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1Fix the driver behind crashes, sound loss and screen glitches2Clear out junk files and repair common Windows errors3Scan for outdated or missing drivers - takes under a minuteIn a February 2025 interview, Nationwide’s then-CTO Jim Fowler argued that technology should take routine administrative work off employees’ hands so they can focus on judgment, customer problems and other work that benefits from human attention. His examples—from AI summaries for claims representatives to a companywide training push—show how that ambition might work. They do not, however, establish independently measured gains in productivity, accuracy or customer outcomes.
Who is Jim Fowler, and what was his business context?
Fowler was Nationwide’s executive vice president and chief technology officer when CIO published its interview with him on February 13, 2025. The article described him as having more than 20 years of technology leadership experience. His remit, as presented in the interview, connected technology modernization and innovation with business strategy and workforce development.
Fowler said he had been at Nationwide for six years and that the company’s revenue had risen from $42 billion to $60 billion during that period. He credited the technology organization with playing an important role; that account does not establish technology as the cause of the full increase.
One strategic link he emphasized was Nationwide’s aim to become the preferred partner for a smaller number of larger intermediaries. That depends on interactions that are easy and dependable. Digital systems and connected data are therefore not just back-office efficiency projects in this framing: they support distribution relationships and the experience of doing business with the insurer.
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Four forces Fowler sees reshaping insurance
Artificial intelligence: remove routine work, keep human judgment
Fowler’s AI thesis is oriented toward augmentation. Software can help with repetitive, clerical tasks, while employees spend more time on complex decisions and human-centered interactions. That distinction matters: automating a task is not the same as automating a whole role, and a faster workflow is not automatically a better customer outcome.
For an insurer considering automation, the practical test is whether the task is sufficiently repetitive and bounded to delegate safely. Leaders should also define what remains with the employee: accountability, decisions with significant customer consequences, and work requiring context or empathy. Measures should include quality and customer experience as well as time saved.
Connected data: more timely signals, more responsibility
Fowler pointed to telematics, smart-home technology, market data and the ability to move and analyze information close to real time. For insurers, connected data may inform risk assessment and modeling. But more data does not guarantee better decisions: its quality, relevance and representativeness matter, as do consent, privacy, security, bias and compliance obligations.
A digital workforce: people working with machines
Fowler described a future of humans plus machines, rather than machines replacing humans. The implied worker is not necessarily a software engineer. Instead, employees need enough digital fluency to learn tools, use them effectively, evaluate their outputs and recognize when a task requires human judgment.
That fluency has to sit alongside insurance expertise. A tool may summarize records or generate a suggestion, but domain knowledge helps an employee identify missing context, question a flawed output and decide what action is appropriate. The potential value of AI is therefore conditional: routine work must be removed without simply increasing workloads, and the time gained must be put to useful purposes.
Advanced computing: a future-facing bet on quantum
Fowler singled out quantum computing as a potentially important form of advanced compute, with possible applications in running more models for problems such as risk prediction, financial-growth analysis and market-performance modeling. His interview presents this as a forward-looking possibility. It is not evidence that Nationwide was operating production quantum systems or realizing measurable results from them at the time.
What kind of worker does this strategy require?
The future-worker idea becomes more practical when expressed as capabilities rather than a prediction about job titles:
- Adaptability: willingness and support to learn changing tools and workflows.
- Task judgment: ability to distinguish work software can assist with from work that needs human review or ownership.
- Data and AI literacy: ability to ask useful questions of internal systems and interpret outputs critically.
- Domain expertise: knowledge of insurance processes and customer circumstances to put generated information in context.
- Continuous learning: regular opportunities to update skills as tools and responsibilities change.
- Human-centered performance: using time freed from clerical work for listening, explanation, negotiation and customer problem-solving.
That model requires deliberate job design. If automation removes administrative steps but employees have no time, authority or training to apply their expertise, the promised shift to higher-value work may not happen.
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What Nationwide’s reported AI examples show
“Chat With Your Data” and employee-led discovery
Fowler said Nationwide had made “Chat With Your Data” available to associates. He cited two property-and-casualty underwriters among the tool’s top users, interpreting their use as an example of employees finding a way to remove a tedious part of underwriting without being given a prescribed application.
The example illustrates why frontline adoption can matter: people closest to a workflow often know where friction lies. But reported use is not proof of improved underwriting accuracy, lower costs or changed staffing. The interview does not provide the tool’s architecture, performance measures or independent evaluation. For this kind of experimentation to be useful, employees need access and clear guardrails, and leaders need to assess whether the tool improves the work rather than merely attracting attention.
Claims Log Notes and preparation for customer calls
Fowler described complex claims that can contain 50 or 60 entries from customers, builders, adjusters and others. In his example, a representative may spend 15 to 20 minutes reviewing the history before being ready to address a customer’s question. Those figures are his illustration, not an independently measured industry average.
The system he described processes claims-log entries using an overnight AI process and generative AI at the time of interaction. It produces a short summary of recent issues and actions, along with a prediction about why the customer may be calling. The intended benefit is to reduce time spent reconstructing the history and leave more room for conversation.
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Clear out junk files and repair common Windows errorsFree Scan →Scan for outdated or missing drivers - takes under a minuteDriver Scan →A prediction about a caller’s reason is a hypothesis, not a fact. Representatives need access to the underlying notes and must be able to correct or disregard a summary. That is especially important where omissions or errors could affect how a claim is handled. Sensitive claim information also requires appropriate access and security controls. The interview describes the intended workflow, not verified changes in handling time, accuracy or customer satisfaction.
Training at scale—and what the numbers do not prove
Fowler said Nationwide had approximately 25,000 associates, mandated more than eight hours of technical training per associate, and delivered more than 70,000 hours of AI-related training in the year before the interview. These are executive-reported figures in the February 2025 account; “the prior year” refers to that interview context and should not be treated as a precisely specified calendar-year total.
Training volume alone does not show whether employees can use tools safely or apply them to real work. A strong program distinguishes general awareness from hands-on practice and tailors learning to roles such as underwriting, claims, customer service and management. It should also address privacy, security, unreliable outputs and when to escalate or seek human review. Organizations can assess training through demonstrated proficiency, adoption, error patterns and workflow or customer outcomes—not attendance hours alone.
How to avoid investing in technology for its own sake
Fowler’s account of blockchain illustrates a portfolio principle: explore a technology enough to understand it, but stop active investment when it lacks a clear business problem or return. He described shelving rather than destroying the work, leaving room to revisit if market conditions or useful applications change.
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That approach separates technology potential from present utility, a proof of concept from production value, and strategic optionality from open-ended spending. Pausing a project can be disciplined management, not an admission of failure. Fowler’s assessment reflects Nationwide’s experience and investment judgment; it is not a universal claim that blockchain has no use.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What leaders should measure before calling an AI rollout successful
The interview offers executive-reported practices and examples, but no independent validation of adoption, productivity or business impact. It does not quantify changes in claim handling time, underwriting throughput, decision accuracy, cost, customer satisfaction or employee experience, nor does it detail the tools’ governance, security or technical architecture. A useful evaluation should answer questions such as:
- Business relevance: Does the tool address a defined customer, partner, risk or employee problem?
- Quality and fairness: Are decisions accurate and consistent, and are underwriting or claims outcomes monitored for unfair effects?
- Human accountability: Can employees inspect source information, challenge outputs and override recommendations?
- Data governance: Are access, retention, privacy and security controls appropriate for the information used?
- Employee impact: Does automation remove undesirable work and create time for better work, or simply raise quotas and intensify workloads?
- Customer outcomes: Are service clarity, speed and fairness improving, not just internal processing time?
- Portfolio discipline: Are weak projects stopped, and are successful pilots reliable enough to scale?
Common failure points include summaries that omit material facts, employees overtrusting confident-sounding outputs, tools introduced without workflow redesign, training treated as a compliance exercise, and productivity claims made without a baseline. A nominal human review is not a meaningful safeguard if staff lack the time or authority to question the system.
A practical framework for technology leaders
- Start with a painful workflow. Identify a recurring task that consumes time or creates friction for employees, customers or partners.
- Define the outcome before selecting a tool. Set measures for quality, time, customer impact and risk, with a baseline for comparison.
- Involve frontline users. Give employees controlled access to experiment and a channel to report errors and useful applications.
- Train by role. Pair tool instruction with domain-specific practice and guidance on privacy, security and human review.
- Preserve accountability. Make clear which decisions remain employee-owned and ensure users can inspect relevant source information.
- Monitor after launch. Track adoption, errors, overrides, customer outcomes and workload effects; revise the workflow when results show a problem.
- Stop or narrow weak projects. Continue investment only when evidence supports a meaningful use case; retain the option to revisit shelved ideas when conditions change.
The leadership point behind the technology
Fowler’s argument is ultimately about organizational choices as much as computing. Tools can make routine work easier, but leaders determine whether that creates better service, more meaningful employee work or merely a faster way to process the same pressures. The result depends on training, workflow design, governance and a willingness to stop projects that do not solve a real problem.
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Outbyte PC Repair FREEClear out junk files and repair common Windows errorsFree Scan →Outbyte Driver Updater FREEFix the driver behind crashes, sound loss and screen glitchesFind Drivers →The interview also includes a personal leadership note: Fowler reflected on moving his family repeatedly during his career and urged leaders to remember that career decisions affect the whole family. It is a reminder that workforce transformation and executive ambition both have human consequences beyond the technology itself.
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