Trends reshaping the future of IT and business

# New technologies that accelerate iteration are constantly impacting the market, and people can easily become numb to this impact. While every technology has the ambition to change (or even "revolution") the business world, our analysis of some of the more meaningful technology trends provides a compelling case that something important does. is happening.

These technology trends collectively accelerate the key characteristics that define the digital age: granularity, speed, and scale. It’s the scale of these changes—in computing power, bandwidth, and analytics technology—that opens the door to new innovations, businesses, and business models.

For example, the emergence of cloud computing and 5G has exponentially increased computing power and network speeds, thereby promoting greater innovation. The development of the metaverse of augmented reality and virtual reality opens the door to virtual R&D (e.g. through digital twins and immersive learning). Advances in artificial intelligence, machine learning and Software 2.0 (code written by machines) have brought a range of new services and products – putting everything from self-driving cars to connected homes within reach.

People spend a lot of effort identifying technology trends, but less attention is paid to the impact of these changes. To help understand how management will need to adapt to these technology trends over the next three to five years, we spoke with business leaders and thought leaders on this topic. We are not looking for predictions, but rather explore realistic scenarios, their implications, and what executives may need to do to meet the challenges.

This discussion identified some broad, interrelated changes, such as how the rapid growth of technology has a centrifugal force on enterprises, pushing innovation to expert networks at the edge of the company; how the speed and diffusion of these innovations require New approaches build continuous learning around the skills needed; how these democratizing forces mean IT can no longer serve as a centralized controller of technology deployment and operations, but needs to become a key enabler and influencer.

1. Edge Innovation

Key Technology Trends

We estimate that 70% of companies will adopt hybrid or multi-cloud management technology , tools and processes. At the same time, 5G network speed will be about 10 times faster than the current 4G LTE network speed. Based on 4G, 5G Internet speed can be increased by 100 times, reaching 20Gbps per second. By 2024, more than 50% of user traffic will be augmented by AI-driven speech, written text or computer vision algorithms, while global data creation is expected to grow from 64.2 zettabytes (zettabytes) in 2020 to 180 zettabytes in 2025 ZB or above. The low-code development platform market is expected to grow at a compound annual growth rate (CAGR) of approximately 30% by 2030.

Shift 1: Innovation evolves around personal networks of experts at the edge of the enterprise, supported by the ability to scale across the business

These technologies promise to use virtually unlimited computing power and Massive data sets, along with huge leaps in bandwidth at low cost, make it cheaper and easier to test, release and rapidly scale innovations. The resulting acceleration in innovation will mean that businesses will face more disruption from more sources. Centralized strategy and innovation functions cannot expect to stay in lockstep. Companies will need to become more involved in networks outside the enterprise to identify, invest in and even acquire promising opportunities.

Corporate venture capital funds (VCs) with centralized teams have always sought out and funded innovation, but their track record has been suboptimal, often because the teams lack the necessary skills and are not aligned with The changing needs of each business unit vary widely. Instead, companies need to figure out how to leverage their “front-line people,” particularly business domain experts and technologists, so that they can effectively serve as the venture arm of the company. This is because the people who code and build solutions are often well connected to strong external networks in their field and have the expertise to evaluate new findings.

For example, a pharmaceutical company taps its own expert researchers in various fields (such as gene expression), and these people have a good understanding of the outstanding talents in this field outside the enterprise.

While businesses will need to create incentives and opportunities for engineers to build and engage with their networks, the key point must be to empower/empower teams so that they can use their allocated budget as they see fit, and decide on techniques to achieve its goals (within prescribed guidelines).

The IT organization of the future can play an important role in building the ability to scale and make innovation work for the business, which has traditionally been a challenge. Individual developers or small teams often don't naturally think about how to scale an application. When non-technical users across the organization use low-code/no-code (LC/NC) applications to design and build with point-and-click or pull-down-menu interfaces This problem may be exacerbated during the procedure.

One pharmaceutical company knows this well and gives business units the flexibility to use a non-standard innovative idea when it proves to be superior to existing methods. In return for this flexibility, the business must commit to helping the rest of the enterprise use this new idea and build it into the company's standards.

When considering how this scalability might work, companies could, for example, assign senior developers to "productize" applications by refactoring the code so that they can scale. IT leaders can provide tools and platforms, easily accessible reusable code libraries, and flexible, standards-based architectures to make it easier to scale innovation across the business.

Questions for leaders to think about

•What incentives will best encourage engineers and domain experts to develop, maintain and leverage their networks?

•What processes are in place to track and manage venture capital activities?

•What capabilities do you need to identify innovation opportunities and “industrialize” the best ones so they can be deployed across the enterprise Share?

2. A Culture of Continuous Learning

Key Technology Trends

Artificial Intelligence, Machine Learning, Robotics and Others Progress increases the rate of technological change tenfold. By 2025, we estimate that 50 billion devices will be connected to the Industrial Internet of Things (IIoT), and 70% of manufacturers are expected to use digital twins regularly (by 2022). By 2025, approximately 70% of new applications will use low-code/no-code (LC/NC) technology, up from less than 25% in 2020. The global Metaverse revenue opportunity could approach $800 billion by 2024, up from about $500 billion in 2020. Entrepreneur and futurist Peter Diamandis said the proliferation of technological innovation means we are expected to experience more progress in the next 10 years than in the past 100 years combined.

Shift 2: Technical literacy becomes core to every role, requiring continuous learning and building on the level of personal skills when deployment is required

With technology As growth and expansion push innovation to the edges of the enterprise, companies need to be ready to integrate the most promising options from all fronts. This will create huge opportunities, but only those companies that develop real technical intelligence through a culture of “continuous learning” will be able to seize this opportunity. A cornerstone of this effort includes training employees at all levels—from “citizen developers” using LC/NC tools or working in entirely new environments like the Metaverse to full-stack developers and engineers who will need to continually develop their skills , to keep up with changing technology. We have seen situations where poorly trained employees using LC/NC produce sub-optimal products.

While there will always be a need for a more formalized path to basic learning, we anticipate an accelerated shift from regular taught courses to continuous learning to deliver different technical skills across the enterprise. In practice, this will mean guiding employee development around imparting skills. This requires breaking down capabilities into minimal sets of combined skills. For example, one large technology company created 146,000 skills data points for the 1,200 technical skills it assessed.

The key is that these skill “pieces” – such as a piece of code or a video of a specific negotiation strategy – need to be integrated into the workflow so that they can be delivered when needed. This might be called a "LearnOps" approach, where learning is built into operations. Netflix has built this integrated mindset, where data scientists work directly with product managers, engineering teams, and other business units to design, execute, and learn from experiments/trials.

Just as important as being able to learn is establishing a learning culture where continuous learning is expected and easy to do. What may be instructive is the way top engineers learn, a community that is highly aware of the need to keep their skills updated. Their habit of sharing code is ingrained and they are always attracted to new projects. For example, one advantage of using open source is the built-in community that can constantly update and review the code. In the same spirit, we see companies building products that budget extra time for people to try out new tools or technologies. Other companies are also budgeting for "learning buffers" to deal with setbacks in product development so teams can learn from them.

With broad, open and thoughtful information sharing as a core value, Netflix built the Netflix Experimentation Platform as an internal product that serves as a repository of solutions for future teams to reuse. It has a product manager and innovation roadmap with the goal of making experimentation a simple and integral part of the product lifecycle.

To support this continuous ability to learn and experiment, businesses need to be able to accept mistakes, with a focus on limiting the impact of potentially costly errors such as loss or misuse of customer data. IT will need to build protocols, incentives and systems to encourage good behavior and reduce bad behavior. Many companies are beginning to adopt practices such as automated testing to prevent errors from happening in the first place; creating spaces where errors do not affect other applications or systems, such as quarantine zones in cloud environments; and establishing resiliency protocols.

Questions for leaders

•Have you listed the most important skills your company needs?

•Advanced user data analysis What is the minimum level of learning required for operators and operators?

•How do you track what employees are learning and whether that learning is effective and translates into better performance?

3 .IT as a Service

Key Technology Trends

It is estimated that by 2028, the global cloud microservices platform market revenue will increase from 952 million in 2020 USD increased to $4.2 billion. GitHub already has more than 200 million code repositories and is expected to have 100 million software developers by 2025. Nearly 90% of developers are already using APIs. Software 2.0 creates new ways of writing software, reducing complexity. From 2021 to 2028, software purchased by companies from cloud service platforms, open repositories and software as a service (SaaS) will grow at a compound annual growth rate of 27.5%.

Shift 3: IT becomes a driver of product innovation by delivering small, interoperable chunks of code

When innovation is pushed to the edge of the enterprise, continuous learning When culture permeates the enterprise, the role of IT changes dramatically. This requires IT to transform from its traditional role of "protector of large technology assets" to "provider of small code blocks." The gold standard of IT efficiency will be its ability to help people stitch together pieces of code into useful products.

We have seen many successful practice cases. For example, employees at G&J Pepsi Bottling Company with little experience in software development developed an app that could examine images of store shelves, identify the number and type of bottles on them, and then automatically complete refills based on historical trends. One pharmaceutical company grew its low-code platform from 8 to 1,400 users in just one year. Business users outside of IT are now building applications with thousands of sessions per month. According to a McKinsey survey, companies that provide support for "citizen developers" score 33% higher on innovation than the bottom quartile of companies that do not provide this support.

These developments point more towards a "do-it-yourself" approach to technology, where IT builds useful reusable chunks of code, sometimes assembling them into specific products, and makes them available through user-friendly cataloging systems for Used by businesses to create desired products. IT provides guidelines such as API standards and instructions on the environments in which code may be most useful; protects the most sensitive information, such as customer data and financial records; and tracks their adoption. This tracking capability will become even more important as bots, AI, algorithms, and APIs proliferate. Transparency alone is not enough. IT will need to understand all activities through advanced technical performance and management capabilities, as well as the development of new roles (data diagnostic experts, robot managers, etc.).

This "IT-as-a-service" approach puts product at the center of the operating model and requires organizing IT around product management. Some companies have been moving in this direction. But achieving the scale needed to support fast-paced and broader innovation also requires a deeper commitment to product owners, working with leaders on the business side of the company and managing teams with true profit and loss (P&L) responsibility. Many businesses, from traditional to digitally native, are finding that having product leaders set overall product and portfolio strategies, drive execution, and empower product owners to drive innovation aligned with business results and P&L metrics increases the flow of technology Deliver return on capital and accelerate the pace of innovation.

Questions for leaders to think about

•What is your vision for how the role of IT organizations will change to democratize technology?

• How will you advance the role of technical product manager, and do you have a roadmap for developing this role?

•What systems do you need to establish to manage and track code usage, reuse, and performance?

4. Expanding the Boundary of Trust

Key Technology Trends

It is estimated that by 2022, almost 100% of mobile phones will have biometric capabilities. Devices such as smartphones will use biometrics in transactions. The effectiveness of these technologies has improved significantly, with the best facial recognition algorithms improving 50-fold since 2014. These developments are fueling a profound unease in the relationship between technology and its consumers. A Pearson Institute and AP-NORC Center for Public Affairs Research survey found that "about two-thirds of Americans are very or extremely concerned about hacking involving their personal information, financial institutions, government agencies or certain utilities."

Shift 4: Trust expands to encompass broader stakeholder concerns and becomes the responsibility of the entire enterprise

These seismic shifts in technology power and capabilities will create more customers Contact point. Even as IT's role in the enterprise becomes more of an enabler, the expanding digital environment means IT must expand its trust capabilities in security, privacy and networking. So far, consumers have largely embraced the conveniences technology offers—from ordering products online to remotely adjusting the temperature in their homes to monitoring their health through personal devices. In exchange for these conveniences, consumers are willing to provide some personal information. However, as technology develops further, concerns about privacy and trust are growing, raising the stakes for the broader topic of trust. Consumers are increasingly aware of their rights to identity, making decisions based on values, and demanding ethical use of data and responsible AI technology from businesses.

The most obvious concern among consumers is cybersecurity, an ongoing issue that has been put on the boardroom agenda. But the issue of technology-driven trust is much broader, driven by three characteristics. One is that the sheer volume of personal data such as biometrics collected by companies and governments raises concerns about privacy and data misuse; second, personal security issues are becoming more and more common in the real world. For example, wired homes, connected cars, and the medical IoT are all attack vectors that could impact people's lives; a third problem is that advanced analytics seems too complex to understand and control, leading to deep unease about people's relationship with technology. This question is driving the development of “explainable AI” and the movement to eliminate bias in AI.

A further factor that adds to this complexity is that enterprises often need to manage and ensure trust across entire technology ecosystems. Take wired homes, for example. The proliferation of devices such as virtual assistants, security, communications, power management and entertainment systems means a large number of vendors need to agree on standards for managing home connected security networks.

These trends require further expansion of the boundaries of trust. Unless companies rethink how they manage and cultivate this trust, many of the important advantages enjoyed by incumbents—existing relationships with customers and proprietary data—will be at risk. Companies need to consider putting identity and trust management at the core of customer experience and business processes. This can only be achieved effectively when the company assigns a dedicated leader with real authority and board priorities, and takes enterprise-wide responsibility across the trust and safety areas. Given the technological underpinnings of this trust environment, IT will need to play a key role in monitoring and remediation, such as assessing the impact of new legislation on AI algorithms, tracking incidents, identifying the volume and nature of high-risk data processing activities and automated decision-making, and monitoring Consumer trust levels and issues affecting them.

Questions for leaders to think about

•Who is responsible for the trust and risk posture across the enterprise?

•How do you connect customer trust with How integrated are the entire cybersecurity processes?

•What privacy, trust and security processes are in place to manage the entire lifecycle of data?

The pace of technological change will inevitably continue to accelerate. In the future, successful technology leaders will need to not only adopt new technologies but also build capabilities to absorb ongoing change and make it a source of competitive advantage.

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