What Is Your AI Business Use Case?

In 2020, O'Reilly published a book summarizing the surveys about adopting artificial intelligence in enterprises and listed some of the most common factors that hold back its further implementation. According to this book, five key artificial intelligence adoption obstacles were:

1. Company culture does not yet recognize the need for AI – 23%
2. Lack of data or data quality issues – 19%
3. Difficulty in hiring skilled people – 18%
4. Difficulty in identifying the appropriate business use cases – 17%
5. Technical infrastructure challenges – 8%

I have addressed the culture and data issues in my previous posts; this time, let us talk about the artificial intelligence business use case.

The main reason is that artificial intelligence could constitute your competitive advantage as well as be just a handy productivity tool or feature. Artificial intelligence products and technology companies are incentivized to market the latter. Thus, most information on artificial intelligence business use cases you find online is guiding you in the wrong direction.

Many vendors of artificial intelligence technologies and products free-ride the tide to sell their products: I keep getting LinkedIn messages promising Google-like business performance with yet another AI-enabled chatbot, analytics platform, or data labeling solution every week. When encountering another AI-enabled SaaS tool offering you a Google-scale advantage, beware: a competitive advantage available for sale to every competitor is a contradiction in terms.

The Mirage of AI Business Use Cases

To illustrate that, let us google "AI business use cases." My first link leads to the Master of Code article titled "10 Amazing Cases Of Using AI in Business". It features ten AI applications, from sales and marketing to healthcare. If you go through them one by one, you will notice they promote performance and say nothing about the competition. It should be a warning sign: AI can provide you with operational effectiveness. It also can be at the heart of your competitive advantage. AI leaders and strategists must tell apart one from the other. Performance tools available to every company on the market benefit your customers and AI vendors but do not help your company in the long run.

The Lesson from Japan's Operational Effectiveness Movement

To illustrate this point, let us start with history. In the 1990s, a new school of thought got rapid traction. It praised performance and flexibility, claiming that in the new, modern world, any competitive advantage is, at best, temporary and will inevitably be copied by your rivals. Hence, you must focus on operational efficiency, outsource aggressively, keep the core activities as small as possible and constantly benchmark your company against the competitors with numerous "best practices." Indeed, companies have properly invested energy in becoming leaner and more nimble. In many industries, however, it led to hypercompetition homogenizing the players and making their products and services virtually indistinguishable. Michael E. Porter described it in his classic 1996 paper "What is Strategy?"

The operational effectiveness movement dates back to the Japanese breakthroughs of the 1970s and 1980s. At that time, Japanese companies were so ahead of Western competitors in operational efficiencies that they could simultaneously offer better quality and lower costs and appeared unstoppable. Today's best operational practices, such as total quality management and lean manufacturing, originate in Japanese companies, particularly Toyota Motor Corporation, and were originally influenced by Frederick Winslow Taylor's Scientific Management ideas.

Yet, as Michael Porter, Hirotaka Takeuchi, and Mikio Sakakibara argue in their 2000 book "Can Japan Compete?", Japanese companies rarely had strategies. Those that did — Sony, Canon, and Sega, for example — were the exception rather than the rule. Most Japanese companies copied one another's product varieties, features, services, and operational practices. As global competitors caught up with the operational practices, it turned out that there was little else Japanese companies could rely upon. A company can outperform rivals only if it can establish a lasting difference. Turns out, operational best practices and tools were too easy to copy — since the late 1990s, Japanese GDP growth flattened, and even the courageous Abenomics policies of prime minister Shinzō Abe did not change things much. In hindsight, Michael Porter's and Hirotaka Takeuchi's diagnosis was correct.

Strategy Trumps Performance

Strategy and positioning remain crucial for sustainable competitive advantage. We can see the evidence around us every day: half of the biggest companies in the world by market cap today are Internet companies built on powerful network effects underlying their businesses. Consider internet advertising: the global market size is estimated at USD 476 billion, and we would expect it to be crowded with a swarm of fiercely competing rivals. Yet, it has been dominated by a few giants for decades. Nobody tries to compete head-to-head with Google or Facebook even though today's cloud technologies and computer speeds simplify building a search engine beyond anything Larry Page and Sergey Brin could imagine in 1998 when they started Google.

Even the enormous increase in engineering efficiency of the last two decades does not compensate for the competitive advantage built with proprietary data and machine learning: if one copies all Google codebase and pastes it into one's own cloud servers, line by line, one will be unable to compete with Google because of the absence of all the historical data. Correspondingly, Google, Facebook, Netflix, and Amazon could afford and sometimes indeed are much less effective in software engineering practices compared to Infosys, Epam, and Accenture, which made performance their core competency. Still, engineering performance superiority does not give the latter a single chance should they decide to go head-to-head with the former.

Productivity is necessary for your business but rarely sufficient. If you achieve it by means that could be easily imitated or purchased by your competitors, you engage in the exhaustive arms race that — here comes the crucial part — produces absolute improvement but leads to relative improvement for no one. The surplus will be captured by the customers and suppliers of the productivity tools and products.

The AI Business Case Trap

With this in mind, let us return to the artificial intelligence business case. Suppose you purchased an AI-powered customer service chatbot (case #1 of the Master of Code article referenced above). It greatly improves your customers' experience, thus justifying your premium prices. Shortly, your competitors will buy it, too, and you all will have to bring your margins down to the previous levels. Customers now have superior service at nearly the same price as before, thus capturing a large fraction of newly created value. The artificial intelligence chatbot vendor company gets its fees from every competing company, thus capturing the remaining surplus. Yet, your company's and your rivals' market share and profit margins remained the same.

Being designed right, artificial intelligence technologies can turn your proprietary data into a competitive advantage as formidable as Google's, Facebook's, and Amazon's, even if you are a small startup or a boutique consulting company. However, with the wrong design, the same technologies will only temporarily contribute to your operational efficiency, destined to be quickly copied by the competitors and put your company on the trajectory of once-revolutionary Japanese companies.

Why the Wrong Information Proliferates

Yet, this wrong design benefits artificial intelligence technology and product vendors as they share part of the created surplus. And that is why they actively market it. Much of the AI business case information you encounter comes from some marketing material. They often leak into even the most respectful summaries and even analytical articles since, at a glance, both designs involve artificial intelligence, and telling them apart requires a rare blend of technical and business expertise. It makes searching for a valid business use case in a haystack of AI marketing materials particularly hard.

Arbitrary artificial intelligence features and the vast majority of third-party AI products do not contribute to your competitive edge. Building AI competitive advantage requires identifying the point of competitive strength, coordinated cross-functional efforts, and close alignment between the AI, product and business strategies. If designed right, your artificial intelligence initiatives can position your company for long-term leadership, even in crowded or highly competitive markets. If designed wrong, they would drain your company of precious resources and time and eventually benefit your technology vendors and customers, not your company.

This blog is dedicated to AI competitive advantage, and we are doing our best to explain how it works and how you can build one for your product or company. You can check our other posts to get an extensive explanation of what the network effect is and how AI enables it, and what culture helps you build the right AI products.

If you need help in building an AI competitive advantage for your business, look no further. Our team of business experts and AI technology consultants has years of experience in helping technology companies like yours build sustainable competitive advantages through AI technology. From data collection to algorithm development, we can help you stay ahead of the competition and secure your market share for years to come.