By Dean Benard
When it comes to adopting new tools and processes, whether in workplaces or professional regulation, one lesson always holds true: slow is smooth, and smooth is fast.
Artificial intelligence (AI) is no exception. It’s everywhere right now, promising efficiency, insight, and transformation. But after more than two decades helping organizations improve investigations, mediations, and governance systems, one thing is clear: jumping in too quickly without a plan leads to mistakes that erode trust and credibility. The same risks apply with AI, especially in professional regulation, where decisions affect public confidence and legal outcomes.
The message is simple: start slow but start now. Don’t wait for the perfect product or the fully formed strategy. Begin with small, targeted steps that help you learn, test, and adjust. Involve your key people early including leaders, staff, and even skeptics, so they understand the purpose and can help shape the approach.
Why the Rush Feels Urgent (and Why Slowing Down Works Better)
Many organizations are feeling the pressure to “get on board” with AI. The appeal is real: tools that can sort complaints, identify risk patterns, or analyze data faster than ever. McKinsey reports suggest successful AI adopters could see productivity gains of up to 40%. In a regulatory environment, that could mean faster triage of complaints, earlier detection of emerging issues, and faster more efficient investigations.
But the reality is more complex. Only a fraction of AI implementations deliver on their promises, and rushed adoption can create bigger problems than it solves. We’ve seen how flawed investigations, driven by assumptions or incomplete evidence, can undermine entire processes. AI used without careful oversight can amplify those risks: biased outputs, missed details, or unreliable conclusions.
Starting slow doesn’t mean stalling. It means learning deliberately. Try a pilot project. For example, use AI to assist with something low risk but common. Compare the product and outcome with manual / traditional approaches for a few months. Did it save time? Highlight nuances you missed? That kind of measured experimentation builds confidence and evidence for what works.
In investigations, haste leads to noise, not insight. AI is no different.
Bringing Stakeholders to the Table
Change that lasts is built collaboratively. No one owns the outcome alone, and AI adoption should be no exception. Investigators, IT, HR, and legal teams will all live with the impact, so they need to be part of designing the approach from day one.
Form a small AI steering group of five to seven members who meet regularly. Include operational voices, ethics advisors, and a few skeptics who can stress-test ideas. Together, identify high-value use cases and agree on principles for responsible implementation.
Equally important is communication. AI can trigger understandable anxiety about job changes or data use. Transparency helps. Hold informal sessions to explain what’s being tested, why it matters, and how people will be supported. Offer short learning modules on AI basics and hands-on training for the tools you pilot. When people feel informed and included, they become partners in innovation, not obstacles.
Ethics First: Building Trustworthy AI
In professional investigations, one slip in fairness can unravel months of work. The same applies to AI. These tools reflect the data and design choices behind them, and that can introduce bias or opacity. In regulation, where scrutiny is high, transparency and accountability must be built in from the start.
Establish a clear AI policy early. Define acceptable uses, perhaps allowing AI to sort data or identify patterns but reserving all credibility or decision making for human judgment. Require clear disclosure when AI tools are used in reports or analysis. For example: “This section was generated with AI-assisted pattern recognition. See appendix for data sources.” That level of openness protects both fairness and credibility if questioned later.
Audit data for balance and bias. Use available tools that flag potential disparities; and consult diverse stakeholders to identify problems or missed opportunities. For sensitive regulatory data, apply privacy-by-design approaches, such as keeping data local, ensuring appropriate encryption of data and clear policies and procedures for data security.
Most importantly, prepare teams to explain AI’s role clearly and confidently. Investigators may one day face cross-examination on how a tool influenced their process. Training and documentation turn those moments into demonstrations of competence rather than vulnerability.
Scaling Up Thoughtfully
Once your pilot proves its worth, expand carefully. Use a framework to assess readiness, looking at governance, data quality, and policy integration. Roll out in stages, learning from each phase.
Designate “AI champions” within teams, who are experienced investigators or analysts who can share real examples of success, such as time saved or trends uncovered. Celebrate small wins and monitor outcomes closely.
Budget and skills are common barriers, but partnerships can help. Collaborate with consultants or academic institutions for specialized support and training. View AI as a strategic investment in long-term effectiveness, not a quick cost-saving fix.
Challenges will arise including bias, data silos, missed disclosures, but with clear policy, stakeholder engagement, and a culture of learning, your organization will adapt.
The Bottom Line
AI isn’t a shortcut to better investigations or regulation, it’s a tool that rewards careful, ethical, and transparent use. Start small, learn fast, and stay intentional.
At Benard + Associates, we’ve helped organizations navigate change that protects fairness and builds trust. The next evolution is here: AI can enhance insight, reduce workload, and strengthen accountability, if implemented with care. We started 18 months ago and are only now beginning to roll out our AI applications to our work.
So, take the first step. Form your steering group. Draft your AI policy. Pick one small, low-risk use case and begin. Progress starts with action, and in this case, slow and steady truly wins the race.
