While global spending on digital transformation is projected to reach $3.9 trillion by 2027, according to the research firm IDC — a staggering 70% of digital transformation initiatives fail to meet their intended business goals, according to research from Boston Consulting Group and McKinsey.
The disconnects leading to failure often begin at the very outset of these initiatives. For instance, 87% of senior business leaders say digitalization is a company priority, but only 40% of organizations report achieving positive business outcomes from digital investments. This gap highlights the common critical misalignment—companies invest heavily in technology but not always with a clear vision of how those investments will drive revenue, reduce costs, or deliver meaningful market differentiation.
Compounding this challenge are persistent issues with data quality, cybersecurity risks and the struggle to cultivate internal innovation. As organizations rush to adopt technologies such as AI, many find their data infrastructure ill-prepared for the workload, with nearly 80% of enterprise data still unstructured or siloed, hampering efforts to extract value. Additionally, the rapid pace of transformation is expanding the attack surface for cyberthreats, particularly as operational technology converges with IT systems.
Experts we recently interviewed cited the following five obstacles that enterprises must navigate effectively if they wish to optimize digital transformation outcomes:
Obstacle #1: A Misaligned Focus on Technology Rather than Desired Business Outcomes
One of the most prominent mistakes organizations and CXOs make is focusing too heavily on technology and not enough on the business outcomes of their investments. Many organizations spend significantly on their digital transformation efforts without clear business objectives in mind. This leads to dissatisfaction among business leaders and even job losses for CXOs. The here is straightforward, explains Tim Crawford, CIO strategic advisor at research and advisory firm AVOA: become business-focused first and focus on how technology can better help to reach business outcomes. “That means aiming for outcomes such as revenue expansion, cost reduction, market differentiation, and the like,” Crawford says.
For instance, Crawford points out that many organizations spent millions upgrading their digital infrastructure without focusing on specific outcomes from that outlay. When business executives ask what they got for their investment, the answer is often “nothing” because the focus is on the technology for technology’s sake rather than business transformation. “CIOs should be “business leaders first, who happen to have responsibility for technology,” Crawford adds.
Bobby Cameron, vice president and principal analyst at research firm Forrester, agrees and advises companies to design their technology stacks from the “outside” by starting with their desired business results and then building technology to support those outcomes. Cameron adds that high-performance organizations are those organizations that are “continuously trying to improve business results through technology.”
Obstacle #2: Systemic Data Quality and Data Preparation Issues
While data is crucial to digital transformation success, enterprise data is often not consumable, requiring significant preparation before technologies like AI can be applied effectively.
The central investments necessary for AI success include building the infrastructure with scalable cloud infrastructure, adequate storage, compute, and networking to handle AI workloads. Data governance processes must be in place, such as proper metadata management systems, data catalogs, and data lineage tracking capabilities, as well as ensuring AI efforts stay strategically aligned with business goals.
“After watching many customer outcomes, we learned that if an organization doesn’t have high-quality data available, they’re not going to fulfill the promise of AI or meet their expectations,” says Anand Logani, chief digital and AI officer at technology services provider EXL.
Patrick Dey, VP of data and innovation and office of enterprise transformation at Rockwell Automation, adds that organizations don’t necessarily need perfect data, “but they still need their data to be good enough to work with, but most companies have not had success in extracting value from their data because they have yet to figure out how to use it,” Dey says.
Obstacle #3: Rapid Digital Transformation Can Increase Cybersecurity Risk
As digital transformation accelerates, so do the associated cybersecurity risks. For instance, when genAI began to take hold, many organizations compressed their related digital transformation adoption timelines from months to weeks. Rapid adoption can create significant cybersecurity risks, especially when organizations prioritize speed over security.
Another area of rapid change is the convergence of operational technology (OT) and information technology (IT), which has expanded the attack surface in many critical infrastructure industries such as healthcare, manufacturing, and energy generation. With adversaries no longer needing to directly target OT systems to cause operational consequences, as demonstrated by the Colonial Pipeline incident.
The industrial sector faces unique cybersecurity challenges during digital transformation. As Nigel Gibbons, director and senior adviser of global cloud security services at cybersecurity services provider NCC Group, said, many OT protocols were not “secured by design,” creating inherent vulnerabilities when these systems connect to modern networks. Gibbons described a dangerous situation where traditional OT systems’ “castle-like security” is compromised when integrated with more open cloud environments.
To mitigate the increased risks stemming from rapid transformation, organizations should identify their critical assets, work with their security practitioners, develop threat models, and implement appropriate controls before rushing ahead.
Obstacle #4: Balancing Top-Down and Bottom-Up Innovation
Experts contend that organizations often struggle to balance bottom-up innovation and top-down, structured innovation. As the name indicates, top-down innovation is directed by senior leadership, and executives and top-level managers drive new ideas and change. In contrast, bottom-up innovation stems from employees at all organizational levels identifying potential areas of innovation based on their daily experiences.
Sheldon Monteiro, chief product officer at digital consultancy Publicis Sapient, explains that a mix of both top-down and bottom-up is recommended, with structured approaches to idea generation, hypothesis testing, and iterative development. “How companies manage organic, bottom-up innovation versus top-down, curated garden approach is critical. You don’t want to crush, or try to crush, innovative approaches at the bottom that teams put forward, but at the same time, especially in regulated environments, you don’t want chaos either,” Monteiro says.
So how do companies balance what you see with your clients?
Monteiro recommends organizations:
- Take a strategy-led approach sponsored by the C-suite while still allowing for ground-level innovation
- Quantify value and hold teams accountable for achieving desired outcomes
- Use a structured approach to idea generation, hypothesis testing, and iterative development
It’s been Monterio’s experience in working with clients that it’s those organizations that clearly defined their strategy and business-value targets were more successful in balancing innovation with organizational structure. They emphasized that while hackathons and ideation sessions were valuable for education, at some point, organizations need a clear strategy to focus their innovative efforts.
Obstacle #5: A Lack of Deep Industry Context
CXOs need a deep understanding of the state of their industry before they can begin fundamentally rethinking how their organization creates value, serves customers, and competes. Without a nuanced grasp of industry dynamics, regulatory pressures, customer expectations, and competitive threats, CXOs risk falling victim to obstacle #1 and investing in technology for technology’s sake. The most successful business transformations are those where leaders start with a clear-eyed view of what their industry demands and where it’s headed, then work backward to identify the digital capabilities that will drive tangible business outcomes.
Joseph Batista, director and chief creatologist at Dell EMC, explains that companies must understand their industry context and unique DNA, including the dynamics of their ecosystem and value chain, to transform effectively. Batista added that CXOs should consider themselves “students of the industry” to drive successful digital transformation. He noted that those who truly understand their industry context and how their company operates within it gain a significant advantage.
There’s a lot at stake for CXOs that fail to get their digital transformations right, as those organizations that manage to make the right investments in the right technologies will find themselves pulling ahead in efficiency, innovation, and market relevance. The difference between leaders and laggards is growing sharper: digital maturity is increasingly a predictor of business resilience and growth. Organizations that ground their strategies in deep industry knowledge, prioritize business outcomes over technology for their own sake, and invest in foundational data and security capabilities are far more likely to realize the promise of digital transformation.
Ultimately, the obstacles that so often derail transformation efforts are also the very challenges that, when addressed head-on, can set organizations apart. When $3.9 trillion is on the line, and most efforts still fall short, the way forward is clear: treat obstacles not as roadblocks but as the essential path to sustainable digital success.
