Enterprise digitalization is often viewed by leaders and employees as a purely technical task—one that can be completed by buying software, implementing systems, or hiring technical teams. However, the technical aspects are far more complex than expected, with challenges extending beyond technology itself. Below are the core technical hurdles facing enterprises in their digitalization journeys.
Technical Selection: A High-Stakes Choice
For most traditional enterprises starting digitalization, choosing the right technical path is a critical, high-risk decision. Technical architecture, programming languages, deployment methods, and ecosystem compatibility directly impact a system’s scalability and long-term maintenance costs. Many enterprises blindly adopt trendy technology stacks without matching them to their business scenarios or team capabilities, leading to immediate system failures post-launch. Others rush into AI investments without basic digital foundations, resulting in underutilized tools due to insufficient data and mismatched use cases.
The dilemma between third-party software and in-house development adds complexity. Relying on third-party solutions risks dependency on external providers for core technologies and interfaces, creating supply chain vulnerabilities and cost overruns. In-house development, meanwhile, brings high labor costs, technical misalignment risks, and team stability challenges. Balancing “technological advancement” and “maturity” demands strategic foresight and rational assessment based on the enterprise’s development stage.
System Integration: Breaking Data Silos
System integration remains the top pain point in digitalization. Most enterprises grapple with years-old heterogeneous systems like ERP, MES, and CRM, forming isolated “data islands.” When connectivity is needed, inconsistent data standards and interface protocols emerge as major barriers. Obtaining interface documentation from vendors often involves fees or refusal; some providers have even ceased operations, making integration nearly impossible.
Compatibility between legacy and modern architectures poses another obstacle. Older systems built on traditional frameworks require high stability, while new systems adopt microservices and cloud-native technologies. Differences in communication protocols, data formats, and security mechanisms make forced integration prone to crashes. Enterprises often invest heavily in middleware or adaptation layers, and some restructure core business processes, consuming time, resources, and increasing risks.
Data Governance: A Complex Undertaking
The ultimate goal of digitalization is data-driven decision-making to boost efficiency and competitiveness. Yet most traditional enterprises face fragmented data sources, disorganized formats, and poor quality, limiting data value. Unifying data standards alone requires cross-departmental coordination, often involving intense negotiations. Building a “data middle platform” does not solve the problem overnight.
Data governance essentially involves reorganizing business logic, restructuring processes, and redistributing interests—an ongoing battle. Compounding the issue is the difficulty of quantifying results; short-term returns are rarely visible, leading to a severe imbalance between investment and outcomes for many enterprises.
Security & Compliance: Persistent Risks
Security and compliance are constant concerns for IT industry. Data breaches or system outages can disrupt operations and trigger legal liabilities. Enterprises face external threats like hacker attacks and viruses, as well as internal risks from human error or malicious behavior.
Worse, enterprises often take a short-sighted approach to security investment. Before incidents, budgets are cut, staffing is inadequate, and technical defenses are outdated. Post-incident, they engage in “firefighting” investments, falling into a cycle of “panic after incidents, forgetfulness afterward.”
Endless Technological Investment
Digitalization is perceived by leaders as a continuous “money-burning” process. Expenses cover hardware, software licensing, platform construction, and training—each an open-ended investment. Technological iteration outpaces expectations, with newly launched systems at risk of obsolescence quickly.
Enterprises face a dilemma: underinvestment leads to falling behind, while sustained spending strains finances. The rapid emergence of cloud computing and AI exacerbates redundant construction and waste. Moreover, technological investments often outpace business and organizational transformations, leaving technical dividends unrealized. Many expensive data platforms end up unused, reducing digitalization to formalism.
Talent Shortages and Misalignment
Talent gaps and misalignment hinder digital transformation. Professionals with both business and technical expertise are scarce, leading to siloed teams—business departments complain technical teams lack needs understanding, while technical teams criticize business counterparts for poor systematic thinking.
Competition for top talent disadvantages small and medium-sized enterprises, creating talent gaps. Outsourcing may alleviate pressures but fosters dependency, weakening control over core systems. Even enterprises with in-house teams face misalignment between organizational and talent development, with technical teams marginalized and lacking strategic influence. Core talent loss further diminishes transformation momentum.
The essence of digitalization’s complexity lies in its deep integration with business strategy, organizational processes, data assets, talent capabilities, security, and cost management. Enterprises face an interconnected, dynamically evolving system rather than isolated technical issues. Success requires leaders to recognize that technology’s value lies in its ability to drive sustainable business competitiveness, demanding strong technical absorption, integration, and operational capabilities.
