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4 ways to future-proof your business

StrategyDriven Managing Your Business Article |Future-proof your business|4 ways to future-proof your businessThe business landscape is highly volatile. New developments both economic and social can have a massive impact on how your business functions. This sometimes makes it seem difficult to plan ahead, exposing you to problems further down the line. Future-proofing a business isn’t as difficult as it sounds, though, and these tips should help.

1. Invest in automation and artificial intelligence

Automation and artificial intelligence go hand in hand and they’re both set to play a big role in the future of business. You can start today by automating simple tasks like answering emails, setting automatic software updates for your network and even your social media profile. Artificial intelligence is everywhere, too, and easy to implement. Chatbots can streamline your customer service department and they can even be used to build mailing lists and work on customer outreach programs. Adopting these technologies early will ensure that your business is well equipped to face the future, whatever it holds.

2. Hire an advisor

No matter your intentions, your knowledge of future tech can never really match up to that of a professional advisor. These people spend every day of their lives learning about how businesses will develop. They specialize not just in predicting business trends, but in telling you how best to leverage them. Advisors like https://www.gioletellier.com/ are well placed to make predictions and ensure that your business never falls behind the curve. An additional benefit of hiring an advisor is that they take the strain off you and your staff. Future-proofing demands an enormous amount of data crunching and analysis. Having somebody else do this for you frees up staff to concentrate their energies elsewhere.

3. Research

Research and development is a key component of any healthy business. Continual innovation ensures a steady flow of ideas that can be transformed into products and cash flow. Future research is slightly different, however. It pays to research what your competitors are doing and how bigger businesses in your sector are embracing change for the better. Keep a continual eye on your customer base, too. Demographics might change as time goes by so it’s important never to lose sight of who you’re selling to. A continuous cycle of research and implementation ensures that you’re never taken by surprise if a sudden change occurs or market conditions shift.

4. Gather data

Following on from the previous point, translate themes into data. If your identify shifts in the market, customer base changes or the mass adoption of a new technology occurs, translate those trends into raw data. This makes change more easily quantifiable and gives you something to compare, month on month, year on year. Data allows you to trace not just the trajectory of your business, but that of the business world as a whole. Too many companies are taken by surprise by sudden market shifts, but if you continue to amass and monitor data, it shouldn’t be a challenge to identify potential disruption before it happens.

How Technology Has Advanced The Cosmetic Industry

StrategyDriven Editorial Perspective Article |Technology|How Technology Has Advanced The Cosmetic IndustryAs we head further into the digital era, the use of technology has continued to revolutionise our lives in so many ways. But how has it changed the medical sector? In this article, we will be providing you with insight into how technology has advanced the cosmetic and medical industry in the last 10 years.

Ai And Computer Technology Has Improved Customisation

When looking at the cosmetic industry, there has been one common problem that several people have faced, finding the perfect shade of foundation that matches their skin. The use of AI and computer technology allows for you to scan the skin and determine the base colour as well as the undertones to find the perfect match.

Video Calls Have Improved The Consultation Process

In addition to the use of computers in-store to find the perfect shade, the world of technology has also allowed for computers to be used in the consultation process. This has been used by several medical practices in the UK as well as those providing a hair transplant procedure in Turkey to speed up the consultation process and limit the amount of travel that is needed to get to and from appointments. This way of communicating with medical staff is only set to escalate soon as we continue to adapt our way of life to accommodate Coronavirus and the social distancing restrictions that have been put in place.

Increased Performance For Cosmetic Procedures

Technology has also allowed for several cosmetic procedures to be completed in half the time that they would have been 10 years ago, with advancements such as those employed by laser skin care clinics enhancing efficiency and recovery times. This streamlined approach, supported by electronic reporting, has significantly decreased the duration of surgeries and facilitated quicker patient recovery. For example, the use of Keyhole surgery as a replacement for several other surgical procedures has helped to limit the risk associated with some of these surgeries as well as improve the overall healing process for their patients. As a result, this has also sped up the process of patients returning to their everyday lives following a surgical procedure.

Virtual Try-On Will Replace The Traditional Samples

The final way that technology has advanced the cosmetics industry is through virtual try on. With the pandemic leading to the removal of makeup testers in stores, many brands have turned to computer technology to provide a virtual try-on to those shopping online. This has benefitted several smaller brands as well as larger companies such as Charlotte Tilbury as this is capturing not only the digital market but providing a new experience for customers to try when they return to physical stores. This is a huge benefit for so many companies as it provides them with an experience that they have never had before.

With so much to change in the next five years, there are several ways that this is set to continue in 2021. How do you think that this will continue to change in the not too distant future?

The Feeling Economy and Customer Empathy

StrategyDriven Customer Relationship Management Article |Artificial Intelligence|The Feeling Economy and Customer EmpathyArtificial intelligence (AI) and automation as a workforce disruptor is a genie out of the bottle. The Brookings Institute, a little more than a year ago projected about 25% disruption of the U.S. workforce – about 36 million jobs – in the coming decades. But at the same time, the needle also is moving on A.I.’s transformation of how businesses and their customers interact.

To give this collective shift more context, AI has moved from replacing jobs associated with inspecting equipment, manufacturing goods, repairing things to replacing humans in thinking tasks–the likes of data dives and calculations. The shift originated in the Industrial Revolution and gave rise to the current “Thinking Economy.” Just as the industrial revolution automated physical tasks by decreasing the value of human strength and increasing the value of human cognition, AI taking over thinking tasks is further reshaping the landscape and ushering in a “Feeling Economy.”

AI in this Feeling Economy is doing more of the ‘brain’ work. Subsequently, humans increasingly are handling the ‘heart’ work, including social interaction, emotion recognition, nuanced communication and genuine care for customers. In the workplace, the feeling tasks of jobs – communicating with co-workers and clients, selling to or persuading others, and building and maintaining interpersonal relationships – are more important than the thinking tasks of jobs.

The rapid proliferation of “thinking AI” also is significantly transforming the goods and services marketplace. The consumer interface to the business often is AI-driven.

Online-connected consumers with smartphones can tap digital assistants — from Apple’s Siri, to Google Assistant, to Amazon’s Alexa, Samsung’s Bixby, and Microsoft’s Cortana — to answer questions, order supplies and control home electronics among other capabilities, some of which have not even been thought of yet. As time goes by, as digital assistants become more understanding of such things as context and can do a better job of personalization. GPS navigation systems, such as Waze and Google Maps, simplify the difficult navigation task of finding destinations, even if the consumer has never been to those destinations before.

The machine-to-machine transactions — consumers purchasing via the likes of Amazon Prime through Amazon’s website or app, for example — leaves the emotional connection largely to humans. To match the emotionality of the consumer, the customer-facing personnel must become more empathetic, which in turn makes the consumer even more emotionally driven – requiring greater feeling intelligence on the part of the business.

Further consider the case of the customer service representative, whose easy, repetitive tasks like providing information and making appointments are being taken over by A.I. In this context, a consumer with a non-routine problem is much more likely to be emotionally involved, and the service person to whom AI escalates the problem will need to be much more empathetic than the traditional customer service person. The emotionality of the consumer forms a feedback loop: the consumer is more emotional, so the business must become more emotional, which makes the consumer even more emotional, and so on.

In our new book, The Feeling Economy: How Artificial Intelligence Is Creating the Era of Empathy, we describe a real-life scenario reflecting the thinking-to-feeling transition happening in customer service:

A recent doctoral graduate, an African-American man named Jared, was trying to buy a car. He started out with one salesperson, who took a more thinking-oriented approach. This was a good match for Jared, because PhDs are among the most thinking-oriented people on Earth. The salesperson, being good at his job, was trying to match Jared’s interaction preferences. Unfortunately, Jared was then passed off to an African American salesperson, no doubt to try to match Jared’s cultural background and ethnicity. This salesperson, knowing that business needs to be more emotional as time goes by, tried an emotional approach with Jared, calling him “my Black brother,” and using other emotional appeals. Such an approach will work the vast majority of the time as consumers become more emotionally driven. For Jared, though, it was not what he needed. The one thing we know, however, is that there will be fewer and fewer thinking-oriented consumers like Jared.

As thinking AI is making consumers more feeling-oriented—from their product expectations to their everyday life—companies can take advantage of this trend by tailoring sales, marketing and service to meet the needs of these increasingly emotionally-driven buyers.


About the Authors

Roland T. Rust is Distinguished University Professor, David Bruce Smith Chair in Marketing, and founder and Executive Director of the Center for Excellence in Service at the University of Maryland’s Robert H. Smith School of Business. An award-winning scholar, he has edited several major journals and consulted with American Airlines, AT&T, Dupont, Eli Lilly, FedEx, Lockheed Martin, Microsoft, NASA, and Sony, among many companies worldwide. Ming-Hui Huang is Distinguished Professor in the College of Management at National Taiwan University. A Fellow of the European Marketing Academy, she also is International Research Fellow of the Centre for Corporate Reputation at the University of Oxford, UK, Distinguished Research Fellow of the Center for Excellence in Service at Maryland Smith and incoming Editor-in-Chief of the Journal of Service Research. Their book, The Feeling Economy: How Artificial Intelligence Is Creating the Era of Empathy (Springer International Publishing; January 2021), can be found at https://www.amazon.com/Feeling-Economy-Artificial-Intelligence-Creating/dp/3030529762.

Are CEOs Really Necessary Anymore?

StrategyDriven Editorial Perspective Article |CEOs|Are CEOs Really Necessary Anymore?It seems like a ridiculous question to ask, somewhat like wondering whether cars really need drivers. Just imagine all the things a driver does every second in order to reach a specific destination: taking in vast amounts of inputs about current conditions of the vehicle’s motion, receiving thousands of changing data points from all the visual clues about lanes, traffic, signs, pedestrians and all the other moving vehicles in the vicinity, then comparing all this information to a previously set route, and making all the complex choices necessary to arrive safely.

You could almost think about that driver as being on the receiving end of a firehose of data, sorting out the most important patterns, and then turning all of that into a best course of action — the very definition of Intelligence. And that’s why we’ve come so close to going from data that one human can process, to Big Data, which requires dozens of sensors to process.

With increasingly vast bodies of knowledge about experiences, one can see how business Intelligence, with enough computing power, became Artificial Intelligence. And, so, before too long, the taxi you’re about to hail in Phoenix, shows up; Poof! No driver necessary.

Which brings us back to those folks in the corporate driver’s seat — the CEO. Doesn’t much of a CEO’s job consist of being on the receiving end of ever-increasing floods of data that can now be gleaned in real time from inputs around the globe? The tick of every sale quickly contributes to a pattern revealing how the marketplace is receiving our products at every given moment. Supply chains are linked to these inputs, as is every other variable the CEO needs to be concerned about, from available corporate resources to stock price.

And as AI begins to make choices based on mining Big Data, the role of the CEO as patchcord between data input and decision output seems destined to become smaller and smaller until, at some point, an organization is going to run autonomously. As futurist Ray Kurzweil observed in 2005, in the near future, machine intelligence is going to exceed human intelligence. He named that moment, the Singularity. Will there be a moment when the Singularity arrives in the C-suite? It seems inevitable.

AI or Human Agency?

Or maybe not. Maybe great organizations are not really machines, like some automobiles or even spacecraft, that can complete their journeys without human intervention. To find out, it may be worthwhile to make some sharp distinctions between what Big Data driving AI can do, and what it cannot. BDAI (for short) is excellent at making sense out of the current state. It’s also pretty good at making predictions about trajectories, given no black swan or other -unforeseen circumstances. So BDAI is pretty useful for management to be able to see where we are and where we might be headed.

But, what about agency, or intentionality, or what today we generally call strategy? If we have enough past information of competitive successes and failures, BDAI is capable of helping leaders develop options. In some instances, in a large consumer products organization, for example, it is not difficult to imagine letting BDAI decide the optimal number of versions of a toothpaste brand, which will maximize performance in the marketplace, and even continue to optimize those decisions over time.

Yet, what happens when there is a genuine disruption in a marketplace, when new inventions shuffle the whole deck? If BDAI had been in place at Olympus Camera on the day that Steve Jobs introduced the iPhone, would the company’s management information system have warned leadership that the pocket camera industry, at that moment, was entering an irreversible swoon?

CEO’s Role- Wisdom and Innovation

Finally, we come to the two basic responsibilities that a CEO can perform that, as yet, BD and AI together cannot. The first is to make wise decisions over time that express a coherent vision. The second is to lead innovation. Famously, Steve Jobs had no interest in market research when imagining where Apple needed to go next. He thought in broad terms about what human beings might do with powerful new tools, and went about creating them. Sometimes, it took a while for people to get what Jobs was giving them, but eventually, he re-ordered the world.

Same for Elon Musk. Musk’s long arc in guiding Tesla from highly-ignored sports car, which financed the luxury Model S, which, in turn, made possible the 3, is now crushing an entire global industry. And, underneath it all, still not widely-perceived, is that Musk is also transforming the global electrical grid with a complete infrastructure of vast battery capacity.

Jobs, Musk and other disruptive founders built their organizations to maximize the value-creating potential of their visions. Those organizations are no less than the living, breathing manifestations of their founders’ identities and are as unique as the founders themselves.

After the Founder

Once the founders have departed, subsequent leaders, in order to maximize the quality of their decision-making, will always need to be aware of the identity that still pulses at the heart of their organizations. Without this essential understanding, the dangers are ever-present that the easy persuasiveness of Big Data, married to the seemingly incontrovertible direction supplied by Artificial Intelligence will, eventually, lead even the most successful organization astray.

So, are CEOs really necessary anymore? Yes, if they realize that their main job is to ensure that the identity of their institutions provides the center of gravity around which Big Data and AI are reliably deployed. Otherwise, companies are in peril of becoming driverless, autonomous vehicles, subject to an uncertain future fraught with potentially lethal hazards.


About the Author

StrategyDriven Expert Contributor | Gerald SindellGerald Sindell is a partner of The Identity Dynamics Institute. He was the CEO of two New York publishing companies, Tudor and Knightsbridge. He has been instrumental in developing enterprise operating systems for EOS Worldwide, Accenture, and The Balanced Scorecard Institute.

The Value of the Human Mind – How Machine Learning is Helping Humans Win

StrategyDriven Innovation Article | The Value of the Human Mind - How Machine Learning is Helping Humans Win

Overview

There is no doubt that we are living in the AI era. Artificial intelligence is at work all around us today. Even if we do not realize it, our thoughts and actions are training the technology to respond in the way we desire. Machine learning is one of the fundamental tasks of AI. Just as the name implies, the machines and platforms we use daily are learning from the consistent input we provide. Let’s look into ways that machine learning is helping to make our lives much easier.

What is Machine Learning?

Machine learning is an AI component that uses algorithms to find and apply data patterns. The process involves the input of data into a model that is used to predict an outcome. The more data that is input into the model, the “smarter” the machine application seems to get.

The data used can take on many forms, such as text numbers, images, videos, clicks, etc. If there is a way that the item can be stored, it can be applied to a machine-learning model. There are a variety of ways that machine-learning algorithms are incorporated. These various algorithms fit under three specific types of machine learning.

StrategyDriven Innovation Article | The Value of the Human Mind - How Machine Learning is Helping Humans Win

Types of Machine Learning

The three types of machine learning are supervised learning, unsupervised learning, and reinforcement learning. Deep learning is the refined form of machine learning we see used daily. Deep learning uses an algorithm to create what are called neural networks. Neural networks are loosely based on the neural networks of the human brain. Each of these types of neural networks fits in one of the three categories.

Supervised Learning

Supervised learning involves machine learning, where variables—called features—and labels are assigned to the model used. These features and labels are utilized to properly classify the data received. The algorithm can identify patterns based on predetermined features and labels.

An example of machine learning using the supervised model is a machine that can count coins of different denominations. If the weights (features) of nickels, dimes, and quarters (labels) are input into an algorithm, the model can predict the denominations of the coins based on knowing the weight (feature) of each. Another example of this is with a music streaming service that predicts the best choices of music to play based on the genre you routinely choose.

Unsupervised Learning

Unsupervised learning does not use predetermined features and labels. The model is set up to search for any patterns it can recognize. The process is much like a person collecting shells at the beach and later categorizing them based on their shapes. Since there are no labels in this process, there is a greater ability for the machine to analyze the data to locate hidden structures contained within it. Unsupervised learning has become popular among those in the cybersecurity community.

Reinforcement Learning

Behavior modification involves the use of reward and penalty to encourage or discourage specific activities. For instance, if a dog is being house trained, it will be rewarded when it does its business outside and scolded when it does so inside. Reinforcement learning uses these same feedback responses to train machines to learn.

The algorithm for reinforcement learning is based on a trial and error model. Large amounts of data are input into the model, and the machine is rewarded or penalized subsequent to whether the selections help or hinder the objective of the application. Reinforcement learning is seen with the training of robots for industrial automation.

StrategyDriven Innovation Article | The Value of the Human Mind - How Machine Learning is Helping Humans WinHow Machine Learning Helps Humans

We see machine learning at work in our everyday lives. From our search engine results to our ride-sharing apps, machine learning is front and center in the process.  What he have seen is that augmented intelligence enhances human’s intelligence. Let’s focus on some of the applications that use machine learning.

GPS has become a staple in the lives of all travelers. GPS uses machine learning to assist us in reaching our destination by using other users’ input and recognized patterns. Picture recognition used by Facebook is another form of machine learning that uses a supervised model. Ride-sharing apps use various machine learning models to predict destinations, estimate times, and determine pricing.

StrategyDriven Innovation Article | The Value of the Human Mind - How Machine Learning is Helping Humans Win

Conclusion

Once merely an element in sci-fi movies, AI has become a part of our daily experience. Whether we notice the presence of machine learning or not, our lives have been made notably simpler due to its role in developing applications designed to give humans the advantage. Hopefully, you can now recognize the ways that AI continues to benefit you every day.