Frederik R. Pedersen, CEO and Co-Founder of EasyTranslate, driving innovation at the intersection of AI and human expertise.
Traditional industries are often described as slow to adopt AI. For many, the typical ROI: speed, cost and efficiency, isn’t what their clients value most. Instead, trust and expertise bolster long-standing client relationships in traditional industries.
Despite that, AI adoption is rising, but with big gaps. McKinsey’s The State of AI report released earlier this year indicates nearly two-thirds of companies have yet to scale AI across their enterprise. Instead, businesses are stuck in experimentation or pilot phases.
But, adoption is accelerating among newer small businesses. A JPMorganChase Institute study reported that businesses founded in 2025 reached 10% AI adoption in six months, compared to more than six years for those founded in 2019. Reasons include lower costs, cloud delivery and the accessibility of generative AI tools, which became more mainstream in the last few years.
The gap is widening between newer, digitally native businesses moving quickly toward AI adoption versus a slow, cautious progression—often marked by stops and starts, within traditional industries, especially those built on expertise and trust.
That caution is not irrational. In high trust sectors, speed is rarely the primary driver of value. And, if speed jeopardizes trust, it can harm a business’s bottom line. Harvard Business Review famously reported decades ago acquiring a new customer can cost up to 25 times more than retaining an existing one, while a 5% increase in retention can boost profits by 25% to 95%. Forrester’s global B2B trust research reinforces this, showing buyers, globally, prioritize competence (53%) above all else, followed by dependability (33%) and consistency (28%) over speed or innovation when selecting vendors. This proves that for high trust industries, risk is the primary constraint on adoption.
The translation industry is a clear example. It sits at the intersection of regulation and nuance, particularly in legal, medical and government contexts, regulatory-heavy sectors where errors carry significant consequences.
As AI becomes more capable, the opportunity goes beyond automated workflows to include rethinking how to scale businesses in high-trust sectors. In an M&A context, that means integrating fragmented providers while introducing new technology without undermining the trust those businesses were built on.
Here are five lessons I learned from doing that.
1. Continuity Plus Relationships Equals Trust
In specialist translation, client relationships are often anchored in individuals: linguists or account leads who understand context, terminology and regulations.
During acquisitions, it’s easy to prioritize systems integration and overlook these human dependencies. But continuity is critical to retaining confidence.
Treat account leads as a trust layer, a crucial part of ensuring the stability of the client relationship. Keep them visible, client-facing and central to delivery, even as workflows evolve behind the scenes.
2. Visibility And Transparency
Translation has traditionally been sold as a finished product. AI changes that by introducing layers of automation that are often invisible to clients.
That opacity can create friction and distrust. Clients want to understand not only what they are receiving, but how it was produced and, more importantly, how they can mitigate risks.
This shifts the burden onto providers to clearly explain where AI is used, how humans are still involved and how output is scaled when humans and AI collaborate.
3. Design Around Risk
Clients worry about nuance, domain accuracy and accountability, especially in regulated environments.
The strongest operating models acknowledge this directly. Rather than pursuing full automation, they structure workflows so that AI handles volume and repetition, while human experts retain responsibility for judgment and validation. This hybrid approach increases capacity without compromising quality where it matters most.
4. Align Incentives With Strategy
• Introducing AI can trigger concerns around pricing pressure, client perception and long-term positioning.
• If those concerns aren’t addressed, integration slows, even when the strategic rationale is clear.
• Demonstrate that AI drives efficiency and scale without eroding quality or relationships.
• Positioned correctly, AI becomes a growth lever, not a threat.
5. Use Compliance As A Front End Differentiator
In high trust sectors, credibility is often established via compliance frameworks.
Investing in recognized standards such as ISO/IEC 27001 (information security), ISO 17100 (translation services) and SOC 2 (data handling and operational controls) does more than reduce risk. It signals to clients that AI-enabled workflows are being implemented within structured, auditable systems.
I have firsthand experience with this. International standards are the first part of any conversation with a potential new customer engaged in compliance-heavy sectors or managing classified data. We don’t move forward without this.
For smaller firms, achieving and maintaining these certifications can be difficult. Within a consolidated structure, they become a shared asset and provide an immediate competitive advantage.
Strong compliance credentials often accelerate client adoption faster than technical capability.
Conclusion
AI is already reshaping how expertise is delivered and scaled.
The real opportunity in AI-led M&A is not just to introduce new technology, but to build operating models that preserve trust while increasing capacity. Those lessons are increasingly relevant anywhere expertise and risk intersect.
Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?
