Macro technology forces
As the unprecedented expansion of technology-driven innovation fuels a high-stakes game of competitive whack-a-mole, an organization’s ability to exploit technology to its advantage will determine its survival. Leaders across sectors now routinely elevate technology to a strategic business priority.
Emerging technology trends stir competing emotions and narratives, often pushing us beyond our comfort zones. We’re inseparable from our mobile devices, but they constantly interrupt us with an overwhelming flow of information. Cognitive assistants are helpful time-savers, but most of us find them a little creepy. Investors are bullish on flying taxis, but savvy consumers are distrustful of empty media hype and promises that exceed reality. And a painful tension exists between the possibilities of exciting novel technologies and the responsible exploration of technology domains at the forefront of an organization’s 18-to-24-month investment road map.
Eleven years of research and deep engagement with global business and technology executives have helped bring clarity to this ongoing drama—and a simpler way to think about significant technology developments. Last year’s Tech Trends report1 explored nine macro technology forces that have been—and continue to be—the backbone of business innovation and transformation: digital experience, analytics, cloud, digital reality, cognitive technologies, blockchain, the business of technology, risk, and core modernization. For a decade, we’ve been tracking their emergence and eventual ascent, exploring how organizations are using them to innovate and drive purposeful, transformational change.
Digital experience, analytics, and cloud are enabling technologies that have proven their value—and then some—over the past decade. They are the basis of numerous successful corporate strategies and new business models.
This decade’s disruptors are digital reality, cognitive technologies, and blockchain. Adoption is on the uptake, with business cases multiplying across industries. We expect these disruptors to spark surprises throughout the 2020s.
The business of technology, risk, and core modernization are foundational technologies. To carry the weight of technology-driven transformation and innovation initiatives, they need to be stable, strong, and sustainable.
These macro forces help drive meaningful conversations about emerging technologies not only with the CIO, CTO, and tech shop but with the CEO and the broader C-suite, board members, and line-of-business presidents. Discussing emerging technologies in the context of this framework can help simplify the tsunami of tech advances and ground in reality the investments and innovations coming from labs, startups, and R&D centers around the world. Smaller trends can be plotted on the evolutionary trajectory of these macro forces.
This year’s update takes a fresh look at enterprise adoption of these macro forces and reviews how they’re shaping the tech trends predicted to disrupt businesses over the next 18 to 24 months. We also peek beyond the horizon and unveil three macro forces—ambient experience, exponential intelligence, and quantum—that we expect to shape enterprise and technology strategies into the 2030s and beyond.
When we first began exploring digital experience, analytics, and cloud a decade ago, we understood the possibilities but weren’t quite sure how significant their impact would be. Since then, these now-familiar enabling forces have powered the disruption of businesses, operating models, and markets. They continue to evolve at an astounding pace.
Digital experience continues to be a critical driver of enterprise transformation—in fact, 64 percent of participants in Deloitte’s 2018 global CIO survey say digital technologies will affect their businesses in the next three years.2 Since we examined this trend last year in Beyond marketing: Experience reimagined,3 organizations are dispensing with the traditional notion of customer acquisition-focused marketing, focusing instead on creating more human-centric interactions—including with their own employees and business partners.
This year, in human experience platforms, we discuss how leading organizations are creating customized, emotionally intelligent digital experiences based on individuals’ behaviors, preferences, and emotions using an integrated array of AI capabilities such as voice stress analysis and microexpression detection tools. Consider, for example, the use of EEG- and machine learning-enabled headsets that shed light on situations that distract or create stress for employees, enabling businesses to design better workflows and work environments.
Analytics includes foundational capabilities and tools that generate powerful insights. Data management, data governance, and supporting architecture are age-old problems that not only are critical building blocks for AI programs but are tactical concerns as organizations work in a dynamic and complex regulatory environment with mandates on data residency, privacy, and usage.
CIOs understand what’s at stake: 60 percent of them say that data and analytics will affect their businesses in the next three years.4 But the issue is becoming only more challenging. The tried-and-true concepts of “data at rest” and “data in use” are joined by “data in motion,” which is supported by tools for data streaming, ingestion, classification, storage, and access. The good news? Cloud, core modernization, cognitive, and other technologies are bringing fresh solutions to an exceptionally complicated challenge.
Developments in data analytics have helped advance many of this year’s trends. For example, the ability to efficiently and cost-effectively process and integrate large amounts of data has spurred the creation of more advanced digital twin technology—but it has also created a deficit of trust, leading to our focus on ethical technology and trust.
Cloud’s takeover of the enterprise is nearly complete. Ninety percent of organizations use cloud-based services5 and they aren’t putting on the brakes. In fact, cloud investments are expected to double as a percentage of IT budget over the next three years.6 As we predicted in 2017, the use of cloud, extending beyond infrastructure, has given rise to everything-as-a-service, enabling any IT function to become a cloud-based service for enterprise consumption.7 Hyperscalers—the handful of massive companies that dominate the public cloud and cloud services industries8—have shifted investments higher up the stack, providing platforms for advanced innovation in the other macro forces, including analytics, cloud, blockchain, digital reality, and in the future, quantum.
Cloud has also forced the reimagining of some tried-and-true roles. For example, as we discuss in architecture awakens, giving architects the ability to take full advantage of modern cloud-based offerings plays a critical role in developing complex IT systems and applications in a hybrid world.
Today’s disruptors—digital reality, cognitive technologies, and blockchain—are the descendants of experience, analytics, and cloud. As the change agents of the coming decade, these newer trends may no longer be considered novel, but they’re on the cusp of becoming as familiar and significant as their predecessors.
Digital reality technologies, including AR/VR, mixed reality, voice interfaces, speech recognition, ambient computing, 360° video, and immersive technologies, promote more natural user engagement by seamlessly extending a human-centric experience beyond the confines of keyboards and screens. The goal is natural, intuitive, and potentially imperceptible interactions with underlying technologies.
Commercial applications of digital reality are growing.9 For example, as discussed in human experience platforms, many companies are using digital reality technologies to deepen emotional connections and empathy among customers and employees. And in digital twins, we see how digital reality can help bring the digital twin to life. Using AR, a manufacturer can provide its workers with a view into 3D content from a digital twin, improving worker productivity.10
Cognitive technologies, such as machine learning, neural networks, robotic process automation, bots, natural language processing, neural nets, and the broader domain of AI, have the potential to transform nearly every industry. These technologies personalize and contextualize the human-technology interaction, allowing businesses to provide tailored language- and image-based information and services, with minimal or no human involvement.
Demand for cognitive technologies is skyrocketing—IDC forecasts spending to reach US$77.6 billion in 202211—although their potential benefits are accompanied by significant trust and tech ethics considerations. As we discuss in ethical technology and trust, a company can help build a reputation as a trusted global brand by being transparent about the use of cognitive technologies, evaluating the impact on customer trust, and proactively seeking to understand and mitigate the effects on customers and their data.
Blockchain is a critical technology priority for more than half of those who participated in Deloitte’s 2019 Global Blockchain Survey, a 10-point increase from 2018. Eighty-three percent could identify compelling blockchain use cases, a 9-point increase from the previous year. Results suggest that in 2019, the topic of enterprise blockchain discussions shifted from, “Will blockchain work?” to, “How can we make blockchain work for us?”12
Financial services and fintech companies continue to lead blockchain development, but other sectors—notably, government, life sciences and health care, and technology, media, and telecommunications—are also advancing blockchain initiatives.13 Similar to cloud, our architecture awakens trend discusses how blockchain provides architects with an opportunity to do bold new things, disrupting the status quo as they work on multidisciplinary teams to help achieve business outcomes.
The business of technology, risk, and core modernization may seem prosaic and dull, but these forces are undeniably the heart of the business. And companies continue to make considerable investments and advances in these well-established domains. Combined, they provide a reliable, scalable foundation for digital transformation, innovation, and growth, and are a requirement for successful investments in analytics, cognitive, blockchain, and other disruptive technologies.
The business of technology—how IT operates—is evolving as technology and business strategies converge. As companies increasingly look to reengineer IT not only to deliver operational excellence but to partner with business functions to drive value creation, many IT teams are shifting their focus from project delivery to product and business outcomes and adopting collaboration-enabling development methodologies such as Agile and DevOps.
The supercharged technology function can then help enterprises become more agile in their response to technology-driven market and business changes. In finance and the future of IT, we take a closer look at how new approaches to technology finance are helping fuel business agility. And in architecture awakens, we examine how organizations are redefining the architect’s role to cultivate responsiveness to overarching business needs and encourage collaboration with business and end customers.
Risks facing enterprises in an innovation-driven era extend far beyond traditional cyber, regulatory, operational, and financial threats. Participants in the 2019 CEO and board risk management survey14 said the top threats to their companies were those related to new disruptive technologies and innovations, ecosystem partners, brand and reputation, and organizational cultures—even as they acknowledged they hadn’t prepared for or invested appropriately to manage these risks.
Beyond the essentials of compliance and security, organizations are approaching the broader issue of trust as a corporate strategy driven by the potential risks that emerging technologies could have on products, services, and business goals. Ethical technology and trust examines the broad implications of trust—including ethics and responsibility, privacy and control, transparency and accountability, and security and reliability—on an organization’s people, processes, and technology.
Core modernization reflects the ongoing pressures that digital transformation, user expectations, and data-intensive algorithms put on core systems in the front, mid, and back office. Whether it’s digital finance, a real-time supply chain, or a customer relationship management system, core systems support key business processes. Many CIOs recognize that their legacy systems lack the agility to innovate and scale, with 64 percent of CIO survey participants currently rolling out next-generation ERP or modernizing legacy platforms.15
In an era of instantaneous, always-on, tailored interactions, organizations need to lower their overall technical debt. Thoughtful approaches to modernizing the core—reengineering existing legacy systems, refreshing ERP systems, and rewriting systems—are more important than ever. Architecture awakens discusses how technology architects are building on future-forward architectures that leverage new platforms to get the benefits of agility, automation, security, and scalability.
Emerging forces on the horizon
As the three disruptor forces are gaining ground and are poised to make significant business contributions in the coming decade, three technology developments and innovations—the horizon next—are waiting in the wings. We will begin to feel their impact toward the end of the 2020s.
Ambient experience envisions a future in which technology is simply part of the environment. Computing devices continue to increase in power and shrink in size. These ever-smaller devices are evolving our input from unnatural (pointing, clicking, and swiping) to natural (speaking, gesturing, and thinking) and their interactions from reactive (answering questions) to proactive (making unanticipated suggestions).
As devices become seamless and ubiquitous, they and we are becoming increasingly inseparable. Imagine a future world where tiny, connected, context-aware devices are embedded throughout the office, home, and beyond, functioning as part of the background. Or neurofeedback technology that today enables game-playing through brainwave analysis16 could serve as the foundation for direct brain and neural interaction, allowing us to think a question or request and have an appropriate response or action delivered to our ambient experience. For example, thinking, “I need to leave for the airport in an hour” could trigger a cascade of background activity, including arrangements for automated flight check-in, a virtual boarding pass for biometric screening, a self-driving car programmed to activate at the correct terminal, setting your home smart system to “away,” and halting deliveries for the duration of the trip.
Exponential intelligence will build on today’s cognitive capabilities. Today, machine intelligence can find patterns in data but can’t interpret whether those patterns have inherent sense. It lacks the ability to recognize and respond to the nuances of human interaction and emotion. And it is also very narrow—it can defeat a human chess grandmaster but can’t understand the need to flee from a room on fire.
The future promises more. With semantic and symbolic understanding, machines will be able to tease out actual causality from spurious correlation. With a combination of technologies from human experience platforms, our virtual assistants will increasingly be able to recognize—and adapt to—our moods. And as researchers make progress at creating broad, not just narrow, expertise, exponential intelligence will be able to move beyond the statistical and computational. It will ultimately lead to more capable AI with, dare we say, personality.
Quantum harnesses the counterintuitive properties of subatomic particles to process information and perform new types of computation, communicate “unhackably,” miniaturize tech, and more. For quantum computing, the special properties of these quantum bits, or qubits, have the potential to create exponential change. By manipulating individual particles, quantum computers will be able to solve certain highly complex problems that are too big and messy for current supercomputers—from data science to material science.
As researchers overcome current technical limitations, quantum computers will increasingly supplement classical computers. Data scientists will be able to scan ever larger volumes of data for correlations; material scientists can use qubits to simulate atoms in ways that are impracticable on classical computers; and fascinating possibilities exist in many other areas including communications, logistics, security and cryptography, energy, and more.
Joaquin Duato, Vice chairman of the executive committee, Johnson & Johnson
Johnson & Johnson helps people live longer, healthier lives by creating innovative medicines, life-altering medical devices, and trusted consumer products. While there’s no denying that technology touches every aspect of our business, what matters most to our patients and customers is how our products help improve their lives. Technology absolutely plays an important role in getting us there, but it is a means to achieving our greater purpose of improving human health.
Within this context, technology’s potential has always been front and center in some areas of our business, such as R&D. What has changed in the last few years is that now everyone appreciates that technology is an enabler, everywhere in the organization, across lines of business, functions, and our talent pool. Today, the convergence of multiple disruptive technologies is helping us generate more value for our stakeholders by making better decisions and working more productively.
First, we’re making better business decisions thanks to data science. Given J&J’s reach, there is tremendous potential in connecting our data and embedding higher-quality, more efficient, and increasingly predictive decision-making tools across the organization. To do this, we are working cross-functionally to build our data science foundation by understanding what types of data are available, cleaning and engineering it so it can be analyzed more easily, and defining our next-generation data standards and architecture. The results are already impressive. For example, supply chain leaders are using advanced analytics to plan and improve process controls. In addition, R&D depends on data science to advance clinical trials and screen new medicine candidates faster than ever before so we can deliver safe and effective new medicines to patients in need.
We’re also using data science to help doctors make better patient health care decisions. For a hypertension study, J&J scientists collaborated with the Observational Health Data Sciences and Informatics network to perform research on hundreds of millions of patient records within the network’s international database.17 The study included insurance claims data and patient records from 4.9 million patients, making it the most comprehensive study ever on first-line drugs used to reduce hypertension. Rather than conducting single pairwise comparisons of two medicines for a given outcome, as most studies do, data science technologies enabled the team to evaluate 22,000 pairwise comparisons at once. By accelerating the research process, advanced analytics and cognitive technologies can help doctors deliver better patient care.
Second, we’re using intelligent automation—automation technologies combined with artificial intelligence—to give our employees the gift of time. Intelligent automation reduces repetitive and routine work while generating insights that employees can use to improve compliance, quality, and speed. For example, our finance collections team automates routine tasks, giving members more time to engage with customers to resolve disputes. This has led to improved cash flow, increased productivity, greater efficiency, and enhanced job satisfaction.
Over the past 18 months, we’ve automated nearly 30 global processes affecting 300,000 transactions. We’ve improved business outcomes and quality, while giving back more than 15,000 hours to our teams. And we’ve merely scratched the surface of what’s possible—we intend to scale these solutions across our enterprise.
These overarching initiatives are underpinned by investments in cloud, core modernization, comprehensive cyber risk strategies, and more. Our employees are just like consumers in that they want to have a frictionless experience with technology in their day-to-day lives, so we’re focusing on improving their digital experience wherever we can. We’re simplifying workflow, making processes less complicated, and shifting to modern user-centric designs powered by data science and intelligent automation.
All of this requires an evolved technology organization that works as a strategic partner and not just a service provider. Our technology team’s role is to ensure we apply disruptive technologies in concert to help the organization deliver stronger outcomes, and we’re introducing ways to measure the connection between the function’s performance and the outcomes the business cares about. For example, how can we use metrics to show that R&D is making better decisions because our technology function is delivering cleaner data?
In the same way that the technology organization needs to understand the business outcomes it supports, our executives need to recognize how technology can help them achieve the outcomes they desire. We don’t expect our executives to become programmers, but they should be able to identify how, when, and where technology can help them drive better results. And we want them to develop a dose of healthy skepticism so they can distinguish between hype and technologies likely to deliver lasting outcomes.
We look at technology as an enabler that helps our people progress in their careers, become proactive change agents, and deliver better outcomes. We take a simple view of new and emerging technologies: They are valuable because they help us achieve business results that are meaningful to our patients and customers—and because they help us create a better, healthier world.
Rob Carter, EVP and CIO, FedEx
For nearly half a century, technology has underpinned critical business and logistics operations on which FedEx customers depend. Almost a decade ago, we committed to an expansive technology renewal initiative based on an ongoing vision for cloud and everything-as-a-service. We began a journey to simplify and modernize our monolithic legacy systems by creating a collection of orchestrated microservices.
We’re wrapping up the primary phase of the IT renewal initiative, and it’s nothing less than a complete refactoring of legacy software applications that typically have long development, testing, and deployment cycles. Our new service-oriented, cloud-based model is more value-driven. The technology team orchestrates software functions as interoperable microservices that can be used across multiple platforms. They are smaller, incremental, and modular, with iterative delivery cycles that enable us to rapidly adapt to ever-changing business circumstances and help us remain in alignment with our customers as they adopt API- and service-driven architectures and workflows.
As the Internet of Things, advanced analytics, and blockchain emerged, we were able to leverage them to sustainably develop innovative new products and services for our customers. We’ve been able to position ourselves ahead of the curve on these and other emerging technologies. For example, we developed and are testing small, embeddable IoT sensors—each about the size of a pack of gum—that provide drop-in connectivity using Bluetooth Low Energy (BLE) wireless networks. This allows us to dramatically expand the amount of shipment data we collect beyond date, time, and location stamps to include temperature, speed, and a host of other measurements. The application of real-time analytics to the sensor-collected data improves visibility into the transportation network, automatically predicts the flow of shipments, and optimizes delivery routes by dynamically routing shipments to bypass network clog points.
When IoT and analytics are combined with blockchain, they have the potential to improve existing chain-of-custody systems and processes. Embedded IoT sensors can automatically transmit data to a blockchain ledger as a shipment moves from point of supply to point of demand, enabling carriers, regulators, and customers to track the provenance of goods, combat illegal and counterfeit products, and simplify the cross-border shipping process. Ultimately, we expect the impact of these technologies to extend beyond product shipments to the end-to-end life cycle of a product as it moves through the supply chain.
To stay ahead of the innovation curve, we must be responsive, which requires an agile framework that allows us to rapidly and iteratively adapt, deploy, and pivot when the market demands it. For instance, our experiments with sensor-based logistics stretch back more than a decade with the launch of our SenseAware device. Initially, we deployed sensors that relied on cell phone networks, migrating to BLE network technology when it proved more efficient. Large and expensive, the original sensors had to be reclaimed and reused. As IoT capabilities matured and became more cost-effective, we were able to roll out smaller, less expensive sensors at scale.
We also embrace risk-taking innovation when the potential reward outweighs the risk. For example, we calculated that the cost of experimenting with blockchain and potentially concluding that it wasn’t useful would be a fraction of the cost of not making an early blockchain move at all. Our willingness to take an early risk paid off. As a charter member of the Blockchain Research Institute and current standards chair of the Blockchain in Transport Alliance, we have access to invaluable contacts and resources in the blockchain industry.
We know this is a continuous journey—we can’t ever stop transforming. New competitors are agile and technologically savvy, so we plan to continue to evolve our analytics capabilities and to integrate artificial intelligence into the logistics network. And there are a few more legacy systems whose long tentacles haven’t been fully pried out yet. But because we can’t predict the next innovation or market force, we haven’t locked ourselves into processes, investments, or technologies that aren’t adaptable to future unknowns. I don’t always know what’s coming next, but with an adaptable set of services and the ability to be agile and iterative, I know we’ll be much faster at delivering value.
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