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  • Ozzie Paez, Shelley Lineham

AI, business strategy and venture capital

There are no shortage of misconceptions surrounding artificial intelligence (AI). Many established companies are struggling to understand AI’s emerging impacts on their industries and markets. There is growing belief that this family of technologies are poised to broadly change markets and industries across the global economy. This realization is attracting record funding from venture capitalists looking for exciting new opportunities. According to Forbes, Venture Capital (VC) funding of AI startups increased 72% in 2018 to a record $9.33 billion dollars, nearly 10% of total VC investments[1].

These trends are reminiscent of the dot-com boom, when record numbers of startups fueled by VC funding drove Internet technologies to amazing successes and colossal failures. Successful ventures became household names, including Cisco, eBay, Amazon and Google. Many failures were quickly forgotten, including, Kozmo, eXcite, and Americast. Current investment trends and our experiences during the boom and bust suggest that more VC capital will be invested and lost on AI initiatives over the next five years than on any other family of tech startups. For investors, the risks can’t be avoided - but they can be tamed.

The primary drivers of risks for investors considering new technology startups are often strategic, not technical. Many startups struggle to find their footing in rapidly changing market environments. New ideas are quickly imitated, while cutting-edge technologies often mature faster than expected. Startups frequently lose track or ignore important components of competitive strategies including positioning, new entrants, and threats from alternative products and services. They often struggle as conditions change in updating their business and revenue models, human capital requirements and asset valuations. AI is particularly challenging because asset valuations sometimes don’t align with the business’s original purpose.

We recently performed a quick strategic checkup for a company that uses drones and remote sensors to perform inspections of field assets and facilities. It owns one of the largest libraries of inspection images and videos in North America. Company leaders were considering using AI technologies like machine learning to automate many inspection tasks. It’s an ideal match because machine learning requires large volumes of relevant training data, without which, even the best algorithms can’t deliver useful results. The most time-consuming phase of many machine learning projects is gathering enough data to select, train and fine tune algorithms.

We began by asking one of their executives if they knew the market value of their library. It’s an important question because in the world of AI, big data libraries can become the company’s most valuable asset. Data can grow into big-data over time as a result of normal operations and without company executives recognizing their growing value[2]. We concluded that this was likely the case with this company.

We suggested that before investing time and capital on new AI initiatives, the Board should consider three strategy options:

  1. Develop a proprietary AI system and supporting capabilities to gain competitive advantage, drive growth and turn the company into a leading provider of inspection services in their market.

  2. Quantify the market value of their image library and a potential sale or licensing to generate potentially large, short-term returns to owners and investors.

  3. Leverage the library to develop and sell AI based inspection systems to their clients and competitors - reframe competitors as potentially valuable customers and revenue sources.

Our quick checkup led us to ask a company executive two important questions:

  • What business are you in?

  • What business should you be in?

These are very important questions for leaders of new companies in fast-changing markets, particularly those embracing AI. We learned working with startups during the dot-com era that innovative products can be rendered obsolete by newer technologies before they make it to market. One company we worked with during that period burned through $140 million of startup capital in about three years. Their broadband services delivery system, which had been cutting edge at the start of the project, was eclipsed by newer technologies during development, so investors cut their losses and pulled the plug.

Summary and implications

We’ve conducted in-depth analysis of AI’s growing impacts on different industries including electric power generation and distribution, healthcare related industries and transportation. These efforts forced us to look beyond existing methodologies in assessing AI’s disruptive impacts on factors like business models, asset values, revenue models, and value propositions. Our challenge was that many proven methods were not particularly useful, while new approaches were generally unproven.

The solution was to update proven tools and methods and augment them with new ones that we developed and obtained from our network. This approach keeps us grounded, adaptable and innovative in our thinking. It also helps us gain valuable insights and develop practical solutions for clients experiencing the disruptive effects of technologies like the Internet-of-Things, Machine Learning and advanced automation. Our on-going research and insights can help VC investors identify opportunities that fit within their risk-return profiles. For them, the risks will always be there, but thoughtful analysis and consideration can help tame those risks and increase the odds of substantial returns.

Next Steps

If this article resonates with you and your investment goals, then let’s have a conversation. Our team can help you understand the risks and potential opportunities in emerging technologies like artificial intelligence and the Internet-of-Things.

Just contact us and we will setup a convenient time to discuss your needs and how we may help you. You can also reach the authors via e-mail at and



[1] Jean Baptiste Su, Venture Capital Funding For Artificial Intelligence Startups Hit Record High In 2018, February 12, 2019, Forbes,

[2] Thomas C. Redman, Does your company know what to do with all its data?, June 15, 2017, Harvard Business Review,

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