AI Meets Governance – Navigating Risk in Emerging Tech Adoption

Introduction

Artificial intelligence (AI) and other emerging technologies are transforming industries at an unprecedented pace. While these innovations offer immense potential for operational efficiency and business model evolution, they also introduce new risks—ranging from data privacy and algorithmic bias to transparency and control. At Altum Strategy Group, we believe responsible governance is the key to unlocking the full value of emerging technologies while safeguarding trust and integrity.

Our Guiding Principles

Our approach to AI is grounded in Altum’s AI Guiding Principles [1], which emphasize:

• People – AI must be designed for good, respecting human judgment, privacy, and rights.
• Process – Governance must ensure transparency, accountability, and fairness.
• Technology – AI must be responsible, agile, and aligned with long-term societal benefit.

These principles are not theoretical—they are embedded in every AI engagement we lead. They guide how we design, deploy, and manage AI systems to ensure they are ethical, effective, and sustainable.

“The fundamental piece is usability. Clean, well-organized data is essential—not just for performance, but for trust. If your AI can’t explain itself or be validated, it won’t be adopted.”
— Andy Pojuner, Managing Director, Technology, Data and Intelligence, Altum Strategy Group

Governance as an Accelerator

We position governance not as a constraint, but as an enabler. With the proper guardrails, organizations can deploy AI faster and safely. Our governance frameworks are designed to be pragmatic, scalable, and aligned with business goals. They ensure that AI systems are transparent, auditable, and continuously improved through real-world feedback.

In one client engagement, we helped a financial services firm implement an AI-powered knowledge base. The project began as a way to improve customer service, but it quickly revealed deeper data quality issues. By using AI as a motivator, the client cleaned and structured their data, enabling more accurate responses and faster resolution times. This “false flag” approach—using AI to drive broader transformation—proved highly effective.

Our Responsible AI Framework

Our work in AI governance follows a structured, human-centered approach:

1. Evaluation Criteria – We develop assessment frameworks to evaluate AI implementations based on accuracy, fairness, and business alignment.
2. Data Lineage Tracking – We ensure training data is accurate, properly sourced, and traceable.
3. Phased Deployment – We design graduated rollouts, starting with experienced staff who can evaluate AI outputs critically.
4. Professional Skepticism – We train senior agents to question and validate AI recommendations before full-scale deployment.
5. Feedback Systems – We implement structured processes to capture user feedback and refine models.
6. Model Refinement – We continuously use real-world insights to improve AI systems.
7. Knowledge Mining – We develop AI capabilities to extract insights from existing knowledge bases.
8. Transparency Mechanisms – We design systems that provide clear sources and reasoning behind AI outputs.
9. Metrics Development – We create balanced scorecards that measure accuracy, efficiency, and trust.

Technology in Practice

Our AI governance solutions are built to scale across industries. Whether it’s a chatbot, a recommendation engine, or a predictive analytics tool, we ensure that AI systems are explainable, secure, and aligned with ethical standards. We also help clients prepare for evolving regulations, such as the EU AI Act (2024), by embedding compliance into their development processes.

In one case, we worked with a client to improve their AI-driven customer support platform. By implementing structured feedback loops and refining the model based on real-world interactions, we increased the system’s accuracy from 82.3% to 96.7%. We also reduced the average time to locate relevant information from 4.2 minutes to just 42 seconds, dramatically improving agent productivity and customer satisfaction.

 Industry Perspective

The AI governance landscape is evolving rapidly. According to recent research, 73% of enterprises have implemented or are developing formal AI governance frameworks. Organizations with effective governance deploy production AI applications 38% faster than those without. Moreover, companies that take a phased, human-in-the-loop approach achieve 45% higher user adoption rates.

Regulatory momentum is also building. The EU AI Act (2024) is the first comprehensive framework for AI regulation, and similar legislation is emerging globally. Proactive governance is no longer optional—it is a strategic necessity.

Case Study Data

Our responsible AI implementations have delivered measurable impact:

• Knowledge Access Improvement: Time to locate relevant information reduced from 4.2 minutes to 42 seconds
• Accuracy Metrics: AI-provided information accuracy improved from 82.3% to 96.7%
• Agent Productivity: Call resolution time improved by 23%
• Training Efficiency: Onboarding time for new agents reduced from 6 weeks to 3.5 weeks
• Knowledge Expansion: Knowledge base articles increased from 3,200 to 3,800 through AI-assisted content generation
• Employee Satisfaction: Agent satisfaction with AI tools increased from 2.7 to 3.5 out of 5
• Customer Impact: First-call resolution rate increased from 57% to 68%
• Business ROI: Customer satisfaction scores improved by 14 points

Client Insights

We often find that clients are eager to adopt AI but unsure how to manage the associated risks. Our role is to provide clarity, structure, and confidence. We help clients move from experimentation to enterprise-scale deployment—without compromising on ethics, transparency, or control.

In one engagement, we used AI to surface patterns in customer behavior that had previously gone unnoticed. This insight enabled the client to address service issues, proactively improving retention and satisfaction. We ensured that the AI system aligned with business goals and user expectations by embedding governance into the process.

Conclusion

At Altum Strategy Group, we believe that responsible technology adoption is the foundation of sustainable innovation. Our AI governance frameworks help organizations navigate complexity, mitigate risk, and build stakeholder trust.

We do not just implement AI—we operationalize it. We ensure that every model, recommendation, and insight is grounded in transparency, accountability, and business value.

In a world where emerging technologies are reshaping industries, governance is not a barrier but a bridge to competitive advantage.

References