LCO 6100 Module 4 Final Paper: A Full Change Plan for a Contemporary Insurer Example

Leading Change at Raritan Valley Mutual: A Complete Plan for Digital Claims in a Hybrid, AI-Assisted Workplace

Student Name

Master of Science in Management, Thomas Edison State University

LCO-6100: Leading Change in Complex Organizations

Mentor Name

Month Day, Year

Leading Change at Raritan Valley Mutual: A Complete Plan for Digital Claims in a Hybrid, AI-Assisted Workplace

Introduction

Raritan Valley Mutual, a composite property and casualty insurer in Trenton, New Jersey, failed in its first attempt to move claims adjusters onto a digital platform. Module 1 traced that failure to five of Kotter's errors; Modules 2 and 3 planned the first and later steps of a second attempt. This final paper brings those plans together and extends them to two conditions the company faces today. Most claims staff now work a hybrid schedule, in the office two days a week. And the new platform includes an artificial intelligence tool that triages incoming claims and estimates repair costs from photos, raising questions about jobs and judgment that the first rollout never faced. Kotter (2012) presents his eight stages as a sequence leaders must work through in order; this plan follows that sequence while adapting each stage to these conditions.

The Case for Change

Garden State settles simple auto claims in nine days, against two days at its largest competitors. Policyholders kept waiting past seven days leave at renewal at triple the usual rate, and independent agents, who sell most of the company's policies, have begun steering new customers elsewhere. Claims costs are rising as adjusters handle simple files by hand. The platform, with its AI triage, can settle simple claims in a day or two and release adjusters to work down the backlog of difficult property files. The change is not optional: without it, the company will lose customers and agents to faster carriers.

Stage 1: Establishing a Sense of Urgency

Urgency rests on evidence adjusters will believe. Adjusters will hear recorded calls from frustrated customers and meet agents who have lost business. The chief claims officer will share the retention data in every team. Armenakis et al. (1993) argue that a readiness message must convey both the need for change and the organization's capacity to achieve it, so urgency will be paired with evidence that the platform works at peer insurers and that leaders have learned from the first attempt. In a hybrid workplace, these sessions will be held in person on office days, because hearing customers together creates a shared experience that a video link weakens.

Stage 2: Creating the Guiding Coalition

Module 2's coalition carries over with one addition: the data scientist who owns the AI triage model joins the claims, IT, customer service and HR leaders and the four frontline adjusters, so that questions about the algorithm reach someone who can change it. The four adjusters give the coalition what Battilana and Casciaro (2012) found decisive for changes that break with routine: a place at the center of the informal network. The coalition will meet weekly, alternating between in-person and virtual meetings, and the chief executive will attend monthly.

Stage 3: Developing a Vision and Strategy

The vision from Module 2 stands unchanged: simple claims paid within two days, and adjusters freed for the hard claims that need their judgment. Four strategic choices follow: start with auto glass and minor collision claims; change adjusters' measures from closed files to cycle time, satisfaction and accuracy; train peer coaches; and govern the AI tool so that adjusters, not the algorithm, make every decision to deny or reduce a claim. The last part responds to adjusters' fear that the AI will judge their work or replace them, and to regulators' expectations that insurers explain decisions made with automated tools.

Stage 4: Communicating the Vision

Supervisors will carry the vision into weekly team meetings, using their teams' own cases. Trained adjusters will coach colleagues at their desks on live files. In a hybrid setting, communication must be deliberate: short weekly video updates from the chief claims officer, a shared channel where adjusters post questions and receive answers within a day, and monthly in-person sessions where teams discuss what is working. Communication will also run upward, with a monthly pulse survey and a coalition report on what was heard and changed.

Stage 5: Empowering Broad-Based Action

The performance system will change first. A three-month grace period protects every adjuster's review from any dip caused by the platform. Supervisors will be measured on adoption. Payment approvals for simple claims under $2,500 will no longer need a supervisor's signature, and the double keying into the legacy system stops once payments flow from the platform. For the AI tool, adjusters will be able to override any recommendation with a short reason, and overrides will be reviewed to improve the model, not to discipline adjusters.

In a study by Edmondson (1999), teams that felt safe speaking up asked for help and admitted errors more often, the learning behavior a new claims tool demands. A change that asks experienced adjusters to learn a new tool, and to question an algorithm, depends on that kind of safety. Supervisors will be trained to respond to mistakes on the platform as learning opportunities, and the coalition will share its own errors openly.

Stage 6: Generating Short-Term Wins

The first target is one-day settlement of auto glass claims within 90 days; the second, two-day settlement of minor collision claims within six months. Both are visible to customers and agents and clearly linked to the change. The coalition will publicize them with customer comments and recognize the adjusters involved, in team meetings and in a company-wide video. Each win will be reported against the first rollout's results, so adjusters can see that the second attempt is different, and shortfalls will be reported as openly as successes.

Stage 7: Consolidating Gains and Producing More Change

Simple property claims join the platform in months seven to twelve, and the coalition then turns to subrogation and fraud referral. Kotter (2012) warns that resistance retreats after early wins but returns if pressure eases; the coalition will continue through the second year, and the old system will be retired only when adoption exceeds 90 percent. Adoption will be reviewed by team, and teams that lag will receive more coaching rather than pressure, since the first rollout showed that pressure alone produced workarounds.

Stage 8: Anchoring New Approaches in the Culture

For new hires, the platform will simply be how claims are handled. Senior adjuster promotions will weigh how much a candidate helped colleagues and improved the triage model. Stories of customers paid quickly, and of adjusters who resolved difficult property claims with time the platform freed, will become part of how the company describes itself.

Building Commitment, Not Compliance

Herscovitch and Meyer (2002) separated commitment to change into three kinds: believing in it (affective), feeling obliged (normative) and fearing the cost of refusing (continuance). Only the first two predicted going beyond the minimum, cooperating and championing the change; the third predicted bare compliance. The first rollout relied on continuance commitment, with adjusters told the system was mandatory. The second attempt aims for affective commitment by showing adjusters how the change serves customers and frees their time for work they value, and by involving them in improving the tool.

Resistance and Its Sources

Resistance will arise from three sources. Some adjusters fear that AI triage will reduce their jobs; the company will commit publicly that no claims positions will be eliminated as a result of the change and that anyone whose simple-claim work shrinks will be retrained for property files. Some doubt leaders will follow through, given the first attempt; visible early wins and monthly reports address that doubt. And some will find the new tools difficult; peer coaching addresses that. Ford et al. (2008) treat much resistance as a reaction to change agents' own conduct, such as broken promises or silence, and as a source of useful warnings. The coalition will track complaints and treat recurring ones as signs of flaws in the platform or the plan.

Measuring Progress

The coalition will review these measures monthly. If adoption or confidence lags, the response will be to examine obstacles before pressing harder, in keeping with the lessons of the first attempt.

Limitations of the Plan

Kotter's model assumes a planned change led from the top, while some research emphasizes emergent change that grows from local experiments. Appelbaum et al. (2012) found support for the individual steps but less for their fixed sequence, and Garden State may need to revisit its vision as the AI tool matures. The plan's measures also depend on customer behavior the company cannot fully control, such as how many claims arise in a severe storm season. Finally, the plan depends on the continued sponsorship of the chief claims officer and chief executive; a change in leadership during the two years would require the coalition to rebuild that support quickly.

Conclusion

Raritan Valley Mutual's second attempt carries its claims platform through all eight of Kotter's stages, adapted for hybrid teams and an AI tool. It builds urgency from customer evidence paired with capacity, a coalition with authority and trust, a vision that serves customers and adjusters, communication suited to hybrid work, the removal of structural obstacles, psychological safety, early wins and anchoring through hiring and promotion. It aims for adjusters who believe in the platform, not merely obey, and six months of measures will show whether that is happening.

References

Appelbaum, S. H., Habashy, S., Malo, J.-L., & Shafiq, H. (2012). Back to the future: Revisiting Kotter's 1996 change model. Journal of Management Development, 31(8), 764-782. https://doi.org/10.1108/02621711211253231

Armenakis, A. A., Harris, S. G., & Mossholder, K. W. (1993). Creating readiness for organizational change. Human Relations, 46(6), 681-703. https://doi.org/10.1177/001872679304600601

Battilana, J., & Casciaro, T. (2012). Change agents, networks, and institutions: A contingency theory of organizational change. Academy of Management Journal, 55(2), 381-398. https://doi.org/10.5465/amj.2009.0891

Edmondson, A. (1999). Psychological safety and learning behavior in work teams. Administrative Science Quarterly, 44(2), 350-383. https://doi.org/10.2307/2666999

Ford, J. D., Ford, L. W., & D'Amelio, A. (2008). Resistance to change: The rest of the story. Academy of Management Review, 33(2), 362-377. https://doi.org/10.5465/amr.2008.31193235

Herscovitch, L., & Meyer, J. P. (2002). Commitment to organizational change: Extension of a three-component model. Journal of Applied Psychology, 87(3), 474-487. https://doi.org/10.1037/0021-9010.87.3.474

Kotter, J. P. (2012). Leading change. Harvard Business Review Press.