Digital transformation often begins with technology. A company wants more visibility, better decisions, faster workflows, stronger customer experience, or greater resilience. So it launches a platform, adds automation, builds dashboards, or starts experimenting with AI. But many organisations discover the same problem: technology changes the surface of work faster than it changes the logic of work itself. McKinsey’s current framing is that digital transformation is really the “rewiring” of an organisation to create value through technology at scale, and its 2026 operations work argues that the next advantage will come from redesigning how work gets done end to end.

That is exactly why TRIZ belongs in the digital transformation conversation.

TRIZ, the theory of inventive problem solving developed by Genrich Altshuller, is not a digital tool and it is not a software method. It is a disciplined way of solving problems by looking for contradictions, patterns of invention, and higher-value solutions instead of settling for local compromise. In a world where companies are being pushed to rewire processes, redesign workflows, and balance technology with human-centred, resilient operating models, TRIZ offers a useful way to think.

Why digital transformation needs more than technology logic

A lot of digital transformation still follows a familiar pattern. Organisations identify friction, then look for software to remove it. Approvals are slow, so a workflow tool is added. Data is fragmented, so dashboards are introduced. Customer service is inconsistent, so AI is layered in. These moves can help, but they often do not solve the deeper contradiction in the system.

That is where transformation efforts stall. The company wants more speed, but also more control. It wants more automation, but also more flexibility. It wants more standardisation, but also a better user experience. It wants more data, but less complexity. These are not just implementation issues. They are contradictions. McKinsey’s 2025 AI survey likewise points to six management dimensions correlated with AI value at scale, including operating model, data, adoption, and technology, which reinforces the point that the problem is rarely the tool alone.

TRIZ is useful here because it starts where many digital programmes struggle: not with “what tool should we buy?” but with “what contradiction are we trying to resolve?”

What TRIZ adds to digital transformation

Traditional digital transformation often works through benchmarking, roadmaps, business cases, and technology selection. Those are important. But they do not always help leaders escape linear thinking. In many cases, the organisation simply digitises the current process, automates the current waste, or adds intelligence to a system that was never well designed in the first place.

TRIZ adds a different discipline. It asks teams to:

• define the real problem precisely
• identify the contradiction at the centre of it
• avoid weak compromise solutions
• look for inventive principles that have solved similar contradiction patterns elsewhere
• move toward the “ideal final result,” where the system delivers more value with less cost, complexity, or harm

That mindset fits digital transformation unusually well. Today’s most important digital questions are rarely about software features alone. They are about how to redesign work, decisions, interfaces, and operating models without simply trading one weakness for another. McKinsey’s 2026 State of Organizations argues that to scale AI, companies need to rewire structures and workflows end to end and shift from traditional functional models toward outcome-oriented operating models.

The contradiction at the heart of digital transformation

Most transformation challenges can be expressed as contradictions.

A company wants more automation, but does not want to lose human judgement.
It wants faster decisions, but does not want weaker governance.
It wants more connected systems, but not more cyber and operational risk.
It wants more standardisation, but also wants teams to remain adaptive.
It wants more data visibility, but less reporting burden.

These are classic TRIZ-type problems because they are not solved well by choosing one side and sacrificing the other. They require inventive redesign.

This is also where current industrial thinking has moved. The European Commission’s Industry 5.0 framework explicitly argues that modern industry should be human-centric, resilient, and sustainable, which means companies are no longer being asked to optimise only for productivity. They are being asked to resolve broader tensions between efficiency, resilience, sustainability, and human wellbeing.

TRIZ is useful because it was built for exactly this kind of tension.

From digitising processes to solving contradictions

A lot of digital transformation still underperforms because it focuses on digitising steps instead of solving contradictions.

Consider a few examples.

A company adds workflow software to an approval process because decisions are too slow. But the real contradiction is not paper versus digital. It is that the business wants stronger control without so many layers of approval. If that contradiction is not solved, the workflow simply becomes a digital queue.

A manufacturer installs dashboards to improve visibility. But the real contradiction is that leaders want more information without overwhelming teams with noise. If that contradiction is not solved, the company gets prettier reporting and no better decisions.

A firm introduces AI support for frontline work. But the real contradiction is that it wants faster knowledge work without weakening trust, accountability, or user confidence. If that contradiction is not solved, adoption stays patchy and value remains limited.

McKinsey’s recent “race to rewire” operations theme reflects the same underlying point: competitive advantage now comes from redesigning workflows end to end, not just layering technology onto existing routines.

TRIZ encourages teams to stop asking “How do we digitise this?” and instead ask “What contradiction is stopping this system from performing better?”

The ideal final result in a digital context

One of the most useful TRIZ concepts for digital transformation is the ideal final result.

In simple terms, it asks teams to imagine the outcome they want without first locking themselves into the obvious solution. That is powerful in digital work because companies often jump too quickly to systems, platforms, and tools.

For example, the ideal result may not be “implement a new workflow platform.” It may be “the right decision happens at the right time with almost no administrative friction.”
It may not be “add another dashboard.” It may be “teams see emerging problems early without spending hours building reports.”
It may not be “deploy AI everywhere.” It may be “knowledge flows to where it is needed with strong human trust and clear accountability.”

This kind of thinking helps transformation teams escape technology-first assumptions. It also aligns with broader 2026 thinking that value comes from redesigned workflows, better operating models, and stronger human–technology integration rather than isolated digital projects.

Why TRIZ fits the AI era particularly well

The rise of AI makes TRIZ more relevant, not less.

AI creates the temptation to automate around complexity instead of solving it. It can generate content, coordinate tasks, summarize information, and support decisions. But if the underlying process is weak, AI often accelerates poor logic rather than removing it. Current enterprise thinking is increasingly focused on human-agent workflows, operating-model redesign, and the need to rethink work itself as AI scales. McKinsey’s recent work on agentic AI, workflow redesign, and rewiring points in that direction, and its March 2026 piece on building businesses with AI describes AI as a “creative amplifier” that supports divergent thinking and rapid testing.

TRIZ can improve this conversation because it gives teams a way to use AI as part of inventive problem solving rather than as an overlay on current dysfunction. AI may help generate options, model scenarios, or explore alternatives, but TRIZ gives structure to the question of which alternatives are actually resolving the contradiction rather than disguising it.

So instead of using AI merely to accelerate the present, organisations can use TRIZ plus AI to design a better future state.

Digital transformation, TRIZ, and Industry 5.0

There is another reason this combination matters now. The conversation about transformation is broadening.

Industry 5.0 has made it harder to define success purely as digitisation or automation. The European Commission’s framework stresses that industrial transformation should combine technological advancement with human-centric values, resilience, and sustainability.

That is effectively an invitation to work with contradictions at a higher level:

• How do we increase technological capability without reducing worker agency?
• How do we gain resilience without creating excessive redundancy and cost?
• How do we digitise more while preserving usability and trust?
• How do we scale innovation while keeping control?

TRIZ gives organisations a practical way to work through these tensions rather than pretending they can be solved by simple trade-offs.

What this looks like in practice

Used well, TRIZ can strengthen digital transformation in several ways.

It can improve problem definition by helping teams articulate the contradiction instead of describing the symptom.

It can improve innovation quality by pushing teams beyond the first obvious digital fix.

It can improve cross-functional collaboration because contradictions usually sit between functions, not within one silo.

It can improve technology selection by making sure the tool is chosen in service of the system redesign, not as a substitute for it.

It can improve leadership thinking by reframing transformation from “technology rollout” to “inventive redesign of how value is created.”

In that sense, TRIZ does not replace operational excellence, process improvement, or digital strategy. It sharpens them.

Conclusion

Digital transformation and TRIZ belong together because both are, at their best, about redesigning systems for better performance. Digital transformation gives organisations new technological possibilities. TRIZ gives them a disciplined way to think more inventively about the contradictions those possibilities create.

That matters now because the real challenge in 2026 is no longer just to digitise. It is to rewire. It is to redesign workflows, resolve tensions between speed and control, combine AI with human judgement, and create operating models that are more resilient, human-centred, and effective. Current work from McKinsey and the European Commission makes that direction clear.

TRIZ is useful because it helps organisations solve the right problem before they automate the wrong one.