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2019 TMT Value Creators Report: Despite a Volatile 2018, Tech Is Still Performing Strongly

Related Expertise:Technology Industries, Telecommunications, Media & Entertainment

Despite a Volatile 2018, Tech Is Still Performing Strongly

The 2019 TMT Value Creators Report

25 February 2019 By Awais Ali , Simon Bamberger , Hady Farag , Patrick Forth , Derek Kennedy , and Fredrik Lind

The end of 2018 was brutal for the technology industry. Alphabet, Amazon, Apple, and Microsoft, four stocks that had each exceeded or flirted with $1 trillion valuations, collectively lost nearly $900 billion relative to their all-time peaks, and the industry as a whole experienced its most volatile year of the 2014−2018 period. (See Exhibit 1.) But despite its short-term struggles, tech was still a long-term winner. The industry recorded a median five-year TSR of 14%, the 4th-best showing among the 33 industries that BCG analyzed. All but a handful of the 89 tech companies in our sample recorded positive TSR over the five-year period, a much stronger overall performance than that of telecom or media, and a reminder that value creation should be viewed over the long term.

In particular, the top ten tech value creators demonstrate the payoff from long-term strategies built around AI, the cloud, the Internet of Things, and software as a service (SaaS). (See Exhibit 2.) The top three value creators—Broadcom, Nvidia, and AMD—are all semiconductor companies, albeit each with a different orientation. Broadcom is partly a play on disciplined consolidation and integration of acquired companies. Nvidia and AMD have capitalized on strong demand for high-performance computing driven by the growth in AI applications, augmented and virtual reality, gaming, and self-driving and driver-assisted systems. These applications have now replaced the smartphone as the growth engine for the semiconductor industry.

The five-year success of the semiconductor companies may be hard to duplicate, however, at least in the immediate future. Recent earnings reports of several companies have highlighted softening demand in such areas as data centers, smartphones, and bitcoin mining.

The multiyear transformation of 4th-ranking Adobe to a SaaS business has returned the company to the top ten after a one-year absence. And at number five, Amazon is thriving with its cloud. Amazon Web Services is growing by more than 40% annually and was the most significant contributor, of Amazon’s three businesses, to the company’s operating income in 2018. Microsoft has likewise had a remarkable five-year run, even though it landed just outside the top ten, at number 12. It, too, has benefited from its move to the cloud with both Azure and Office, carefully orchestrated by CEO Satya Nadella.

The flip side of this story is that success, while hard earned, can be tough to sustain, as semiconductor companies may soon discover. Only three of the top ten (Nvidia, Broadcom, and IT services giant DXC Technology) were top-ten performers in last year’s survey, which analyzed 2013–2017 TSR. And none of the top ten performers from five years ago (2009–2013) are in the top ten this year. In fact, two top-ten hardware companies from 2009–2013 reported negative TSR in the current five-year period, a potent reminder that tech companies must be in a constant state of reinvention or they will revert to the mean.

Growth Is the Most Common Path to Top Performance

While companies can pull individual TSR levers in the short term to improve value creation, growth is generally what matters most in the long run. (See Exhibit 3.) Nine of the top ten tech value creators, for example, experienced double-digit annual revenue growth over the last five years, while the median annual growth rate of the entire sample was 8%. To be sure, companies can also pursue reckless “bad” growth or find other sustainable means to value creation, but over five- and ten-year periods, growth accounts for 50% to 70% of value creation. (See “Ten Lessons from 20 Years of Value Creation Insights,” BCG article, November 2018.)

Many of the companies outside the top ten generated value by pulling TSR levers other than growth. (About 40% generated more than half of their value this way.) For example, the share of dividend-paying US tech companies more than tripled from 2014 to 2017, and the average payout ratio doubled. This development reflects a maturing of the sector—or at least of certain companies—and a decision to placate shareholders using short-term tools. The problem is that TSR drivers other than growth are self-limiting. For example, a company cannot infinitely expand margins and multiples.

The AI Agenda

AI is often touted as the next growth engine for technology. Today, chip companies are clearly benefiting from AI’s need for massive processing power, and cloud providers, such as Amazon and Microsoft, will benefit from the heavy storage and computing needs of AI. 

AI is also rapidly making inroads in the highly contested markets for voice assistants, such as Apple’s Siri, Amazon’s Alexa, and Google Assistant, and for smart-home devices. By some estimates, the adoption of smart-home devices in the US will grow by 42% annually between 2017 and 2022. Through these devices, tech companies are striving to gain an important perch inside the homes of consumers, from which they can then expand. But how and where else will AI play a large role? And will other technology companies be able to generate value through AI, as AI-intensive activities such as self-driving cars reach scale? We see five potential value creation scenarios:

  • Enablers of AI. Semiconductor companies and cloud providers that have constructed strategies around AI have been the biggest winners to date. They are providing the software and hardware that is allowing AI to flourish.
  • AI as a Platform. Many tech companies provide the enabling tools and systems that allow other companies to build AI applications and services. For example, Google, Amazon, and others are offering services that will manage data flows across AI systems. At the same time, all the major cloud vendors offer programming tools, such as the Azure Machine Learning service, that encourage cloud usage. The self-driving-vehicle initiatives of tech companies ranging from Alphabet to the big three in China—Alibaba, Baidu, and Tencent—also fall into this category, given their complexity and potential influence.
  • AI as a Standalone Offering or as Components for Other Companies. Several vendors offer finished solutions powered by AI. IBM Watson Health is one of the best-known AI products. Others include products from Kira Systems, which reviews and analyzes contracts; Feedzai, which provides a fraud detection service; and Cogito, which provides conversational analysis tools to call centers.
  • AI as an Embedded Feature or Capability in Tech Companies’ Own Products and Services. This should increasingly be mandatory for all new offerings. Online mapping and GPS applications, for example, make heavy use of AI, as do digital assistants—notably, Siri, Alexa, and Microsoft’s Cortana. Likewise, AI enables Amazon’s recommendation engine, as well as Google’s and Microsoft’s search engines.
  • AI as an Enabler of Internal Operations. As a general-purpose technology, AI can help improve tech companies’ own internal processes in such areas as sales force effectiveness, customer journeys, demand forecasting, and customer service. At Microsoft, for example, the finance department relies on machine learning to establish baseline revenue forecasts and identify compliance anomalies in contracting. (See the sidebar, “Five Questions for Microsoft’s Bill Duff.”) AI company DeepMind has even helped reduce the data center cooling bills of its sibling, Google, by 40%.

Five Questions for Microsoft’s Bill Duff

Corporate Vice President and CFO of Experiences and Devices at Microsoft

Tech companies are embedding AI not just into their products and services but also into their internal operations to improve efficiency and decision making. Bill Duff, Corporate Vice President and CFO of Experiences and Devices at Microsoft, shares his experience introducing AI into the finance function.

What was the motivation for introducing AI into the finance function?

We are using AI nearly everywhere at Microsoft, not just finance. We see AI as a tool to develop deeper insights into our business and improve overall performance. In addition, AI can augment the work that employees do, enabling them to spend less time on routine tasks and more time on high-value activities.

Where in finance is AI playing a role?

Two prominent examples are forecasting and risk management. We are using machine learning models to confirm and improve the accuracy of our sales forecasts. People in our finance organization still generate forecasts and drive the overall process, but the usage of these machine learning models has created a lot of efficiencies, which has freed up time for our financial analysts to focus on more productive activities. It enables us to have more streamlined and insightful conversations with our business partners, focusing on key areas of business performance and exploring important concepts such as customer lifetime value.

Risk management is another area where we are using AI to improve results. Because of the volume and complexity of our global business, machine learning models help identify deals that should be audited. We have found that AI flags different transactions than our traditional compliance process and has helped detect patterns worth investigating. Overall, it is helping us utilize our resources more effectively and has led to reduced risk.

How did you get started with AI?

It’s been a five-year journey to get where we are today. The first step was to consolidate the many different data silos we had across the company to create a more consistent view of business performance. Once we brought the data together into a single source of truth, we started identifying areas where AI might add value. One of the first areas we focused on was forecasting. We started slowly, running our existing forecast process and machine learning models in parallel. Over time, as we built trust in the model, we were able to integrate it into existing processes to provide more accurate forecasts with fewer people involved and less time spent. This allowed people to spend more time driving business performance. Looking back, it was critical that we started with the data. Having a high-integrity data source was essential in order for us to use AI to improve efficiency, processes, and our overall understanding of the business.

AI can cause anxiety and worry among employees. How did you address those concerns?

There is no question that AI is a potentially disruptive technology. For employees to be open to this kind of change, they need to be able to see how AI improves their opportunity for impact and frees their time to focus on the most productive activities. That way, employees with a growth mindset, which we talk about a lot at Microsoft, can see it as a way to become more valuable rather than disempowered. You cannot underestimate the communications required, both in terms of creating a culture that embraces change and also showing why AI is useful to improve business performance.

Do you have any advice for other companies that are about to embark on a similar journey?

In addition to investing in data and focusing on communications and culture, you just need to lean in and get started. Executive leadership is important to set the foundation and direction. But you also need to create champions within the business who can lead this change at the ground level. As with any change, it makes sense to start with low-hanging fruit and celebrate early successes.

What’s less clear is the best ways for technology companies to capture value from these approaches. But staying on the sidelines is definitely not an option. AI will soon be everywhere. And tech companies need to be in the game to win.

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