Integrating AI tools effectively and upskilling teams are no longer options but necessities for businesses aiming to thrive. IDEX Consulting hosted an event, 'AI: The drive for growth and productivity in the professional services market,' which explored the key components for successful AI adoption and integration, how to mitigate unethical implications and importantly, how to engage teams throughout the change journey.
Expert Insights from the front lines of AI adoption
Hosted by Paul Davey, Managing Director of General Insurance, and Emma Delli-Bovi, Business Director, Legal at IDEX Consulting, the event brought together a distinguished panel of speakers: Richard Nicholas, Partner, AI and Digital Law at Browne Jacobson; John McClelland, Distribution Officer at C-Quence; Martin Robert Hall, Leadership Coach and Greg Williams, Automation and AI Analyst at C-Quence. Our experts delved into the intricacies of AI adoption, offering practical advice and real-life examples.
Watch our recap video where speakers provide simple yet strategic advice on how to get started, how to manage successful adoption and the key risks to watch out for.
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For a recap on what was covered during the event, check out the below summary.
How to get started with AI
For many, the first question is, "How do we even begin with AI?" Our speakers emphasised a foundational approach:
Conduct a business review: Start by identifying gaps and opportunities within your existing processes. Where can systematic activities be optimised? Where could automation significantly enhance customer service, team productivity, and overall efficiency? The goal is to free up your team to focus on strategic, revenue-generating projects that fully utilise their skills.
Establish a governance structure: Appointing a central person responsible and accountable for AI implementation is crucial. This provides a clear point of contact for troubleshooting, solution-finding, and managing the overall adoption strategy. Risk management and clear joint-responsibility for AI-driven decisions are invaluable for successful integration.
Embrace continuous testing and learning: AI tools are constantly evolving. Teams must commit to continuous learning through A/B testing, trialling different platforms, and seeking guidance from data and technology experts. This iterative approach is key to improving and enhancing products and services.
Where AI can have the biggest impact
Automating manual and repeatable tasks: The insurance sector, in particular, is seeing success in using AI for claims processing, underwriting, and broker submissions. Greg Williams of C-Quence advised starting with small, manageable AI projects and gradually expanding.
Data aggregation and analysis: AI excels at quickly and accurately analysing large datasets. It boasts powerful advantages in pattern recognition, identifying subtle correlations across disparate datasets that human analysis might miss. Machine learning algorithms can also extract meaningful insights from unstructured text and detect non-linear relationships between variables.
How to bring teams along the journey
One of the biggest hurdles to AI integration is often employee resistance. Our speakers offered valuable strategies to overcome this:
Adopt a bottom-up approach: Often, the best ideas for AI implementation come from those on the ‘shop floor’ who are directly involved in the work. Empowering employees to identify opportunities where AI can positively impact their job and the customer experience leads to greater buy-in.
Resist a dictatorial approach: Top-down enforcement of AI adoption often leads to resentment. Leaders should instead gather feedback, involve teams actively, and encourage them to lead the change.
Cultivate a culture of ideation and change: Encourage employees to ask questions and share ideas on how products, services, and workflows can be improved. Support a culture where individuals feel empowered to challenge norms and think differently.
Empower employees through collaborative forums: Set up specific sessions and diverse focus groups to foster an environment where sharing new ideas is encouraged and prioritised. Ensure these groups are diverse in terms of seniority, job discipline, gender identification, age, and culture to promote balanced perspectives.
Showcase success stories: ‘Seeing is believing’ - present clear case studies and live demonstrations of how AI has benefited teams, the business, and customers.
Implement iterative rollouts: Avoid trying to automate everything at once, which can overwhelm teams and introduce errors. AI implementation should be an iterative process with continuous feedback.
Grant control and encourage experimentation: People naturally fear the unknown. Encourage gradual experimentation with AI for small wins and tasks, allowing employees to feel in control and excited by changes and improvements.
Prioritise diversity: A lack of diverse perspectives in the change process can derail implementation. Ensure a mix of skills, job roles, experience, gender, and culture to drive effective change across the business.
The role leaders play
Effective leadership is paramount for successful AI adoption.
Lead by example: Leaders' mindsets about change significantly influence how others perceive it. Authenticity, transparency, and a willingness to be vulnerable and make mistakes are crucial. It’s important for leaders to show vulnerability to encourage others to feel comfortable in expressing their concerns and challenges.
Reduce fear: A common fear is that AI will replace jobs. Demonstrating how AI augments operational activities, makes tasks easier, and provides real-life success stories will help to reduce some concerns.
Incentivise adoption: Use gamification and incentives to encourage AI adoption. Reward desired behaviours and ensure employees feel comfortable reporting back on their experiences without the fear of feeling like they are ‘cheating’.
Empower advocates: Build a group of peer advocates who can shape a positive narrative around AI adoption, influencing the pace of change and fostering comfort among colleagues.
Navigating responsible AI deployment
The event also touched upon the broader societal implications of AI, particularly in regulated industries like law:
Legal accountability remains: As Richard Nicholas highlighted, existing laws still apply to AI usage in legal practices. Lawyers remain responsible for the accuracy of information, regardless of AI assistance, and are accountable for any incorrect data.
Robust governance is essential: Implement strong governance structures and build fact-checking into standard practice. Since AI doesn't inherently possess a concept of truth, human oversight is always necessary to mitigate risks.
What AI won't replace
Crucially, the panel emphasised what AI, at its current stage, cannot replace emotional intelligence and human interaction, although AI may be able to simulate empathy it cannot genuinely feel emotions. Human connections are built on shared emotional experiences and authentic responses. The therapeutic value of human empathy involves true mutual understanding rooted in lived human experience, which AI lacks. Human moral intuition, shaped by embodied emotional responses, differentiates human ethical decisions from those that an AI tool can make.
If you’d like more information on the event, topic or are interested in attending similar future events, get in contact with Paul Davey, General Insurance Managing Director and / or Emma Delli-Bovi, Legal Business Director who will be happy to support.
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