The Friction Behind Digital Transformation
Digital transformation began with a simple promise: make work faster, simpler, and more efficient. Organisations invested in enterprise software, automation, cloud infrastructure, and now it is artificial intelligence’s turn. These technologies are expected to minimize effort and let people focus on higher-value work. But is it what is happening?
In many ways, they have delivered on that promise. Information moves instantly across the world, teams can collaborate in different time zones and tasks that needed long manual work can be done in minutes. According to McKinsey's State of AI report (published in November, 2025), 88% of organisations now use AI in at least one business function, up from 78% a year earlier. Access to advanced technology has become remarkably easy.
But as the saying goes, “with great power comes great responsibility,” and many organisations are facing the reality that expanded digital capabilities also require greater integration, governance and, of course, management. Existing systems need to work seamlessly with new platforms, applications, capabilities, while AI models need reliable data and strong governance. Every new addition adds new dependencies, workflows, and integration requirements.
Digital transformation has removed many traditional limitations, but it has also created new forms of complexity.
Complexity Moves
Every significant technological innovation or shift follows the same pattern: it solves one problem while creating another.
Cloud computing removed the need to build and maintain physical infrastructure, but introduced new issues related to security, identity governance, and other operational considerations.
APIs made it much easier for systems to exchange information, but they also created ecosystems of dependencies, where one change could have far reaching consequences.
AI is reducing the time required for analytics, data processing, or content generating, but it also depends on high-quality data, clear accountability and governance.
Technology rarely eliminates operational demands; it just changes where complexity or the bottleneck lies.
The Friction Between Systems
A few decades ago, operational friction was easy to identify. Repetitive manual work, difficult communication across locations globally, and other physical limits that slowed business processes. As we discussed earlier, digital transformation addressed many of those.
Today, friction is often less visible because it exists between systems, not within them.
It appears as data duplication across multiple platforms, poor integration between systems that adds manual work, or AI assistants that produce insights while people still need to spend time to validate results because primary data may be insufficient.
It may look insignificant, but it drains time, resources, and organisational capacity. This is digital friction: the effort required to make technologies, systems, data, and processes work together as a coherent system.
Complexity is Now Operational Challenge
The scale of these operational demands is growing rapidly.
Research from Okta's Businesses at Work report shows that organisations in its customer base deployed an average of 101 business applications in 2024. Every application addresses a specific need, but each also adds another integration, another identity layer, another source of data, and another coordination task.
The costs extend well beyond technology. An IBM estimate cited by Harvard Business Review) put the annual cost of poor data quality to the U.S. economy at more than $3 trillion a year, while McKinsey estimates that technical debt can consume 20 – 40% of the value of an organisation's technology estate before depreciation.
These costs rarely appear under a budget line called "digital friction". They are spread across departments and projects. They come to light as delayed decisions, duplicated work, additional management or integration tasks, and hours spent resolving inconsistencies between systems.
Exposed Friction
AI has amplified this shift.
In many cases, organisations approach AI as a salvation. They expect immediate productivity gains and greater efficiency, but in reality AI often exposes problems that have existed for years.
No AI solution can be reliable when an organisation’s data is fragmented, processes are inconsistent, or there is no clear governance, ownership, or trusted data. AI exposes weaknesses in data architecture, business processes, and operating models. In the best-case scenario, it makes those weaknesses impossible to sweep under the carpet.
Beyond Technology
For a long period of time, transformation was measured by the technologies organisations adopted: new platforms, automation, AI agents, and more. Of course, every new capability helps organisations stay competitive, but each one also introduces new demands for managing interconnected digital systems, where dependencies between systems matter as much as the systems themselves.
Instead of asking what technology or innovation to adopt next, we suggest looking first at where the friction already exists. Sometimes organisations do not need another model or technology. They need a thorough audit, fewer unnecessary dependencies, and confidence that their technologies, systems, and data are working together as intended.
This is the work we have spent years doing. Since 2018, SUPER HOW? Group has designed and deployed critical digital infrastructure for regulated institutions, where interdependencies between systems carry as much weight as the systems themselves.
The experience includes LBCOIN, the world's first blockchain-issued central bank digital collector coin, delivered for the Bank of Lithuania in 2020, with 24,000 tokens issued. D€X TF Project 2, the Bank of Lithuania's contribution to the ECB's two-tier digital euro. Axiology, a MiFID-licensed DLT Trading and Settlement System, now live in production. In every case, the hard part was rarely the technology itself. It was making regulated systems, trusted data, and clear accountability operate as one.
“Digital transformation has never been only about adopting better tools. It has always been about improving the way organisations operate, and that objective has not changed. What has changed is where the effort lies. The organisations that succeed will not necessarily be those with most advanced technologies, but those that reduce digital friction, manages dependencies effectively, and build systems that operate seamlessly.” - Andrius Bartminas, Co-Founder & EVP, SH Group.
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