Where does AI fit into Digital Transformation?

Artificial intelligence (AI), or the ability for computer programs to learn, predict, and make informed decisions, is transforming every sector of the business landscape. It has the potential to dramatically increase value to shareholders and stakeholders for businesses that understand how, when, and why to implement it. Indeed, AI represents as fundamental an opportunity for change in business operations as did previous industrial and technological revolutions.

But just as past business leaders had to learn to leverage first steam power and then electricity, today’s executives and managers must master the fundamentals of AI to drive truly valuable, long-lasting, and adaptive changes to their businesses.

At the moment, only a very small number of organizations have been able to use AI to its full potential. Traditional businesses, including many of those in the pharma space, are still struggling to see where AI fits into a successful digital transformation (DX). The problem is less a question of technical knowledge, which many leaders have, and more one of strategy, implementation, and culture.

This article contrasts traditional businesses to AI-powered ones, examines the characteristics of companies that have successfully digitally transformed using AI, and discusses how firms can create shareholder (and stakeholder) value through AI.

Traditional business versus AI-powered business

In every industry, AI-powered businesses are beating out traditional businesses that have failed to adapt.

Compare Amazon to Barnes & Noble, for example. In 1996, Amazon boasted just $16 million in sales compared to Barnes & Noble’s $2 billion. Today, Amazon has a market cap of around $1 trillion, while Barnes & Noble sits at $475 million—several orders of magnitude smaller. Why? Amazon has openly said that one of its greatest drivers of success is the sophistication of its AI algorithms, which understand customers to an unprecedented degree and, more importantly, can provide targeted, data-informed recommendations to millions of customers per second. In addition, Amazon’s mission ‘To be Earth’s most customer-centric company is Amazon’s purpose and it shines through in everything we do.” The unbeatable combination of AI and customer first powers unsurpassed growth in organizations.

Tesla provides another example. The automotive industry has been dominated by traditional business players for decades. Today, Tesla is worth more than the next six automotive companies—combined. AI plays another critical role here. Tesla is not merely a car production company, it is an artificial intelligence company offering the dream of autonomous, self-driving cars to regular consumers (at $10,000 a pop). Tesla’s disruption of the industry is not due to greater sophistication in production or more spending on marketing. It is an AI company first and foremost, embedded in the automotive space. Again, like Amazon, Tesla uses its’ AI to really understand their customers and deliver what they want. Other car companies have a disadvantage as they put the car dealer in the middle of the model and therefore do not have an intimate relationship with their customers. Not Tesla.

Characteristics of companies that have successfully digital transformed using AI

An important question is how companies that have successfully digitally transformed using AI, like Amazon, Tesla, and AliBaba, differ from others. As it turns out, the main differences between traditional businesses and successfully AI-powered businesses are cultural and strategic. These are the findings of an extensive report published in the Harvard Business Review entitled Building the AI-Powered Organization. Based on this and other studies, there are some key negative cultural aspects that hold businesses back.

Technology is a necessary evil, not a universal support. First, traditional businesses have tended to see AI (even technology in general) as a “necessary evil” to business operations, and not something that can improve processes and experiences across the board. In the words of Imtiaz Adam, “Tech firms and startups view technology including AI as a source of revenue generation and [a] key part of the customer experience. Legacy firms often view tech as a cost and backend process that is an evil necessity relative to the business rather than a key tool to generate value in the core processes of the company.”

Data is collected as an afterthought and put to limited use. Businesses having undergone successful digital transformations did so by understanding the value of data as a precursor to decision-making, customer relations, and production, and not merely as their byproduct. In one review published in the California Management Review entitled “Demystifying AI: What Digital Transformation Leaders Can Teach You about Realistic Artificial Intelligence,” the authors note that integrated data management, or “the organizational capability of managing customer and organizational data in a holistic and integrated fashion, avoiding data silos and incompatible data formats” (emphasis added) helps separate DX leaders from laggards. Look once again to Amazon, whose entire customer journey is informed by data.

AI is developed and implemented in a top-down fashion, rather than directly involving the departments and individuals who will be using it to boost retention and trust. In one example from the HBR study, a firm implemented an AI scheduling solution for events involving hundreds of people. The AI ran through the hundreds of millions of possible permutations, distilling options down to millions, then hundreds. “Experienced human planners then applied their expertise to make final decisions supported by the data, without the need to get input from their leaders.” Importantly, they “[trusted] its output because they’d helped set its parameters and constraints and knew that they themselves would make the final call. When AI is developed with departments, rather than imposed upon them, retention and trust are much higher.

AI understanding and adoption are taken for granted or left to chance. In one study, it was found that the vast majority (90%) of companies who were able to successfully implement and scale AI practices “spent more than half of their analytics budgets on activities that drove adoption, such as workflow redesign, communication, and training.” Cultural and workflow changes require leadership and cannot merely be left to chance. Executives should expect and plan for resistance, finding ways to motivate and engage employees in the move to becoming a full-fledged AI-powered business.

How firms are creating shareholder value through AI

Such cultural changes don’t come easily, but the benefits are more than considerable.

Leveraging AI to make full use of customer and corporate data provides a full, 360º view of the customer. In “Using AI to Track How Customers Feel — In Real Time” (HBR), authors M. Zaki, J. R. McColl-Kennedy, and A. Neely describe how they applied an adaptive AI to customer satisfaction written responses with great success, unlocking several key benefits. The AI showed them what customers really cared about, including several elements leaders had missed out on, like the importance of financing and invoicing, allowing them to redirect resources and better train employees; it allowed the firm to uncover the root causes of dissatisfaction by following-up with flagged customers; it helped the firm to capture the customer’s emotional and cognitive responses to their service and then quantify it, something humans are notoriously bad at. All of this was used to improve customer experience, which ultimately led to greater profits. Cited above, Amazon is another clear example of the value in customer data.

AI allows businesses to be proactive rather than reactive by predicting customer needs, expectations, and even behaviour. The same AI mentioned above allowed the team to spot and prevent decreasing sales by zeroing in on customers that, while giving high satisfaction scores overall, were “at risk of defecting due to historical issues.” It was shown that “if these so-called ‘satisfied’ customers defected they were likely to cost them around $6 million in sales.” By learning from millions of customer experiences in ways no team of humans reasonably could, AI provides robust decision-making information ahead of events. Predictive AI is making its way into customer relations, production, and even notoriously fuzzy areas like share trading with proven success.

Using AI to build new services and products out of existing data. Beyond customer relations, AI can help businesses leverage existing processes and data to create new value in novel services or products. For example, the Press Association is using AI to spur on an industry-saving digital transformation project for local news (a sector that’s been in decline for years). Machine writers are able to churn out hundreds of localized variants of basic news stories in mere hours, which local journalists can then engage with and expand upon. “News providers that still have reports delving into local issues will use these bot-generated stories as the basis for their own, deeper investigations going forward.” It is not quite, but almost, something from nothing.

Streamlining supply chain and production to an unprecedented degree. While Tesla is offering AI as a product, BMW has integrated AI into just about every step of its manufacturing processes and logistics. It uses AI to manage the movement of more than 31 million parts around the world, drive autonomous transport vehicles inside and outside plants, load and unload payloads and, more importantly, make logistical decisions about what needs to go where at what time. This is key, as it allows humans to focus on the craft of making cars, without having to worry about materials being where they should be at what time. Finally, BMW is using AI to check for defects and better distinguish between oil and dust, for example, versus actual fine cracks or imperfections.

Likewise, AliBaba’s Singles Day set record sales this year, despite a global pandemic that not only disrupted supply chains but also presented significant hurdles for the AI whose job it was to predict sales. Nonetheless, by foreseeing a need for greater investment in AI in light of these global changes, AliBaba was able to generate more than $115 billion in sales from November 1st to November 11th, at a time where other online sellers were struggling.

These are just a few of the ways firms are creating shareholder value through AI.

Business impact

The studies mentioned above, and in particular that of Brock and Wangenheim, show that AI is expected to impact business in a variety of areas, including smart services, office automation, management support, smart products, manufacturing automation, and automated customer service.

While expectations are largely aligned across sectors, close to 20% of firms anticipate a particularly high impact. Across industries and firms, the common thread is that those with stronger digital skills anticipate the greatest AI business impacts.

In other words, businesses with a strong digital strategy and digital business development skills, investments in new digital technologies like AI and IoT, skilled and well-funded data science teams, and excellent cybersecurity skills stand out as particularly well-positioned to “realize the potential of the new digital technology.”

The study underscores the importance of technical know-how, data management and analytical skills, and data, certainly, but also that these elements must be “embedded in a coherent and suitable strategic framework to ensure a guided implementation and wider organizational alignment and support.”

In other words, executives and business leaders must establish a clear, strategic path forward, championing the characteristics of companies that have successfully transformed using AI so that shareholder and stakeholder value might be created as a result.

Where to start

Many businesses struggle to fit AI into their digital transformation. They are disappointed by returns after implementing AI or trying to adopt more customer-centric approaches. Others know they have transformative amounts of data at their disposal but are unable to obtain real value. Or they understand the value of data but are unsure how to mine it.

There are myriad factors to take into consideration, muddying the waters and making it difficult to know where and how to begin.