Employee Engagement: The Key to Unlocking AI’s Potential

JJ Foster, Senior Director of Business Analytics, Protective Life Insurance Company

Introduction

Purpose of Paper

The purpose of this paper is to inspire companies to lead Artificial Intelligence (AI) initiatives in ways that actively engage their workforce and position employees as valuable contributors to the transformation process. As AI tools become more prevalent, employees may fear job displacement, leading to resistance or disengagement from technologies designed to assist or partially replace aspects of their roles. However, harnessing the power of the workforce creates a space for both the technology and employees to grow and reap benefits.

The current state of AI has the ability to automate specific tasks, but it rarely replaces entire job roles. Most jobs consist of a variety of functions, some of which are well-suited for automation, while others are inherently human. Historically, technological advancements have not only shifted responsibilities but also created new ones. By thoughtfully engaging employees, organizations can help them recognize the benefits of AI and transition toward responsibilities that require human insight and judgment.

Overview

Two and a half years after ChatGPT entered mainstream conversation, AI continues to evolve at a pace that outstrips most organizations’ ability to adapt.¹ New iterations of large language models (LLMs) and tools promising exponential returns on investment appear almost daily. The workforce is acutely aware of this shift. The World Economic Forum (WEF) predicts that 44% of workers’ skills will be disrupted by 2028.² This forecast is reinforced by frequent announcements of corporate downsizing as organizations deploy AI at scale.

This moment presents both a challenge and an opportunity for the insurance industry. Success in the era of AI requires organizations have access to quality data, set clear strategic goals, and employee an engaged workforce that can provide context and feedback to AI systems. Insurance companies already possess vast data sets and a clear mission. The missing link is often workforce engagement and the ability to use our humanity to unlock AI’s potential.

“Our humanity lies in our ability to think strategically, be creative, and communicate and collaborate to solve complex problems." 
– Geoff Woods, The AI Driven Leader³

AI's rapid advancement will inevitably lead to a number of changes in organizations.  These changes will impact the workforce as job descriptions, responsibilities and headcount within job roles are redefined. However, this does not eliminate the need for humans in the loop. The ability to ask the right questions and collaborate with AI tools is essential to realizing their value. Employees bring wisdom and context that are critical to AI’s success. Effective AI outputs depend on well-formed questions and prompts, which are greatly enhanced when an engaged employee applies their knowledge and context to the tools.

To ensure successful AI adoption, companies must prioritize transparency, robust change management, comprehensive training across all organizational levels, and goal alignment.

Value of Engaged Workforce in the AI World

Value Creation

AI and LLMs offer the promise of increased productivity, enhanced quality, improved customer service, and faster response times. Interestingly, these outcomes are already being achieved by highly engaged employees, even before AI tools are introduced. Findings from Gallup's State of the Global Workforce Report conclude that highly engaged employees drive a 23% increase in profitability compared to their less engaged peers.⁴

When these engaged employees are empowered with AI tools, the potential for value creation multiplies. The willingness to adopt and experiment with AI adds measurable value, regardless of an employee's starting skill level. The most significant performance gains have been observed among users with lower initial skill levels.

A study published in Harvard Business Review by Fabrizio Dell’Acqua and colleagues examined how AI assistance impacted task performance across skill levels.⁵ Both high and low-skilled participants improved, but those with lower initial skills, often newer employees, experienced a remarkable 43% performance increase after using AI tools.

Figure 1: Bottom-Half Skills and Top-Half Skills – Inside the Frontier⁴

Feedback Loop

The expertise that individuals bring to their roles is heightened when using AI tools. Employees using AI tools provide feedback, allowing the AI to learn and adapt to the needs of the user and the task at hand. The value extends not only to the tool itself, but also to the human in the loop, as employees benefit from using these tools to enhance their capabilities and remain current with emerging technologies. This mutual dependency creates a powerful feedback loop where experienced professionals provide context and corrections, while AI tools improve productivity and learning.

In AI First, Adam Brotman and Andy Slack emphasize that the best way to understand AI is to use it. As employees begin experimenting with AI, they naturally share results with colleagues, creating an environment where the technology is championed and not feared.⁶

In the insurance sector, departments such as underwriting and claims are increasingly viewing AI assistants as integral to their roles. According to McKinsey’s The Future of AI for the Insurance Industry, employees who actively use AI tools are more likely to take ownership of them, offering feedback when output falls short and helping to refine the models over time.⁷ This engagement is essential for building AI systems that continuously learn and deliver better outcomes. The absence of employee engagement often creates a culture of resistance to AI and a tendency to focus on its shortcomings, which undermine adoption and limit its potential.

Engaged employees play an important role in this feedback loop. The Oliver Wyman 2025 Forum Global Employee Survey found that engagement is highest in organizations perceived as leaders in AI.¹ In these environments, employees volunteer for pilot programs, suggest improvements, and help troubleshoot implementations. In contrast, employees who feel excluded from AI initiatives are less likely to use the tools, and even less likely to provide the feedback necessary for improvement.

As illustrated in the graphic from Oliver Wyman's survey, employee sentiment around AI is often correlated with productivity gains.¹ This alignment reinforces the importance of involving employees early and often in AI rollouts.

Figure 2: Employee Survey Results at AI Leading Firms¹

A 2025 Harvard Business Review article, Your AI Strategy Needs More Than a Single Leader, warns against limiting exposure of AI projects in organizations.⁸ The authors argue that limiting input to a small group excludes valuable frontline knowledge. Instead, companies should embrace open channels, such as idea portals, workshops, and office hours, to surface the best suggestions from across the organization.

Talent Management

Combining AI tools with an engaged workforce is where the real payoff lies. This coordination creates a self-reinforcing cycle where engaged employees drive better AI outcomes, which in turn builds confidence and fosters further engagement. This dynamic also produces a talent magnet effect. Employees become advocates for their organizations, which attract like-minded professionals and reduce turnover costs that often plague companies struggling with AI adoption.¹

This effect can be a key differentiator for the insurance industry. While fears of job displacement are real, they exist across all sectors. Insurance companies have a unique advantage with a clear, purpose-driven mission that impacts society. McKinsey research encourages companies to lean into this purpose to attract talent from other disrupted industries.⁷ This addition of new talent complements the existing workforce and highlights meaningful opportunities in organizations that are thoughtfully employing AI tools.

McKinsey suggests that ideal organizations source 70–80% of their digital talent internally, supplementing with external hires from adjacent sectors.⁷ The recommended high percentage of internal talent creates a drive for organizations to go beyond the early adopters of new technologies and create a path for the larger organization to engage. Organizations that can leverage internal talent will benefit from less turnover and the ability to apply years of internal knowledge and experience to tune and validate new AI tools.

Recommendations

Transparency

Organizations face mounting pressure from stockholders, board members, investors, and other internal and external forces to harness the power of AI. This pressure will continue to make employees feel anxious about their roles, as these technologies are often perceived as replacements for humans. The companies that foster the most internal participation in these efforts stand to be the biggest winners. This begins with transparency within the workforce regarding the tools being explored and the expected outcomes.

Transparency lifts the veil on what an organization is trying to achieve with the technology and presents the organization as a leader in the AI space. Workers who feel their employers are AI leaders are three times more likely to believe their company has a bright future than those who do not see AI being implemented. This translates to double the satisfaction with upper management and twice as likely to feel secure in their roles. This confidence creates engaged employees who work to find and improve processes and outcomes.¹

Withholding information on AI initiatives may feel like a measure to reduce anxiety in the workforce, but studies reveal that anxiety already exists, and facing the challenge of embedding AI into workflows requires shared ownership and accountability.⁷

Change Management Plan 

A critical part of creating transparency and employee engagement is keeping change management at the heart of AI use cases. AI use cases often focus on access to data, integration of systems, security considerations, and other technical hurdles. While these are very real concerns, the human element should be considered from the onset.

According to Zurich’s Group Head of AI, Christian Westermann, “In our experience, change management represents half the effort required to secure both financial and nonfinancial impact, while efforts to bring clean data to the models, the modeling itself, and the integration of AI account for the other half.”¹³  

This sentiment is echoed by McKinsey's research, which suggests that every dollar spent on AI development requires another dollar focused on user adoption. “Change management is the key differentiator between AI sitting idle and AI transforming operations.”⁷

Figure 3: AI transformation will likely reshape workforce models in the industry⁹

Training All Employees

In Ethon Mollick’s book Co-Intelligence, he references four separate research teams looking across 1,016 job categories to see how AI impacts the roles.¹⁰ All but 36 of these job categories were seen to be affected by AI, with most exceptions being physical labor roles. This highlights the need for AI training across the white-collar workforce, particularly in knowledge-intensive sectors such as insurance industry. Providing AI training helps to demystify the technology and affords employees the opportunity to adjust and adapt to new ways of delivering value.

Mollick goes on to assert that, along with training, his first rule is always to invite AI to the table. This idea of first enabling the workforce and then experimenting with that newfound knowledge helps reinforce the learning and find additional use cases.¹⁰ 

Training is an area where companies can begin to differentiate themselves from their competitors. According to a study by the Boston Consulting Group, only 6% of companies have trained a quarter of their workforce in GenAI. In contrast, companies seen as industry leaders report three times as many full-time employees upskilled in GenAI.¹¹ This gap extends to the C-Suite, where 59% of leaders in the BCG survey rate their executive team as having little or no confidence in their ability to use GenAI.¹¹

A workforce without proper training will struggle to deploy new AI tools and risk falling behind competitors. Operating in the AI landscape requires upskilling all workers and implementing a plan to foster a culture that is continuously learning.¹²

Reward Value Delivery

The one aspect of AI that resonates with potential users is the reduction of mundane tasks.   While GenAI can make emails more straightforward to compose, take meeting notes, and summarize documents, the path forward after these tasks have been automated is to shift the focus to delivering value. The ease of life functionality that AI creates becomes valuable when that time can be redeployed to strategically meaningful tasks.

Rewarding employees who find AI use cases will require a shift in how work is valued. Emphasis on the work product delivered, compared to the effort required for completion or a mindset of billable hours, can be a mental hurdle for both employer and employee. Employees may be hesitant to bring forward use cases that reduce the time spent on tasks, especially repeatable tasks, if they believe the value lies in the effort to complete the work.   If a task traditionally takes a week to complete but is completed in hours using AI, the reward system must recognize and celebrate that efficiency, while focusing on the ultimate value delivered. One key to success, as noted by Sophia Velastegui in a recent Forbes article, is to receive public acknowledgment of AI Utilization.¹²  This outward acknowledgement creates an environment where employees feel safer sharing their successes and a virtuous cycle where more use cases can be shared.

Use Cases

Aviva (United Kingdom) – Claims Overhaul¹⁴

Aviva, one of the UK’s largest insurers, recently deployed AI to improve the claims process. The company used the project as more than just a technology release, but as a cultural transformation that championed an engaged workforce to achieve results.  

Aviva created transparency by involving over 50 cross-functional team members, including "translators" who created a connection between the new technology and business needs. Change management professionals and employees were engaged throughout, providing input and feedback for the overall process and tool set. The team received over 40,000 hours of extensive training.

The results were striking:

• 23-day reduction in liability assessment time
• 30% improvement in routing accuracy
• 65% decrease in customer complaints
• Employee engagement scores doubled
• Customer Net Promoter Score increased by 700%

 Zurich Insurance (Sweden) – AI Literacy¹³

Zurich Insurance Group has similarly stressed the human element in its AI strategy. Christian Westermann, Zurich’s Group Head of AI, observed in a 2023 interview that when introducing advanced tools like GenAI, “the limiting factor is not related to technology or trust, but to people." 

Zurich’s approach was evenly split with 50% technology and 50% people. Half of the effort in each AI project focused on change management, including open communication, transparency, addressing concerns, and ensuring that employees understood the tools.

Zurich treated AI adoption as a cultural shift. Workshops were held to train employees, not just on the “how,” but also the “why.” Staff were encouraged to experiment with AI-driven processes. This investment paid off, and by early 2024, dozens of AI use cases were thriving—from underwriting assistants to claims automation, driven by an engaged workforce.

The Zurich example shows that when companies invest in their people through transparency, training, and support, they unlock bottom-up innovation and high adoption rates.

Moderna (United States) – Mass Adoption⁶

Moderna's AI adoption story leverages the training and change management expertise of Aviva, as well as the internal focus of the Zurich use case. Moderna began by creating transparency through listening tours across the organization to understand the problems employees faced in their day-to-day jobs and discussing how AI tools could assist. They followed the tour by creating training and education programs around AI that were made available to employees, from the C-suite to frontline staff. These foundational elements created a culture of employees engaged and vested in using the available technology to solve problems.

In an interview for the book AI First, Brice Challamel, Moderna's VP of AI products and platforms, is quoted as saying, "It starts with the 'user' [our people] at the core. The Moderna employees are people who need to want to transform."

To support change management, Moderna launched a branded chatbot called mChat, paired with a prompt contest open to all employees. A Microsoft Teams channel was created for submissions. The contest encouraged real-world experimentation in a fun, inclusive way.

• The largest Teams channel at the time had 90 members
• The prompt contest channel grew to over 3,000 members
• 400+ entries were submitted
• 180 solutions were implemented to support business initiatives

Conclusion

AI Impacts Us All 

I began this research with both excitement around AI’s promise and fear of how technology will impact my world. I lead a team consisting of knowledge workers who perform analytical tasks as their primary function. AI has the ability today to significantly influence this work, and the current version of AI is only a steppingstone to what technology will become in the future.

I also considered my elementary school-aged child who, in a matter of a decade, will enter a workforce that will look radically different than the world we operate in today. The ability for technology to largely automate current tasks raises questions about what education best supports future employment and fulfillment. The fear of these unknowns and the human impact led me to this research. Ultimately, the findings provided more comfort than initially expected.

The fear of the unknown is not a new phenomenon. The pace of change and technological advancement is a continuous march forward. Those who have been in the workforce for any period have seen new roles emerge, technology replace tasks, and entire business units come and go depending on strategic needs. This change will continue. AI has a new spin on advancement, offering a robust set of tools, but the concept of change and the need to engage employees to facilitate it remain the same.

One of the encouraging findings in this research is the way organizations that are early adopters have championed their workforce as the enablers of AI tools. There will be changes in roles, automation of tasks, and reductions in human effort along the way. Still, employee engagement is at the heart of finding, developing, and iterating the ideal solutions.

My team of analysts will undoubtedly experience changes as AI tools automate specific job tasks. This will both allow time for other activities and create new functions that do not exist today. The roles will look different from what they do today, and that should be the expectation. The generation that enters the workforce a decade from now will enter with varying expectations of how they utilize AI, or even the next wave of technology, but the need to engage, think critically, and continuously learn will remain constant. This is and will be true for each and every one of us.

This research paper is only intended for the expressed uses of the RGA/International Insurance Society Leaders of Tomorrow program. The author’s opinions and recommendations do not reflect those of Protective Life Insurance Company or any other organization with which the author is affiliated.

References

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