AI Adoption Strategies for Superannuation Funds

Throwing AI tools at your team without a plan is like giving them a Ferrari without driving lessons. AI only drives impact if your workforce knows how to use it effectively. After: 1-defining objectives 2-assessing readiness 3-piloting use cases with a tiger team Step 4 is about empowering the broader team to leverage AI confidently. Boston Consulting Group (BCG) research and Gilbert’s Behavior Engineering Model show that high-impact AI adoption is 80% about people, 20% about tech. Here’s how to make that happen: 1️⃣ Environmental Supports: Build the Framework for Success -Clear Guidance: Define AI’s role in specific tasks. If a tool like Momentum.io automates data entry, outline how it frees up time for strategic activities. -Accessible Tools: Ensure AI tools are easy to use and well-integrated. For tools like ChatGPT create a prompt library so employees don’t have to start from scratch. -Recognition: Acknowledge team members who make measurable improvements with AI, like reducing response times or boosting engagement. Recognition fuels adoption. 2️⃣ Empower with Tiger Team Champions -Use Tiger/Pilot Team Champions: Leverage your pilot team members as champions who share workflows and real-world results. Their successes give others confidence and practical insights. -Role-Specific Training: Focus on high-impact skills for each role. Sales might use prompts for lead scoring, while support teams focus on customer inquiries. Keep it relevant and simple. -Match Tools to Skill Levels: For non-technical roles, choose tools with low-code interfaces or embedded automation. Keep adoption smooth by aligning with current abilities. 3️⃣ Continuous Feedback and Real-Time Learning -Pilot Insights: Apply findings from the pilot phase to refine processes and address any gaps. Updates based on tiger team feedback benefit the entire workforce. -Knowledge Hub: Create an evolving resource library with top prompts, troubleshooting guides, and FAQs. Let it grow as employees share tips and adjustments. -Peer Learning: Champions from the tiger team can host peer-led sessions to show AI’s real impact, making it more approachable. 4️⃣ Just in Time Enablement -On-Demand Help Channels: Offer immediate support options, like a Slack channel or help desk, to address issues as they arise. -Use AI to enable AI: Create customGPT that are task or job specific to lighten workload or learning brain load. Leverage NotebookLLM. -Troubleshooting Guide: Provide a quick-reference guide for common AI issues, empowering employees to solve small challenges independently. AI’s true power lies in your team’s ability to use it well. Step 4 is about support, practical training, and peer learning led by tiger team champions. By building confidence and competence, you’re creating an AI-enabled workforce ready to drive real impact. Step 5 coming next ;) Ps my next podcast guest, we talk about what happens when AI does a lot of what humans used to do… Stay tuned.

Insights from #FutureProofFestival, an illuminating talk by Morningstar’s Chief Data Officer, Lee Davidson, focusing on overcoming AI challenges in the financial industry. As we stand at an inflection point in AI adoption, here are the key insights and strategies shared: 1. Scaling AI Applications:    - Paradigm Shift: focus on tasks amplified, not jobs impacted    - Workflow Redesign: data entry to insight and oversight    - Track marginal cost of production improvements as a KPI 2. De-risking AI Implementation:    - Trust Building: Prioritize building customer trust over pursuing flashy technologies. This approach ensures long-term adoption and value.    - Four Pillars of AI Trust: Explain reasoning, provide evidence, maintain consistency, behave appropriately    - Quality Control: Implement ground truth analysis and accuracy testing. Morningstar uses a 1000-question test set across seven categories to validate AI performance.    - Ethical Boundaries: Set clear guardrails for AI applications, such as avoiding unsolicited financial advice or taking partisan stances on sensitive issues. 3. Ensuring Quality Data for AI:    - Mindset Shift: Treat data as the “oxygen” of the organization, not just exhaust. This perspective elevates the importance of data quality and governance.    - Organizational Challenges: Address common issues like data silos, outdated governance structures, and inadequate quality measurement processes.    - Scalable Architecture: Consider adopting a data mesh operating model for improved scalability and innovation. This decentralized approach empowers individual data domain teams.    - Future-Proofing: Prepare for exponential growth in data volume and complexity. Morningstar’s data assets are doubling every 2-3 years across price, portfolio, and unstructured textual data. 4. Morningstar’s AI Implementation Examples:    - Document Processing: Using AI to infer data from millions of PDFs, with human analysts providing oversight and validation.    - Reasoning Transparency: Implementing chain-of-thought reasoning in AI responses to build trust and explain decision processes.    - Rigorous Accuracy Testing 5. KPIs for AI Success:    - Scaling Efficiency    - Risk Mitigation    - Data Quality The financial industry is at a crucial juncture with AI adoption. By addressing these challenges head-on and shifting our mindsets, we can unlock the full potential of AI while maintaining trust, accuracy, and ethical standards. The finance industry must balance innovation with responsible AI implementation. Let's shape the future of finance responsibly. #AIinFinance #DataStrategy #FinTech #ResponsibleAI

AI adoption fails more often because of people, not because of technology. Every leadership team today wants to “bring in AI.” Budgets are allocated, tools are procured, pilots are run. But six months later… the dashboards look empty. Employees are back to their old ways of working. Why does this happen? Because AI rollouts are treated as a tech project. The reality: it’s a change management journey. Here’s what I’ve seen actually move the needle when it comes to adoption: 1. Anchor it to real pain. Nobody cares about “AI for efficiency.” They care about the 40 minutes wasted reconciling data every morning. Or the endless hours spent formatting reports. Show exactly what pain AI removes—and you’ll get attention. 2. Make it invisible. If AI feels like “one more tool,” it dies. Adoption skyrockets when AI lives inside the systems people already use—email, ERP, CRM, Slack, WhatsApp. The less new behavior you demand, the faster people adapt. 3. Co-create, don’t dictate. Top-down mandates rarely stick. Let employees pilot the system, test its limits, and even break it. When they shape the workflow, they take ownership. That’s when adoption becomes self-sustaining. 4. Upskill with intent. Training cannot just be “how to write prompts.” It has to include judgment. When should you trust AI? When must you double-check? When do you override it? This builds confidence and reduces fear. 5. Stack small wins. Don’t wait for the “grand launch.” Show a two-minute task reduced to 20 seconds. Share how customer complaints dropped because of faster responses. Small wins, repeated and communicated, build belief. Because here’s the truth: The hardest part of AI adoption isn’t accuracy, security, or integration. It’s belief. If your people don’t believe AI genuinely makes their work easier, faster, better—no rollout will ever stick. AI transformation is not about replacing humans. It’s about changing how humans work with machines. And that requires more than a tool. It requires leadership, communication, and patience.

Recently, a CIO from insurance company reached out to me, trying to solve the problem of raining questions about AI like “AI is here to take our jobs”, “We won’t use it”, “You’re just training it so you can replace us” Sound familiar? It’s funny because 71% of BFSI CIOs are ramping up generative AI use to improve employee productivity but over 56% of them fail because of low adoption. Employee concerns about job security, skill gaps, and ethical implications can significantly impede AI adoption and effectiveness. Here’s a Strategic Approach to harness AI's full potential & put focus on your teams: ⭐ Transparent Communication: Address AI's role openly, emphasizing augmentation over replacement. ⭐Comprehensive Education: Implement training programs covering AI basics, specific applications, and ethical considerations. ⭐Skill Development: Identify and bridge gaps in AI tool proficiency. Alternatively, find tools that have low or zero learning curve and no-code to encourage employees to try it out. ⭐Ethical Framework: Develop and promote AI ethics guidelines to ensure responsible implementation. Make it available to all teams to review and comment on. ⭐Trust Building: Create feedback mechanisms for employees to contribute to AI development and deployment. ⭐Leadership by Example: Actively engage with AI initiatives, aligning them with organizational goals. With this people-centric approach, I was able to work with CIOs drive almost 100% AI adoption for our use case with Alltius in BFSI companies. This not only addresses immediate concerns but also positions our organizations for long-term success in the AI-driven future of finance. What strategies are you employing to prepare your team for AI integration?