2020 Tech Industry Outlook
The lack of skilled IT workers is hurting the deployment of emerging technology, according to a new survey from Gartner. In areas from cloud to cybersecurity, this crisis is expected to last for years to come.
Cloud computing and artificial intelligence will once again dominate the technology headlines, but 2020 could be a breakout year for edge computing.
While familiar themes such as cloud computing (including everything-as-a-service, or XaaS) and artificial intelligence (AI) will once again dominate technology headlines, 2020 could be a breakout year for "edge computing."
According to Paul Sallomi, global technology, media, and telecommunications industry leader and US global technology sector leader, the time is right for companies to seriously consider exploring the advantages—including reduced latency and lower bandwidth costs—of processing data locally, at the edge of their networks.
In terms of strategy, partnerships will likely become even more essential for technology companies looking to deliver solutions that drive true business outcomes for customers.
Deloitte's global technology, media, and telecommunications industry leader, Paul Sallomi, shares his perspectives.
Where do you see opportunities for growth in 2020
As we enter a new decade, one thing is certain: cloud adoption will continue to rise as companies embrace flexible consumption through both hybrid and multicloud environments.
For many companies, the hybrid-cloud approach serves as an interim step in the long process of digital transformation. Gartner predicts that by 2020, 90 percent of organizations will adopt hybrid infrastructure management.
In addition to hybrid cloud, enterprises are increasingly adopting multicloud solutions that combine cloud services from multiple providers. According to a 2019 Kentik report, 58 percent of businesses are already using a combination of Amazon Web Services (AWS), Microsoft Azure, and Google Cloud in their multicloud networks.
Growing concern about cloud security presents providers with a unique opportunity. In many cases, cloud providers possess far greater security capabilities and expertise than most businesses could ever hope to develop themselves. For this reason, security has become a key driver of hybrid cloud adoption.
Cloud-based solutions also provide the most popular path for acquiring AI capabilities. Increasingly, enterprises are viewing AI as essential to their continued innovation and growth. As a result, they're investing in AI—and getting a return.
With the explosion of Internet of Things (IoT) devices, combined with the increased portability of computing power and AI-driven tools, the time is right for edge computing to experience significant growth.
Consider this: according to Gartner, companies generated a modest 10 percent of their data outside a data center or cloud in 2019. IDC predicts that in three years, 45 percent of IoT-generated data will be stored, processed, analyzed, and acted upon close to or at the edge of networks. This will largely be driven by IoT applications across industries like manufacturing, retail, healthcare, energy, financial services, logistics, and agriculture.
The benefits of edge computing can extend to factories, distribution facilities, autonomous vehicles—essentially any situation where data must be processed locally versus sending it to the cloud or a data center.
Which Strategies are tech companies using to facilitate growth?
Until recently, strategic discussions typically began with the following question: "Should I buy or build?" As we head into 2020, that question should be modified: "Should I buy, build, or partner?"
With fast-paced developments in emerging technologies, partnerships can be critical for tech companies looking to enhance their existing solutions or provide more targeted offerings.
Based on the key strengths and expertise of partners, companies can pursue research and development; offer more integrated solutions across their hardware, network, platform, or software stack; or target different markets altogether.
Tech companies have leveraged this model extensively to offer improved products and services across areas like AI, cloud, and processing.
For many companies, this approach will require a permanent shift in their overall mindset. Why buy an asset that's not best-in-breed when you can team with someone who has the specific capabilities you need? Partnering represents a more efficient use of capital and will probably drive better outcomes.
This "partnership" concept also extends to multiplayer alliances—complex ecosystems of providers who combine best-of-breed assets to create end-to-end solutions for clients.
When it comes to strategies for developing and expanding their cloud business, many tech companies are increasingly shifting toward the everything-as-a-service (XaaS) model, which encompasses capabilities such as platform-as-a-service (PaaS), infrastructure-as-a-service (IaaS), and software-as-service (SaaS).
In the new world of XaaS, tech companies should deliver highly tailored solutions that reflect a deep understanding of each customer's business and desired outcomes.
Mergers, acquisitions, and divestitures (M&A&D) will remain a viable growth strategy for tech companies in the coming year, with revenue growth, tech assets, and IP expected to be the top drivers.
However, companies are looking to do more than simply enhance technology through M&A&D. Increasingly, they're employing this strategy to expand into new markets and build their consumer bases.
In particular, divestitures can be important where best-of-breed assets are becoming a drain on capital and partnering is a preferable alternative.
One other strategy that no tech company can afford to overlook is building a diverse workforce. There is empirical evidence that inclusive companies generate up to 30 percent higher revenue per employee, are more profitable than competitors, and are eight times more likely to achieve positive business outcomes.
Diversity in the workforce and among partners can also promote ethical use of AI by reducing the potential for bias in certain applications.
What should businesses be mindful of as they plan for growth
In 2020, the regulatory arena promises to become even more complex as various jurisdictions continue to develop their own laws and guidelines. Given the lack of a consistent global regulatory standard, tech companies must closely monitor developments in this space; the price for not doing so can be steep, including potential audits and monetary penalties that can impact brand reputation.
While AI delivers a host of potential benefits, it also brings its share of risks—particularly in the area of AI ethics. These ethical concerns typically fall into four areas:
- Privacy: Collection of data and usage of facial recognition technology without consent
- Lack of transparency: Insufficient visibility into the "secret logic" used by AI algorithms to make critical business decisions
- Bias and discrimination: Underlying data set reflects biases that taint decision-making
- Lack of governance and accountability: Who is accountable for data and AI systems, ethical norms, and unethical practices?
How companies use AI ethically and build trust with their customers, partners, and the general public will likely be key differentiating factors now and in the future.
To ensure ethical use of AI, it is important to include a broad set of employees and partners in ethics-related conversations.
Major tech companies have begun to introduce AI ethics boards, but there has been a substantial amount of concern raised about the lack of diversity in some of those groups.
Another area of concern for both tech companies and enterprises is cybersecurity—including AI-enabled attacks that are becoming more advanced and serious. However, AI also has the potential to protect against existing cyber threats and identify new ones.
To continue growing, tech companies should address skills gaps—particularly in the area of AI. According to our most recent AI survey, "Future in the Balance? How Countries Are Pursuing an AI Advantage," 68 percent of AI early adopters indicated moderate-to-extreme AI skills gaps; the top three roles needed to fill those gaps are AI researchers, software developers, and data scientists.
However, many companies are also looking beyond technical skills and focusing on attracting or developing business leaders who can interpret AI results and make informed decisions based on them.
While organizations may believe that hiring talent will provide an advantage, training their current workforce can be another option.
Last, but certainly not least, is the area of data—or, more specifically, the imperative for companies to become more "data-centric." To enable digital transformation and truly leverage AI's capabilities, companies should develop strategies that treat data as a crucial asset.
However, many companies seem to face obstacles that prevent them from developing a "data culture." According to NewVantage Partners' 2019 Big Data and AI Executive Survey, 72 percent of companies report that they've been unable to forge a data culture, and 69 percent say that they haven't created a data-driven organization.
In addition, 53 percent say they are not yet treating data as a business asset.
AI systems are likely to make erroneous or biased decisions if the data entered into them is error-prone, inconsistent, incomplete, or outdated. Hence, data preparation and standardization is an important first step for AI technologies to deliver their true business value.
Simply put, "You can't analyze what you can't see." To thrive in the next decade, companies will likely need the ability to harvest data and visualize its implications across the enterprise.
New enterprise resource planning (ERP) systems can help with harvesting "one version of the truth" for companies, but this technology should be complemented by transformed processes that enable the system to work across an entire company.
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