation will drive further disruption and innovation.

Transformation as a Lifestyle Choice

The fact that you have transformed your operation today is only a temporary reprieve. You need to redefine your business model to be an agile platform continually identifying and innovating to improve end-customer quality of life: That’s your customer’s customer.

Women as the Mothers of Innovation

The current beat-up of getting more women involved in STEM (science, technology, engineering, math) misses the understanding that innovation has at its root, a deep empathy for the quality of life of others. Developing and elevating women’s inherent intuition as to the plight of others will do more to foster innovation than a plethora of inventions. Hundreds of inventions never see the light of day, yet a handful of innovations have changed the world. Again, please re-read my previous article on Misunderstanding Innovation.

If Malcolm Turnbull truly wants Australia to develop an innovative culture, we should be promoting more people into psychology, sociology, anthropology and statistics. These are the strategic vocations of innovation, while STEM and invention are the tactical solutions. Yes, stats is math, but it allows us to understand bias as well as predictive analytics, which identifies and prioritizes targets for innovation.

See also: Why You Need a Digital Leader  

Where to From Here?

Accepting the need to transform your business model is in itself an inherent risk. Just as a window cleaner straps on a safety harness before scaling a building, so having an active risk and compliance system operational is a mandatory prerequisite before embarking on any transformation. You will need systems that alert you to emerging issues and to give you continual insight, throughout the transformation process, without the need to go and look for it. The business graveyard is as full of those who lost their footing on the way as those who did nothing. This is not a shameless plug for what I do but rather the reason I do it.

Beat Brain Drain: Boost Your Talent Pool

Overview

Shifting demographics are starting to reshape the workforce. As baby boomers retire, in most developed markets there will simply be fewer people of working age to fill positions. Not only is the pool of locally available replacement talent shrinking, but competition for their talent is on the rise.

The people shortage is exacerbated by the lack of growth in graduates with science, technology, engineering and math (STEM) degrees. This is happening at a time when, because of rapid advances in technology, the demand for these skills in the workplace is on the rise.

At the same time, businesses are also finding that the leadership and experience of the baby boomers are being sorely missed. As they leave the workforce, baby boomers are taking decades of knowledge with them, while younger generations often have yet to build up the experience and leadership skills needed to maintain successful businesses.

So how can businesses respond to this confluence of demographic and training challenges to avoid being hit hard by a skills shortage that could be even more pressing than the one the world is already facing? With an emerging challenge this great, this is not just an HR issue – it’s core to future business strategy.

In-Depth

How are business leaders coping with the rising demographic, technological and human resource challenges they face as experienced staff retire and new technologies disrupt industries and require new skillsets?

The first step in addressing these challenges is to understand staffing as part of a holistic business strategy. Organizations need to identify the critical skills and roles needed to support overall goals and objectives and build a sustainable talent pipeline.

Aligning talent strategy to business strategy in this time of rapid change requires taking a long-term view. It’s not just about hiring for the positions you need today but about identifying the critical skills needed to help your business adapt over the long term.

How to Tackle an Emerging Talent Shortage

At a time of increasing competition for qualified people, what many of the firms are focused on now is creating a culture that is attractive for people to join and stay with. With as many as a third of employees thinking about leaving their current job within a year, according to Aon’s latest Workforce Mindset Study, this isn’t just about attracting new staff — it could be about preventing existing employees from being lured away by a competitor.

Both mature and fast-growing industries are focusing on developing a culture to help them compete for scarce talent and become more attractive to their current and future workforce.

When it comes to culture, here are a few things organizations can do:

  1. Develop leaders who are engaging and serve as role models — With corporate leaders increasingly high-profile (and with leaders as a top driver of employee engagement), select and develop people who can act as internal and external role models and create an environment in which people are appreciated and motivated.
  2. Establish a clear employee value proposition — To stand out from the employer crowd, think about how to make your corporate culture feel more distinctive and attractive by offering support in career development and continual training, as well as competitive compensation and benefits.
  3. Develop and articulate a sense of purpose — With workers increasingly wanting employment that means something beyond just making money, explaining what your business stands for can be a powerful tool to attract like-minded talent and drive long-term employee engagement.

In addition to culture-building, planning for tomorrow’s workforce is key. Talent shortages are likely to remain a feature for years to come. Ensure your business has the qualified staff and skill sets needed by adopting a long-term program for attracting, training and developing the people who will drive its success over the long term — not just for this year’s needs, but for five to 10 years.

There are a few things organizations should consider doing to help with their long-term talent needs:

  1. Establish apprenticeships — Not only does on-the-job training help you cultivate the skills your business will need, it can help promote loyalty and long-term engagement. With training and career development opportunities as strong pull factors for modern workers — especially from younger generations. Making training a continuing part of your business from the early stages of employees’ careers can be a powerful proof point in your commitment to employee development.
  2. Work with schools — Encourage changes in the educational system that support the development of needed skill sets in the long term. Ensure students are made aware of career opportunities in your industry and of the true value and potential a career with your organization can bring.
  3. Commit to workforce diversity — Women and minorities still have significant under-representation within the managerial ranks of many industries. Organizations should be reaching out to qualified minorities, not just because practicing equality in the workplace reaffirms your business’ commitment to fairness, but because diverse workforces are a proven driver of innovation. Not only have organizations with greater gender equality proven to perform better, but being seen as promoting gender equality in the workplace can be a powerful attractor for talent.
  4. Globalize your hiring — Developing countries around the world are producing well-qualified staffing for accounting, data analysis and other financial services, while in healthcare a majority of newly qualified healthcare professionals (including nurses and general practitioners) are graduating from schools in these countries. In a globalized world, the competition for talent is also increasingly global, so you increasingly need to look where the talent is, not just where you would like it to be.

Talking Points

“Rather than focusing on salaries alone as the cure-all for attracting employees, corporations would be wise to look closely at the wider expectations and demands of their candidates, if they are to draw in the best talent. … While increasing the flexibility of the job offer can provide an effective short-term solution to draw in the best candidates, ultimately even these measures won’t resolve systemic talent gaps that have a significant impact on the long-term health of the business.” – Tara Sinclair, chief economist, Indeed

“The struggle to fill vacancies is holding back growth and opportunities for business, and it is essential that both government and industry work together quickly to identify ways to plug this gap.” – Mike Hawes, chief executive, Society of Motor Manufacturers & Traders

“Companies looking for sophisticated skillsets are starting to look to foster skills and relationships with future employees among high school age students.… If you’re not already thinking five to ten years ahead for your talent needs, you need to.” – Usha Mirchandani, partner, talent analytics, Aon Hewitt

Further Reading

The Robocalypse for Knowledge Jobs

Long-time Costa Rican National Champion Bernal Gonzalez told a very young me in 1994 that the world’s best chess-playing computer wasn’t quite strong enough to be among the top 100 players in the world.

Technology can advance exponentially, and just three years later world champion Garry Kasparov was defeated by IBM’s chess playing supercomputer Deep Blue. But chess is a game of logic where all potential moves are sharply defined and a powerful enough computer can simulate many moves ahead.

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Things got much more interesting in 2011, when IBM’s Jeopardy-playing computer Watson defeated Ken Jennings, who held the record of winning 74 Jeopardy matches in a row, and Brad Rutter, who has won the most money on the show. Winning at Jeopardy required Watson to understand clues in natural spoken language, learn from its own mistakes, buzz in and answer in natural language faster than the best Jeopardy-playing humans. According to IBM, ”more than 100 different techniques are used to analyze natural language, identify sources, find and generate hypotheses, find and score evidence and merge and rank hypotheses.” Now that’s impressive — and much more worrisome for those employed as knowledge workers.

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What do game-playing computers have to do with white collar, knowledge jobs? Well, Big Blue didn’t spend $1 billion developing Watson just to win a million bucks playing Jeopardy. It was a proof of concept and a marketing move. A computer that can understand and respond in natural language can be adapted to do things we currently use white collar, educated workers to do, starting with automating call centers and, sooner rather than later, moving on up to more complex, higher-level roles, just like we have seen with automation of blue collar jobs.

In the four years since its Jeopardy success, Watson has continued advancing and is now being used for legal research and to help hospitals provide better care. And Watson is just getting started. Up until very recently, the cost of using this type of technology was in the millions of dollars, making it unlikely that any but the largest companies could make the business case to replace knowledge jobs with AIs (artificial intelligence). In late 2013, IBM put Watson “on the cloud,” meaning that you can now rent Watson time without having to buy the very expensive servers.

Watson is cool but requires up-front programming of apps for very specific activities and, while incredibly smart, lacks any sort of emotional intelligence, making it uncomfortable for regular people to deal with it. In other words, even if you spent the millions of dollars to automate your call center with Watson, it wouldn’t be able to connect with your customer, because it has no sense of emotions. It would be like having Data answering your phones.

Then came Amelia…

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Amelia is an AI platform that aims to automate business processes that up until now had required educated human labor. She’s different from Watson in many ways that make her much better-suited to actually replace you at the office. According to IPsoft, Amelia aims at working alongside humans to “shoulder the burden of tedious, often laborious tasks.”

She doesn’t require expensive up-front programming to learn how to do a task and is hosted on the cloud, so there is no need to buy million-dollar servers. To train her, you literally feed her your entire set of employee training manuals, and she reads and digests them in a matter of a few seconds. Literally, just upload the text files, and she can grasp the implications and apply logic to make connections between the concepts. Once she has that, she can start working customer emails and phone calls and even recognize what she doesn’t know and search the Internet and the company’s intranet to find an answer. If she can’t find an answer, then she’ll transfer the customer to a human employee for help. You can even let her listen to any conversations she doesn’t handle herself, and she literally learns how to do the job from the existing staff, like a new employee would, except exponentially faster and with perfect memory. She also is fluent in 20 languages.

Like Watson, Amelia learns from every interaction and builds a mind-map that eventually is able to handle just about anything your staff handled before. Her most significant advantage is that Amelia has an emotional component to go with her super brains. She draws on research in the field of affective computing, “the study of the interaction between humans and computing systems capable of detecting and responding to the user’s emotional state.” Amelia can read your facial expressions, gestures, speech and even the rhythm of your keystrokes to understand your emotional state, and she can respond accordingly in a way that will make you feel better. Her EQ is modeled in a three-dimensional space of pleasure, arousal and dominance through a modeling system called PAD. If you’re starting to think this is mind-blowing, you are correct!

The magic is in the context. Instead of deciphering words like insurance jargon when a policyholder calls in to add a vehicle or change an address, IPsoft explains that Amelia will engage with the actual question asked. For example, Amelia would understand the same requests that are phrased different but essentially mean the same thing: “My address changed” and “I need to change my address.” Or, “I want to increase my BI limits” and “I need to increase my bodily injury limits”.

Amelia was unveiled in late 2014, after a secretive 16-year-long development process, and is now being tested in the real world at companies like Shell Oil, Accenture, NNT Group and Baker Hughes on a variety of tasks from overseeing a help desk to advising remote workers in the field.

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Chetan Dube, long-time CEO of IPSoft, Amelia’s creator, was interviewed by Entrepreneur magazine:

“A large part of your brain is shackled by the boredom and drudgery of everyday existence. […] But imagine if technology could come along and take care of all the mundane chores for you, and allow you to indulge in the forms of creative expression that only the human brain can indulge in. What a beautiful world we would be able to create around us.”

His vision sounds noble, but the reality is that most of the employees whose jobs get automated away by Watson, Amelia and their successors, won’t be able to make the move to higher-level, less mundane and less routine tasks. If you think about it, a big percentage of white collar workers have largely repetitive service type jobs. And even those of us in higher-level roles will eventually get automated out of the system; it’s a matter of time, and less time than you think.

I’m not saying that the technology can or should be stopped; that’s simply not realistic. I am saying that, as a society, there are some important conversations we need to start having about what we want things to look like in 10 to 20 years. If we don’t have those discussions, we are going to end up in a world with very high unemployment, where the very few people who hold large capital and those with the STEM skills to design and run the AIs will do very well, while the other 80-90% of us could potentially be unemployable. This is truly scary stuff, McKinsey predicts that by 2025 technology will take over tasks currently performed by hundreds of millions of knowledge workers. This is no longer science fiction.

As humans, our brains evolved to work linearly, and we have a hard time understanding and predicting change that happens exponentially. For example, merely 30 years ago, it was unimaginable that most people would walk around with a device in their pockets that could perform more sophisticated computing than computers at MIT in the 1950s. The huge improvement in power is a result of exponential growth of the kind explained by Moore’s law, which is the prediction that the number of transistors that fit on a chip will double every two years while the chip’s cost stays constant. There is every reason to believe that AI will see similar exponential growth. Just five years ago, the world’s top AI experts at MIT were confident that cars could never drive themselves, and now Google has proven them wrong. Things can advance unimaginably fast when growth becomes exponential.

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Some of the most brilliant minds of our times are sounding the alarm bells. Elon Musk said, “I think we should be very careful about AI. If I had to guess, our biggest existential threat is probably that we are summoning the demon.” Stephen Hawking warned, “The development of full-artificial intelligence could spell the end of the human race.”