IT operations: Many organizations are doubling down on cloud technologies to support business continuity, remote workforce management, proactive cybersecurity, and proactive governance because the pandemic has illustrated the need to be prepared for future business disruption.7 Nearly all legacy applications will be migrated to the public cloud by 2024,8 and analysts expect the cloud-based conferencing market to grow to over US$6.3 billion by 2024 (from US$5 billion in 2020)9 to support the remote work trend. To address the IT operations business driver, we’ll touch on four corresponding outcomes that all focus on building resilient and secure cloud applications, networks and infrastructures:
- Business operations and continuity: Looks to achieve automated business operations and redundancy for greater organizational resilience and to protect the integrity of core business services
- Remote workforce management: Aims to meet or exceed baseline operations and continuity requirements to enhance operations supporting the workforce (whether internal/external workers or technology that enables work) in achieving work outcomes
- Proactive cybersecurity: Uses intelligent automation to build on business continuity and optimized workforce requirements to streamline cybersecurity monitoring, threat detection, and remediation, particularly in light of secure cloud infrastructure requirements necessary to enable the remote workforce
- Proactive governance: Expands proactive cybersecurity automation strategies to automate a wide array of business operations, including cyber; risk; IT; compliance; and governance, via a DevOps culture that accelerates time to production by contemplating security and risk early
Data strategy: Data is the backbone of strategic decision-making in a digital world.10 With data volumes growing at a dramatic rate and despite going through a “big data” decade, some organizations still struggle to gain meaningful business intelligence. A Deloitte survey of US-based analytics professionals reveals that 63% of surveyed organizations are aware of analytics, but lack the necessary infrastructure, and are still working in silos or are expanding ad hoc analytics capabilities.11 Cloud and data modernization strategies are inextricably linked12 and, therefore, to harness actionable data intelligence, organizations can use cloud to enable data consolidation, analytics, intelligence through machine learning (ML), and insight at the “intelligent edge.”13 The secure cloud’s role in supporting data strategies could be significant, with the global cloud analytics market expected to grow by 25% to US$65.4 billion by 2025,14 cloud ML expected to reach US$13 billion by 2025,15 and cloud-adjacent technologies, including edge and quantum, on the rise.16 Given this data strategy driver, we’ll focus on four corresponding business outcomes:
- Data consolidation: Tries to bring together and secure data from across the organization whether related to workforce, customers, industries, or geographies
- Data analytics: Aims to gain meaningful, actionable business intelligence from secure data, whether consolidated or not, to aid decision-making
- Data intelligence (ML): Focuses on creating artificial intelligence (AI)–enabled, predictive business strategies with data security across a range of potential stakeholders (workforce, client, etc.), and internal and external use cases
- Data at the “intelligent edge”: Looks to harness data from across a more distributed data ecosystem and a more distributed range of computing devices (mobile, cloud, edge, and batteryless platforms) to gain meaningful business intelligence while securing data
Customer experience management: Many organizations have placed—or are looking to place—the customer at the center of their business strategy. Research suggests this to be a sound approach—companies that focus on “human-centric marketing” have been found to grow up to 17 times faster with double the 3-year revenue growth of their peers.17 Regardless of the business outcome, cloud technology can create seamless, immersive, and impactful experiences. To support the customer experience management business driver, we can focus on four corresponding business outcomes:
- Frictionless agile experience management: Attempts to deliver speed and usability to customers with secure, trusted solutions
- Omnichannel customer experience management: Focuses on securely optimizing the experience across every point of interaction with the customer, whether mobile, online, or offline
- Personalized and virtual experiences: Looks to use a number of possible technologies—AI, augmented reality (AR)/virtual reality (VR), consumer identity, etc.—to create more personalized experiences and user journeys where individuals are empowered to consent and note preferences as to what information they share and when
- Powering spatial web: Attempts to build interconnected, fully immersive, and data-driven experiences, bringing in technologies, such as AR and digital twins, to engage and delight users
Distributed ecosystems: The distributed cloud market is expected to grow by as much as 24% to reach US$3.9 billion, by 2025.18 Digital ecosystems have gained attention, with 52% of CIOs in an industry survey saying their “deep and integrated digital ecosystems” greatly enhance innovation.19 Academic research has shown that creating a large network of ecosystems can help harness distributed innovation to better solve externally driven problems.20 While platform and ecosystem models have been around for decades, the continued rise of disruptive and next-generation technologies we discussed in Figure 1 have pushed organizations to take a closer look at whether these strategies are ripe for innovation. For the distributed ecosystems business driver, we can focus on four corresponding business outcomes:
- Enterprise platforms: Supports a centralized data or product delivery strategy with a consistent experience and secure, centralized data collection across users21
- Connected digital supply chain: Streamlines supply chain operations and enables secure, predictive supply chain planning for greater business resilience and profitability22
- Digital ecosystem: Enables secure data strategies across distributed business networks to increase transparency, streamline operations, reduce fraud, and address other ecosystem concerns
- Network of ecosystems: Aids with solving complex, externally driven problems if they reach beyond the organization’s business ecosystem of direct individuals (workers, customers) and organizations (partners, suppliers) to individuals or organizations that are loosely connected (or networked) to its ecosystem, and thereby might impact it
Once established and prioritized, these business drivers and outcomes can provide a useful starting point to think through the corresponding technology priorities.
Technical considerations impacting the future of cloud innovation
CIOs can be an important partner for the CDO and cloud-innovation business stakeholders to align multiple innovation programs across shared goals and where solutions may be extensible. Conversely, CIOs are well-positioned to offer guidance on where cloud innovation programs require vastly different solutions. These conversations can be streamlined by thinking through four technical factors:
1. The operating model and how centralized or distributed it is
2. Adoption of standards
3. The infrastructure-adaptation potential and how restrained by legacy technology it is
4. The execution strategy and how cloud(s)-centric it is
As with the business drivers, each of these technical factors can be thought of as a continuum, with technical decisions aligned to business requirements—consciously making trade-offs all the while.
Operating models for cloud today are largely centralized. This may be appropriate for product strategies—build this solution for a defined market—but could pose a challenge for programs that cut across teams, business units, industries, and geographies. In those cases, more distributed operating models—such as a committee or center of excellence—might work better; 75% of surveyed organizations with cloud-first strategies are already operating in this way.23
Adoption of standards varies. Standards can be technical (e.g., security), data-driven, or industry-specific. Some organizations prefer open-source software.24 Others follow specific cloud or technical standards, of which there are hundreds, if not thousands.25 There may even be a mandate to use certain standard toolkits, coding languages, or vendors. Think about when standard tools might speed development, when they may constrain future options, and what the trade-offs are.
Infrastructure adaptation is about how easy (or difficult) it is for an organization to modernize technology today and into the future. A regression approach retrofits new technologies into old contexts, and while it may work for modernizing an old building to become energy efficient, it doesn’t typically work well for IT. A stagnant approach tries to make incremental change to improve the solution over time. Cloud strategies, however, tend to thrive when they trend toward evolutionary (migrating and modernizing solutions) or agile (developing iteratively). This allows organizations to eliminate historical constraints and create a stronger foundation for change.26
Execution strategy can have a range of options: Ubiquitous clouds (with flexible computing anytime and anywhere), plural clouds (which bring together multiple cloud solutions), hybrid cloud (which requires a coordinated public/private cloud strategy), and cloud captive (where organizations are locked into a cloud-only strategy, for better or worse). While hybrid cloud is the current standard approach,27 certain scenarios may require something different.
And that’s the important point—to ground business strategy in a concrete technical reality, the CIO and the chief cloud officer can think through these cloud innovation scenarios across the C-suite.
Four potential cloud innovation scenarios
Cloud innovation can support a multitude of different business strategies and scenarios, but how can organizations achieve those possibilities? This is where scenario thinking can help compare and reconcile competing priorities, break silos, and drive collaboration—all to achieve better outcomes and value.
To illustrate, think back to the business drivers (IT operations, data strategies, customer experience management, and distributed ecosystems). These drivers can help organizations to start to innovate differently. We’re going to show a few examples to help place you in the framing and help you see how it can be used to ground overlapping business and technical requirements of four C-suite cloud innovation scenarios.
First, a short explanation of our methodology.
We plotted the two more operational drivers on the x-axis (i.e., the organization’s propensity to prioritize internal operations or external customer experience management) and data maturity on the y-axis. These produce four scenarios: reactive responders for the CEO; experience innovators for the CMO/CxO; proactive data defenders for the CISO; and AI-fueled entrepreneurs for the CDO/chief data scientist (figure 3).