From Fear to Frameworks: Why AI Adoption Fails Without Change Leadership
- Dev Bharti
- Aug 28
- 4 min read
Updated: Sep 22
Artificial intelligence is here, it's growing, and it's disrupting every layer of the value chain. From underwriting and claims automation to fraud detection and customer service, the potential is clear. But why do so many AI adoption initiatives still fall flat?
According to the latest Roots “State of AI Adoption in Insurance 2025” report, 70% of insurance firms are exploring or testing artificial intelligence (AI) tools, yet only 22% have scaled or adopted AI to production. The barriers? Not just technical. In fact, they're mostly human: lack of ai talent (52%), data quality issues (40%), and tellingly, fear the technology won't deliver on promise of capabilities or value (38%) and internal resistance to change (35%).
This article explores why fear, resistance, and a lack of clarity sabotage even the most promising AI deployment projects, and how insurance leaders can replace hesitation with a leadership-driven framework for successful transformation.
Fear of Artificial Intelligence and Generative AI
It's tempting to interpret employee hesitation as cynicism or conservatism. But look closer, and you'll often find it rooted in fear; fear driven by the exponential pace of technology and the overwhelming, sales-heavy messaging from vendors.
Job insecurity: EY research from 2023 found that 75% of employees believe generative AI and machine learning will eliminate jobs.
Lack of understanding: When organisations fail to communicate what artificial intelligence ai will actually do, employees are left to fill the gaps with worst-case scenarios.
Trust deficit: Without clear governance or ethical boundaries, ai systems can feel like a black box, creating more anxiety than enthusiasm.
The consequences are real. Many insurers are stuck in “pilot purgatory” unable to move past initial AI trials due to internal pushback and leadership inertia.
Cultural Resistance Impacts AI Adoption Rates
If fear is the emotional response, culture is the systemic issue.
A 2025 article from Allganize highlights that without cultural alignment and internal support, adoption rates stall regardless of technical readiness.
Common sources of cultural resistance include:
Unclear roles and responsibilities
Disjointed or siloed business function leadership
Ethical ambiguity around ai use
Resistance to changing existing processes
Most employees don't oppose change. They oppose being changed without agency. Artificial intelligence implementation, therefore, must feel less like an imposition and more like a partnership.
Why Lack of Clarity Derails AI Adoption
26% of companies move AI beyond the proof-of-concept stage
It's a common trap: assume that investing in the best tools will guarantee results. But BCG's 2024 research found that only 26% of companies move AI beyond the proof-of-concept stage. The biggest difference between winners and laggards? Not technology. It's clarity from leadership on people, process, and purpose.
“Leaders follow the rule of putting 10% of their resources into algorithms, 20% into technology and data, and 70% in people and processes.”
BCG, Oct 2024
In insurance, an industry built on trust, process alignment and human adoption matter even more. AI can't just be a data science initiative; it has to be a change program that supports human AI collaboration across multiple business functions.
The Cost of Standing Still
The risk isn't just failed pilots, it's lost ground. Insurers that delay ai adoption face slower claims handling and eroded customer trust compared to peers who act decisively.
But scaling ai use isn't an all-or-nothing leap. It unfolds as a journey: quick wins like claim triage and fraud detection build confidence, which then scale into more strategic plays in pricing, customer experience, and supply chain management. Momentum comes from clarity, leadership and iteration and not from one-off technology deployments, which is where frameworks add value, by turning ambition into action through tried-and-tested approaches that leaders and teams can trust.
Framework for AI-Driven Change: What Insurance Leaders Must Do
To overcome fear and unlock significant value, insurance execs need to lead from the front. A siloed “ai team” won't cut it. As Harvard Business Review (Aug 2025) argues, successful AI adoption requires cross-departmental leadership that integrates AI into strategy, operations, compliance, and HR.
1. Set a Clear and Shared Vision for AI Deployment
Articulate why ai tools are being adopted, what problems they will solve, and how success will be measured.
Tie AI goals to strategic business outcomes, not just tech experimentation.
2. Engage and Empower Employees to Develop AI Skills
Involve teams early in the process to create buy-in and ownership.
Run internal campaigns that show how new technologies like voice recognition or virtual agents augment rather than replace roles.
Provide relevant training and support. AI skills aren't just for the tech team.
3. Role-Model AI Use From the Top
Executives must consistently back the adopted AI agenda—not just at launch, but throughout its evolution.
Communicate progress, admit setbacks, and celebrate wins to build momentum.
4. Prioritise Trust and Business Function Governance
Establish transparent policies on public data use, fairness, and ethical decision-making.
Make accountability clear. Who oversees ai outcomes and compliance?
5. Invest in Change Management and AI Talent
Use a structured approach, as recommended by Prosci and IntellectAI, to manage transition phases.
Communicate frequently in both directions, top-down bottom-up.
Turning Hesitation into Action
The insurance industry isn't short of keen interest in AI, it's short of frameworks for change.
The latest data is overwhelming: whether it's the Roots survey or Boston Consulting Group insights, the message is clear:
AI transformation is a human challenge.
And humans need leadership.
By reframing ai adoption as a change leadership journey (not a tech implementation project) insurance leaders can escape pilot purgatory and build scalable, trusted ai systems that deliver real value and positive impact.
Want to go deeper?
Camelot are hosting a special event exploring real-world examples of ai deployment done right in the insurance industry, and what happens when it goes wrong. You'll hear from peers, practitioners, and change leaders tackling the key questions:
How do we overcome resistance to artificial intelligence?
What does a people-first ai roadmap look like?
How can leadership drive adoption without fear?