Here are the top 10 trends in tech

This article was originally published by McKinsey & Company, www.mckinsey.com. Copyright (c) 2022 All rights reserved. Reprinted by permission.

As all things digital continue to accelerate, which technology trends matter most for companies and executives? To answer that question, we developed a unique methodology to identify the ten trends most relevant to competitive advantage and technology investments.

These tech trends may not represent the coolest, most bleeding-edge technologies. But they’re the ones drawing the most venture money, producing the most patent filings, and generating the biggest implications for how and where to compete and the capabilities you need to accelerate performance.

Unifying and underlying them all is the combinatorial effect of massively faster computation propelling new convergences between technologies; startling breakthroughs in health and materials sciences; an array of new product and service functionalities; and a strong foundation for the reinvention of companies, markets, industries, and sectors.

In the next decade, according to entrepreneur and futurist Peter Diamandis, we’ll experience more progress than in the past 100 years combined, as technology reshapes health and materials sciences, energy, transportation, and a wide range of other industries and domains. The implications for corporations are broad. Consider the compressive effects on value chains as manufacturers combine 3-D or 4-D printing with next-generation materials to produce for themselves what suppliers had previously provided and eliminate the need for spare parts. Watch retailers combine sensors, computer vision, AI, augmented reality, and immersive and spatial computing to wow customers with video-game-like experience designs. Imagine virtualized R&D functions in science-based industries like pharma and chemicals or a fully automated finance function in your company.

Will your organization make the most of these trends to pursue new heights of rapid innovation, improved performance, and significant achievement? This interactive covers how fast these technology trends are moving toward you, their level of maturity, and their applicability across industries. It also presents the key technologies underlying each trend, along with their current applications and the disruptions they portend for companies going forward. These disruptions may be significant. A recent McKinsey survey describes how, during the pandemic, technology further lowered barriers to digital disruption, paving the way for more rapid, technology-driven change. Survey respondents in every sector say their companies face significant vulnerabilities to profit structures, the ability to bundle products, and operations. We’ll have more to say soon regarding the impact on specific industries—as well as the strategic questions these trends suggest for your industry, your business model, and your unique organizational capabilities. Until then, our new research helps make sense of a noisy and complex technology landscape.

The combinatorial power of technology fuels the first of the ten trends, in which robotics, the Industrial Internet of Things (IIoT), digital twins, and 3-D or 4-D printing (also known as additive manufacturing, or AM) combine to streamline routine tasks, improve operational efficiency, and accelerate time to market.

How fast are these technologies moving? By 2025, more than 50 billion devices will be connected to the IIoT, generating 79.4 zettabytes of data yearly. Annual installations of industrial robots, which have increased two times to about 450,000 since 2015, will grow to about 600,000 by 2022, even as 70 percent of manufacturers will be regularly using digital twins by 2022. Across industries, about 10 percent of today’s manufacturing processes will be replaced by AM by 2030.

For an idea of the broad implications of next-level process automation and virtualization, consider that 50 percent of today’s work activities could be automated in the next few decades, spurring powerful changes to the future of work, labor costs, and public policy. This will occur as robots become ever-more intelligent and capable. Production will gain scalability even as production lead times decline. This trend will shift competition toward capital-expenditure investments in automation technology and toward the social, emotional, and technological skills needed as intelligent machines take over more physical, repetitive, and basic cognitive tasks—all while compressing value chains as companies use AM to make products and components closer to home.

An automotive OEM used the IIoT to connect 122 factories and more than 500 warehouses globally, while optimizing manufacturing and logistics processes, consolidating real-time data, and implementing analytics and machine-learning throughput.

A big manufacturer used collaborative robots (cobots) mounted on automatic guided vehicles to load pallets directly, without human involvement, increasing operational efficiency and safety.

The city of Carson, NV, created a digital twin to simulate future water supply based on usage hours, optimizing water availability and saving operating costs.

A global aerospace supplier prototyped a 25 percent lighter 3-D-printed fuel nozzle it could quickly produce at scale, with no increase in complexity.

The second trend combines fifth-generation (5G) broadband cellular networks and the Internet of Things (IoT) to enable faster connectivity across longer distances, with exponentially faster downloads and latency (the time it takes to retrieve data) reduced to nearly nothing. Far-greater network availability and capability will drive broad shifts in the business landscape, from the digitization of manufacturing (through wireless control of mobile tools, machines, and robots) to decentralized energy delivery and remote patient monitoring.

We identified hundreds of use cases across 17 commercial domains. Implementing the most promising ones in just four sectors—mobility, healthcare, manufacturing, and retail—could increase global GDP by $1.2 trillion to $2 trillion by 2030.

Superfast connectivity (and internet) has broad implications for organizations. It supports the creation of new services and business models linked to sensor-enabled intelligent products, yields new value-chain offerings (for example, predictive services, augmented-intelligence services), and creates the potential for companies to more seamlessly personalize offerings across channels and create heightened customer experiences. In mobility, for example, IoT sensors and near-global coverage can help manufacturers capture vehicle signals, monitor the condition of each system in the car, and notify the owner to schedule repairs before a breakdown occurs, improving the vehicle’s durability and life span.

A leading telco began construction on an industry-leading greenfield factory using best-in-class information and operational technology integrated through 5G into a seamless network of IoT devices.

A sports stadium’s 5G upgrade targeted real-time updates on players, improved security, crowd-sentiment analytics, and HD 360° augmented-reality replays for fans.

A Chinese railway station used 5G for automated detection of station incidents and anomalies via IoT (for example, videos, and flow detection).

The third trend brings together cloud and edge computing to help companies move computing power further toward the edge of their networks—enabling them to reach data-hungry devices, with far-less latency, in a greater number of locations that are even more remote and to accelerate decision making with advanced analytics on demand. This tech trend will help companies boost their speed and agility, reduce complexity, save costs, and strengthen their cybersecurity defenses.

By 2022, some 70 percent of companies will employ hybrid or multicloud-management technologies, tools, and processes, which are the hallmarks of distributed IT infrastructures. This shift toward distributed IT infrastructure will be reflected by a rise in the software sourced by companies from cloud-service platforms, open repositories, and enterprise software-as-a-service (SaaS) providers—from today’s 23 percent to nearly 50 percent in 2025, if current trends continue, with the potential for this to jump to 80 percent if adoption accelerates.

Business implications arising from trend three include the democratization of IT infrastructure, especially computing power, and a corresponding shift in importance away from IT capabilities toward software-development skills and the talent it requires. Driving this shift is the move to more centralized “as a service” (XaaS) models of delivery enabled by cloud technologies as companies abandon more traditional on-premise IT infrastructures. This shift also suggests new, more modular configurations of business organizations built around “platforms” of activities and technologies that target specific business goals, including digital transformation, new talent requirements (more system architects, for example), lower costs, and higher innovation. More broadly, as data flows into the cloud, barriers to entry fall, enabling strategic moves into adjacent markets as traditional sectoral boundaries continue to blur and ecosystems continue growing in importance.

A European utility migrated about 90 percent of its applications to the public cloud, reducing 15 percent of its IT-run costs and simplifying its portfolio by retiring one-third of its applications.

In 2018, it took the Broad Institute of MIT and Harvard eight minutes to sequence a human genome (at the rate of 16 terabytes per day). Since then, cloud-based analytics has lowered sequencing time by 400 percent.

One manufacturer combined Internet of Things sensors at the edge with centralized cloud-data centers to monitor and analyze maintenance issues in real time, allowing more timely maintenance of manufacturing machinery, lower maintenance costs, and higher return on assets.

The fourth trend reflects the rapid approach of quantum computing and neuromorphic computing, with the latter involving the development of specialized microchips called application-specific integrated circuits (ASICs). Next-generation computing could help find answers to problems that have bedeviled science and society for years, unlocking unprecedented capabilities for businesses. It also promises to cut development time for chemicals and pharmaceuticals with simulations, accelerate autonomous vehicles with quantum AI, and transform cybersecurity—all while reducing hardware costs in IT, quickening machine learning, and enabling more efficient searching of unstructured data sets.

Next-generation computing enables further democratization of AI-driven services, radically fast development cycles, and lower barriers of entry across industries. It promises to disrupt parts of the value chain and reshape the skills needed (such as automated trading replacing traders and chemical simulations reducing the need for experiments).

Preparing for next-generation computing requires identifying whether you’re in a first-wave industry (such as finance, travel, logistics, global energy and materials, and advanced industries) and whether your business depends on trade secrets and other data that must be safeguarded during the shift from current to quantum cryptography. Others companies, for now, should continue to monitor this tech trend closely.

The city of Beijing used a classical quantum optimization solver on a special-purpose quantum computer to optimize traffic flow and reduce traffic jams between the city center and the airport.

Google offers tensor processing units (TPUs) as a cloud solution to bring machine-learning capabilities to the mass market, resulting in machine-learning applications that are 27
times faster at 38 percent lower costs versus graphics processing units (GPUs).

The growing ability of robots to sense, adjust, and make independent decisions plays a crucial role in the Fourth Industrial Revolution. Yet, the artificial neural networks required can strain traditional CPUs.

The continuing progress in next-generation computing, especially neuromorphic (ASICs) chips, brings the needed processing power closer to edge devices to support adaptive robotics with fewer trade-offs. Early applications include self-balancing bicycles and neuromorphic “event” cameras that enable object tracking and high-speed vision control.

The fifth trend deploys AI algorithms to train machines to recognize patterns and interpret and act on those patterns—helping computers make sense of real-world data, including videos or images (using computer vision), text (through natural-language programming [NLP]), and audio (using speech technology).

This tech trend promises to improve customer satisfaction through new customer interfaces and interaction methods—such as searching Amazon for products based on photos. More seamless human–machine interactions are also simplifying applications by translating speech, text, and images provided by humans into machine-readable instructions, boosting human productivity and lowering operating expenses.

By 2024, we estimate that more than 50 percent of user touches will be augmented by AI-driven speech, written word, or computer-vision algorithms, while one billion connected cameras will collect and share visual data by 2021.

An upcoming explosion in AI applications is set to augment nearly every aspect of human–machine interaction and power the next level of automation, both for consumers and businesses. Applied AI will further disrupt research and development through generative models and next-generation simulations. While any company can get good value from AI if it’s applied effectively and in a repeatable way, less than one-quarter of respondents report significant bottom-line impact. That isn’t surprising—achieving impact at scale is still elusive for many companies, not only because of the technical challenges, but also because of the organizational changes required.

A crop-protection producer fed live satellite images of fields through a computer-vision algorithm to detect plant viruses well before they would have become visible to human workers, enabling farmers to make early intervention with mild pesticide doses.

A retailer used computer vision, NLP, and speech technology to build and personalize a 360° customer view to enable tailored retail experiences at home and in-store, mediated through human-like virtual assistants and product-exploration tools.

The sixth technology trend relates to the rise of “Software 2.0,” in which programmers are replaced by neural networks that use machine learning to develop software. It promises to unlock higher-order, edge use cases like autonomous vehicles, where the only way to progress is through AI models. At the other end of the spectrum, Software 2.0 will also provide organizations with a far easier, more iterative, and intuitive way to customize existing code and automate mundane programming tasks through low-code and no-code approaches. Software 2.0 will be further accelerated by emerging tech trends in machine learning that “abstract away” many of the difficulties and complexities that currently hinder the development and application of AI models.

This technology trend makes possible the rapid scaling and diffusion of new data-rich, AI-driven applications. The relative homogeneity of neural networks can also support new open-source libraries and are increasingly modular, interoperable, and usable across domains. That lowers technical barriers to entry for these classes of applications and gives a deeper advantage to those able to source and refine the data needed to train these models through reinforcement learning.

While Software 2.0 will let companies address a new class of higher-order, edge use cases with less code, and therefore fewer developers, successful software producers will still need to evolve their talent—as well as their tech stack and development practices (such as MLOps , which combines infrastructure, tools, and workflows to provide faster and more reliable machine-learning pipelines—similar to how DevOps supports and enables better development of more traditional software). Their relationship with data will also change as data—and approaches to refining data—continue to further emerge as a valuable competitive advantage.

Implementation paradigms for Software 2.0 achieve more with less by moving code written by humans (Software 1.0) to code written by optimized neural networks.

One automotive-company player has leveraged Software 2.0–driven AI to deliver approximately 800,000 autopilot cars that have collected more than three billion miles of driving data, which is used to continually improve their AI.

Metaflow by Netflix provides a unified API to the infrastructure stack to design workflow, run it at scale, deploy to production, and provide automatic version and experiment tracking.

As a result, median deployment time at Netflix reduced by 16 times, from four months to seven days.

One start-up created a model-as-a-service platform to help financial-services companies navigate risk associated with model deployment and to help keep models “healthy” with metrics and dashboards for easier monitoring and debugging. This platform reduces users’ model-design and deployment time from months to minutes without compromising risk and compliance assurance.

The seventh trend describes a set of technologies and approaches designed for a world of increasing cyberattacks, a world in which more than 8.5 billion data records were compromised in 2019 alone. These trust architectures provide structures for verifying
the trustworthiness of devices as data flows across networks, APIs, and applications. Trust architectures could include distributed-ledger technologies (DLTs), of which blockchain is one, and “zero-trust security” approaches to preventing data breaches. In addition to lowering the risk of breaches, trust architectures reduce the cost of complying with security regulations, lower the operating and capital expenditures associated with cybersecurity, and enable more cost-efficient transactions, for instance, between buyers and sellers.

Trust architectures both mitigate risk and, for companies in certain industries, increase it. Cyberrisk goes down as companies use zero-trust security measures to reduce the threat of data breaches. For other industries, however, strategic risk rises with the threat of disintermediation by distributed data ledgers. Companies will also need to pay attention to the shifting role of regulatory oversight; DLT applications don’t always fit easily into existing regulatory frameworks and may prompt diverse responses from different countries and regulatory bodies. More broadly, growing cyberrisk means overall cybersecurity spending will increase dramatically. The monetization of trust technologies (such as cryptocurrency and supersecure data rooms) is poised to do the same.

One company recognized it faced exfiltration risk for sensitive data. By deploying
a zero-trust-security approach, it encrypted data, both at rest and in transit, and implemented the additional controls required to restrict cloud-service providers from accessing that same data.

One company experienced significant global service downtime due to a regional power outage. This company’s few data centers were closely located, with little of the data and network segmentation required to isolate outages.

A zero-trust-security setup diversified data storage across multiple servers, data centers, and regions, enabling greater resilience when faced with local outages.

A retail company wanted to securely segment their networks via zero-trust security to protect point-of-sale devices and cardholder information while enabling rapid connectivity, with cost as the number one priority.

The retailer achieved 50 percent reduction in security-related capital expenditures and operating expenditures by simplifying compliance with payment card industry (PCI) data-security standards.

One retailer streamlined its supply-chain management by recording all processes and actions from vendor to customer and coding them into smart contracts on blockchain.

This made it easier to trace food provenance (leading to safer consumption), required fewer human actions in the chain, and improved tracking of lost products.

The eighth technology trend reflects a confluence of advances in biological science combined with the accelerating development of computing, automation, and AI, which together are giving rise
to a new wave of innovation called the Bio Revolution. This tech trend promises a significant impact on economies and our lives and will affect industries from health and agriculture to consumer goods, energy, and materials. The biomolecules’ dimension of the revolution, which includes “omics” and molecular technologies, has evolved as the fastest-growing, most cutting edge of biological science, but biomachines, biocomputing, and biosystems are also important dimensions. Some innovations come with profound risks rooted in the self-sustaining, self-replicating, and interconnected nature of biology that argue for a serious and sustained debate about how this revolution should proceed.

The rapid pace of biological science will soon bring competitive disruption—and not just in healthcare. As biological innovations penetrate industries such as food, consumer health, and materials, they are yielding higher margins in exchange for increased personalization from consumers and patients. New markets may emerge, such as genetics-based recommendations for nutrition, even as rapid innovation in DNA sequencing leads ever further into hyperpersonalized medicine and rapidly accelerated vaccine development. At the same time, the Bio Revolution’s opportunities and risks require navigating not just competitive implications but also ethical and even moral issues.

Organizations need to assess their “bQ” or biological quotient—the extent to which they understand biological science and its implications. They should then sort out the resources they need to allocate to biological technologies and capabilities and whether to integrate those into their existing R&D or partner with science-based start-ups.

Amyris’s new production methods combine digital and biological skills to provide pure, stable skin-care products in high volume, at low cost, and from renewable resources.

Ginkgo Bioworks, which produces bacteria with industrial applications, created the Ferment Consortium to give spin-off companies full access to its genome-mining platform for cell programming.

Trace Genomics is profiling the soil microbiome to interpret health- and disease-risk indicators in farming.

Agriculture and pharma incumbents are collaborating with Caribou Biosciences, CRISPR Therapeutics, and Pairwise to harness unique biocapabilities.

Innovations in materials sciences are at the heart of the ninth trend as next-generation materials like graphene and 2-D materials, molybdenum disulfide nanoparticles, nanomaterials, and a range of smart, responsive, and lightweight materials enable new functionality and enhanced performance in pharma, energy, transportation, health, semiconductors, and manufacturing. Because they have a lighter environmental impact, next-generation materials will be critical for tomorrow’s sustainable economies. While many of these materials are still in the research stage, others are closing in on their commercial potential. Molybdenum disulfide nanoparticles are already being used in flexible electronics, while graphene has helped propel a resurgence in 2-D semiconductors. More new materials are on the way as computational-materials science combines computing power and associated machine-learning methods and applies them to materials-related problems and opportunities.

Materials design and discovery hold a critical place in the 21st-century economy, with broad potential impact spanning the transportation, health, microelectronic, and renewable-energy industries. By changing the economics of a wide range of products and services, next-generation materials with significantly higher efficiency in many as-yet-untouched application areas may well change industry economics and reconfigure companies within them.

Healthcare companies can create nano drug-delivery systems using biodegradable nanoparticles as drug carriers, for example, through micellar nanoparticles.

For example, the company NanoCarrier is creating nano drug-delivery systems for cancer pharmaceuticals.

Manufacturers can use graphene-based composites to enhance composite properties, for instance, in rust-preventing paint and in sports equipment.

For example, the company Applied Graphene Materials creates graphene dispersions that can be used for paint and coatings, as well as in composite materials.

Vertically aligned carbon nanotubes have been used to generate batteries with three times the storage and ten times the power as conventional batteries.

French company NAWA Technologies is focused on creating ultrastrong, multifunctional lightweight materials that can also store energy, whether in a vehicle, airplane, building, or mobile device.

The tenth trend reflects new technologies addressing the rapidly growing need for clean-energy generation. These include systems for smart-energy distribution in the grid, energy-storage systems, carbon-neutral energy generation, and fusion energy. These new technologies will have broad application:

As clean technologies come down the cost curve, they become increasingly disruptive to traditional business models, affecting both industry structure and competitive dynamics. Companies must keep pace with emerging business-building opportunities by designing operational-improvement programs relating to technology development, procurement, manufacturing, and cost reduction and by grasping how climate-change mandates affect energy costs and alter the balance sheet of carbon-intense sectors while increasing the performance standards that accelerate the adoption of next-generation clean technologies. Advancing clean technologies also promises an abundant supply of green energy to sustain exponential technology growth, for instance, in high-power computing.

A global conglomerate identified the solar photovoltaic (PV) industry as a promising area for growth and moved swiftly to access specialist capabilities through M&A.

Buying a major stake in a leading global PV manufacturer boosted the share price of the responsible division by 40 percent in the months thereafter.

A global brewer introduced energy-efficiency measures, process redesigns, and fuel changes targeting substantial cost savings and 50 percent carbon abatement.

An environmentally aware city tackled the next frontier of carbon abatement through a combination of behavioral and technological measures ranging from congestion pricing to building retrofits.

This city now has an opportunity to implement a bold strategy to attain its targets, continue to influence the national and global debate on climate change, and reinforce its leadership
in the field.

Frontline staff helped a wastewater plant conduct an onsite diagnostic to identify improvement levers and launch a customized transformation program to support continuous improvement.

After four years, the utility had increased its operating profit by 19 percent per year, leading to annual savings of $178 million while improving its regulatory performance score by 7 percent a year and reducing leakage by 30 percent. Customer complaints declined by almost a third.

This research examines a range of factors to identify the technology trends that matter most to top executives and the companies they lead. For every trend, we calculated a momentum score based on the growth rate of the technologies underlying the trends, which we derived from an in-depth analysis of six proxy metrics: patent filings, publications, news mentions, online search trends, private-investment amount, and the number of companies making investments. We then rolled the scores of the underlying technologies into a single composite score for the trend itself. Examining composite momentum scores—together with a given trend’s industry applicability and technical maturity—can help executives recognize how much disruption a tech trend is likely to cause and how soon that disruption will have business implications.

The underlying metrics are diverse, the better to account for the varied perspectives each represents. The number of research publications within a field provides a leading indicator of trends in technology as they emerge. Patent filings give a measure of the importance placed on a particular trend by corporations. The quantity of private investment, as well as the number of companies making investments, indicates whether a clear financial interest exists for a specific trend. Finally, search trends and news coverage reveal the level of public interest in a trend. Combining early indicators with measures of public and financial interest creates a holistic view of each technology trend and provides a good way to rank and compare their potential impact. Using the growth rate as the basis of the momentum score differentiates areas that are merely large from those that are on their way to massive.

Finally, we reviewed our analytical results with external experts on McKinsey’s Technology Council, leading to a unique perspective that combines research analysis with qualitative insights from some of the leading thinkers of our time.