Domain Knowledge Is Becoming a Commodity Faster Than You Think

Engineering consultancies have always sold expertise. Deep knowledge of classification rules, regulatory frameworks, design methodologies—that's been the core value proposition.

But a significant portion of what was sold as "expertise" was really pattern recognition over large bodies of documented knowledge. And that's exactly what AI does well.

The Expertise Illusion

Consider what senior engineers and consultants actually do on a daily basis:

• Interpret regulations and apply them to specific situations
• Reference past projects to inform current recommendations
• Recognize patterns in technical data and draw conclusions
• Apply standard methodologies to new problems
• Verify compliance against established criteria

Much of this work involves accessing, processing, and applying documented knowledge—rules, standards, precedents, methodologies. It's sophisticated pattern recognition, not novel invention.

For decades, this pattern recognition was valuable because it required years of training, access to institutional knowledge, and accumulated experience. Clients couldn't do it themselves, so they paid experts.

How AI Commoditizes Pattern Recognition

Large language models and specialized AI systems excel at exactly this type of work:

Regulatory interpretation. AI can read classification society rules, flag state requirements, and international conventions—then apply them to specific technical scenarios with high accuracy.

Precedent retrieval. AI systems can search thousands of past projects, identify relevant precedents, and suggest how they apply to current situations.

Pattern detection. AI analyzes technical data—structural calculations, stability assessments, process simulations—and identifies patterns that humans might miss.

Standard methodology application. AI applies established engineering methodologies consistently, without fatigue or oversight gaps.

Compliance verification. AI checks technical deliverables against applicable standards, identifying non-conformances and verification gaps.

The knowledge work that justified premium consulting rates is rapidly becoming a commodity available at a fraction of the cost.

What This Means for Your Firm

If your value proposition depends primarily on documented knowledge and pattern recognition, you're facing commoditization:

Margin collapse. When AI can provide equivalent analysis for cents instead of thousands of euros, hourly rates for knowledge work become unsustainable.

Client disintermediation. Clients will increasingly use AI directly for routine analysis, coming to consultants only for genuinely novel problems.

Competitive pressure. New entrants with AI capabilities can deliver equivalent outputs at lower cost, undercutting established firms.

Talent devaluation. Engineers whose primary value was knowledge access and pattern application find their skills worth less in the market.

The Defensible Remainder

Not all expertise is being commoditized. The defensible remainder includes:

Novel judgment under genuine uncertainty. When no established rules apply, when precedents don't exist, when the situation is truly unprecedented—human judgment remains essential.

Trust relationships and political navigation. Complex client organizations require relationship management, stakeholder alignment, and political acumen that AI cannot replicate.

Ethical accountability. Someone humans can hold responsible when things go wrong. Liability, professional ethics, and accountability remain human domains.

Implementation and change management. Advice is easy. Driving organizational change, managing implementation, and ensuring outcomes—that's where value persists.

Proprietary data advantages. Firms with genuinely unique data pipelines—data no one else has access to—can deliver insights that AI alone cannot match.

The Pivot Required

To survive commoditization, firms must pivot their value proposition:

From knowledge to judgment. Position your firm as providers of judgment in uncertain situations, not knowledge in documented ones.

From analysis to accountability. Sell the ability to stand behind recommendations, manage liability, and deliver outcomes—not just reports.

From projects to partnerships. Move from transactional project delivery to ongoing advisory relationships where trust and continuity matter.

From generic to proprietary. Develop unique data assets, specialized methodologies, or domain-specific AI systems that competitors cannot easily replicate.

The Warning Signs

How do you know if your firm is vulnerable to commoditization?

• Most of your work involves applying established standards and methodologies
• Junior staff can handle significant portions of your deliverables after modest training
• Clients frequently ask "can't we just use software for that?"
• Your differentiation is primarily based on experience and credentials rather than unique capabilities
• Competing primarily on price and availability rather than distinctive value

If these describe your firm, the commoditization wave is coming for you.

The Timeline

The commoditization of domain knowledge is happening in phases:

Phase 1 (now): Routine analysis, compliance checking, documentation—already being automated.

Phase 2 (1-2 years): Standard engineering calculations, regulatory interpretation, precedent analysis—AI capabilities rapidly improving.

Phase 3 (3-5 years): Complex synthesis, multi-factor analysis, novel situation pattern-matching—AI approaching human-level capability in constrained domains.

The window for pivoting your value proposition is narrowing. Firms that recognize this early and adapt will capture the remaining high-value work. Firms that don't will compete on price for increasingly commoditized services.

Is your expertise truly defensible, or is it just pattern recognition waiting to be automated?