There is something that happens inside large insurance organizations that is easy to observe and hard to argue with.

Technical expertise accumulates over years, sometimes decades. The people who have it know things that cannot be quickly learned — the product complexity, the regulatory relationships, the underwriting logic, the claims nuances that separate a defensible decision from an expensive one. That knowledge is real. It was hard-won. And organizations that prize it aren't wrong to do so.

But at some point, pride in expertise stops being a competitive advantage and becomes a closed door. "We know how it's done" is a statement that can mean two very different things. It can mean: we have deep capability that outsiders underestimate. Or it can mean: the way we've always done it is the way it will be done.

Those two meanings lived comfortably together for a long time. In a stable, regulated environment where the job was consistent execution at scale, they were essentially the same thing. They are not the same thing any more.

The role has changed. The hiring criteria haven't.

My direct experience is in life insurance, which gave me a specific window into one part of a much larger and more varied industry. But the structural pattern I'm describing shows up across the sector in the data.

Insurance has one of the highest employee tenure rates in the U.S. economy. According to the Bureau of Labor Statistics, median tenure in insurance was 4.9 years as of January 2024, compared with 3.5 years across the private sector overall. That gap reflects something real: Insurance is technically complex enough that the learning curve is steep, the career pathways are well-defined, and once someone has built genuine expertise, there are good reasons to stay.

The result is an industry with deep organizational memory, strong internal culture, and — this is the part worth sitting with — a hiring logic built around selecting for people who already fit that culture. Sector experience as the primary filter isn't laziness. In a domain this technical, it looks like prudence.

The problem is that the middle manager role in a transforming insurance organization now requires something sector experience doesn't reliably build. It requires what I'd call cross-boundary judgment: the ability to synthesize signals across domains that didn't used to talk to each other, to make decisions without a clear precedent in the playbook, to manage a workforce whose skills and expectations are shifting while simultaneously absorbing a strategic pivot and maintaining execution velocity. All at once. Often with the same or reduced resources.

That is not a job description. That is a description of what transformation actually asks of the people accountable for making it happen. And it is a set of demands that years of deep sector experience — on its own — does not prepare you for. In some cases, it prepares you against it. The longer you've succeeded by applying known patterns, the harder it becomes to recognize when the pattern no longer fits.

What the wrong filter produces

Steve Jobs made a version of this argument decades ago, about the need for people who could move fluently between technical depth and human experience. George Anders developed it further in his work on what he called "jagged resumes" — candidates whose career paths crossed domains in ways that looked unconventional on paper and proved, in practice, to be exactly the flexibility that complex environments require.

Insurance has its own version of this problem, and it is structural. The sector-experience filter isn't applied by accident. It's applied because the technical complexity is real, because the regulatory environment — state-by-state in the U.S., country-by-country for global insurers — demands people who understand the stakes, and because the consequences of a bad judgment call in a regulated environment are not abstract. These are legitimate reasons to prize expertise.

But the filter is being used to solve a different problem than the one that now exists. The technical complexity of insurance hasn't disappeared. What's changed is that operating in that complexity now requires people who can also navigate conditions that have no established pattern — AI-driven workflows that are being invented in real time, workforce dynamics that have no precedent, competitive pressure from insurtechs that are unburdened by the infrastructure that makes large insurers what they are.

The sector is not short of people who know how insurance works. It is short of people who know how insurance works and can operate effectively when the rules of how it works are being rewritten around them.

Vertafore's 2023 survey found that roughly one-third of insurance professionals entered the industry from another sector. That means cross-sector pathways are already significant — the question is whether those entrants are being placed in roles where their cross-boundary capability actually gets used, or filtered past hiring managers who default to the most familiar profile.

What this costs

The talent shortage pressure is real and accelerating. Industry projections suggest approximately 400,000 workers will leave the insurance industry through attrition and retirement in the near term. That is not a diversity initiative argument. It is a pipeline arithmetic argument. The experienced cohort is aging out faster than it is being replenished, and the incoming generation has different expectations.

A 2025 survey by Young Risk Professionals found that 69% of insurance workers ages 21 to 35 believe AI will improve their workflow — but only 8.5% report being strongly encouraged to use it at work. That is not a technology gap. That is a judgment gap. The people who could help the sector absorb what is coming are already inside the building. The question is whether the organization is structured to hear them.

The hiring filter problem compounds this. If the primary selection criterion remains sector experience, the incoming talent pool shrinks precisely when the need for new capability is at its highest. And if the organizational culture treats unfamiliarity with established patterns as a disqualification, it will systematically exclude the cross-boundary judgment that transformation now requires.

The question worth asking

Insurance organizations know they need to transform. The evidence is visible everywhere: AI pilots underway, digital initiatives announced, transformation programs staffed and funded. The commitment is real.

What is less clear is whether the talent strategy is keeping pace with the transformation ambition. The sector's technical and regulatory complexity hasn't diminished — it has grown. The expertise required to navigate a multi-state regulatory environment, to underwrite complex risk, to manage claims with precision — none of that is going away. Those capabilities still matter enormously.

The question is whether organizations are also building muscle in what transformation now additionally requires: the ability to synthesize across boundaries, to act under genuine uncertainty, to lead people through conditions that have no established pattern. These are not soft skills. They are the core operating requirements of change leadership in this era — capabilities like cross-functional coalition building, decision quality under ambiguity, and the organizational readiness to absorb what AI and digitization are actually asking of the people responsible for making them work.

Are insurance organizations finding the right balance between deep sector expertise and these newer demands? Are they developing change leaders built for this era — or are they still relying on the 20th-century model of change management, which assumed that expertise plus a clear playbook was enough?

Those are the talent questions that will determine whether transformation investments produce results — or produce another round of pilots that never quite scale.

This is the first in a two-part series. Part Two examines why organizations that hire differently still struggle to deploy better judgment when they find it.

One in five. That's roughly how many drivers in states like Florida get behind the wheel without insurance, according to the Insurance Research Council's most recent data. The standard explanation is economic. Coverage costs are often too much, so some people go without. The policy response follows: steeper penalties, higher surcharges for lapsed drivers trying to come back. The diagnosis is not wrong, exactly. But it is incomplete in one critical respect: it treats the uninsured rate as something that happens to the insurance industry, rather than something the insurance industry has, in meaningful part, produced. I'd argue that a clear look at how non-standard auto products are designed in Florida suggests the latter and that the implication, for those of us who build these products, is more uncomfortable than the industry has typically been willing to acknowledge.

The Fee Cascade

Picture a driver who has been paying premiums faithfully for months. Then one paycheck comes up short. One missed installment. What happens next isn't bad luck; it's a sequence that was designed. Many non-standard carriers respond to a missed payment by assessing a Late Payment Fee. That fee gets added to the arrears, inflating what's already owed. If the swollen balance tips the driver over the edge, the policy cancels. Then comes the Reinstatement Fee. Now the driver is staring down up to four compounding obligations at once: the original missed amount, the late fee, the reinstatement fee, and potentially a catch-up payment to get back in good standing.

For a household running on variable income, that cascade is often the breaking point. Not a choice. Not a misunderstanding of consequences. The product made recovery too expensive at exactly the moment financial strain was most acute. This isn't an edge case. It's the mechanism by which the non-standard market, in aggregate, produces and sustains a meaningful share of the uninsured population.

The Price of Re-Entry

The compounding doesn't end at cancellation. When a lapsed driver's financial position stabilizes and they try to get back on the road legally, the industry often greets them with a surcharge. The lapse, the very outcome the fee structure helped produce, is now a rating factor. Re-entry premiums are higher than they were before cancellation. Down payments may be steeper. Carriers often treat the interrupted tenure as a non-payment risk signal, so the customer who couldn't clear a compounded reinstatement balance may now face a bigger first-payment obligation than they would have had they never lapsed at all.

The cycle sustains itself. Fee structures, reinstatement terms, and rating factors are deliberate product choices, not features that emerged without anyone's involvement. The uninsured rate is, among other things, a record of their cumulative effects.

A Different Product Design

When we built Clearcover's non-standard product in Florida, we started from a different premise: The fee cascade isn't an inevitable cost of serving a financially volatile segment. It's a design choice, and design choices can be remade.

We replaced the typical compounding structure with a single, knowable charge that doesn't grow during periods of financial strain. Paired with payment flexibility built around the income variability that defines much of the non-standard segment, the goal is straightforward: design products for the reality of how customers in this market actually manage money, and price the risk accordingly.

We're not arguing this is the only way to design a non-standard product. We're just saying it's a way worth trying, and that the early signal is promising enough to invite the broader segment to keep experimenting too.

The Honest Reckoning

Product design isn't the only reason drivers go uninsured. But honest reckoning requires acknowledging that the industry's fee structures and rating rules have not been neutral. They have worked, systematically, to make re-entry harder for the drivers most likely to lapse, compounding financial strain in a population that had already demonstrated it was operating at the margin. That's not an accident. It's a policy choice, and it has consequences that show up in uninsured rate data every year.

The philosophical shift the moment calls for isn't complicated, even if the execution is. As an industry, we all need to stop designing products that treat a missed payment as an opportunity. We need to build them for the reality of how customers in this segment manage money. The uninsured driver problem isn't a compliance problem to be resolved through enforcement. It is the predictable output of product decisions often made by this industry and we have the capacity to rebuild those decisions intentionally.

Consider agriculture. It is one of the oldest industries in human history, and among the last you might expect artificial intelligence (AI) to meaningfully reshape. Yet precision agriculture is doing exactly that. Satellite imagery, soil sensors, weather models and other tools are being integrated and synthesized by a new generation of AI models to guide planting decisions, predict yield variability, and optimize irrigation at the individual acre level. Crop insurance underwriting, for an example closer to home, once driven almost entirely by historical loss tables and weather averages, is being rewritten around real-time field data that only machine learning models can interpret at scale. An industry defined by tradition and seasonality is being transformed by technology faster than some financial services firms have updated their customer portals.

The insurance industry is at a similar turning point. For years, insurers have orbited platform modernization, making small improvements and then pulling back due to operational risks. Legacy systems have kept organizations in a holding pattern: stable enough to operate, but less agile in adapting to the pace the market now demands.

That dynamic is shifting. AI is fundamentally transforming the way insurance platforms are built and run, turning modernization from a long-term goal into an immediate strategic priority. This is no longer only about small efficiency gains. Platform modernization now takes center stage in competitiveness, partnerships and making better decisions at scale.

Why legacy platforms keep insurers grounded

Many insurers operate within monolithic core systems that integrate policy administration, billing, claims, underwriting and reporting within a tightly coupled environment. Often customized over decades, these systems are deeply embedded in daily operations. As a result, modernization can feel less like a technology upgrade and more like open-heart surgery.

The limitation is not age but adaptability, and at a more fundamental level, the design philosophy of what a core transaction system should be. Legacy platforms were not architected to be open. They are walled gardens with narrow access, mostly through user interfaces, built to control entire workflows and departments within a single environment. This philosophy benefits software vendors but limits an insurer’s ability to customize, adapt and integrate AI capabilities. The issues go deeper than closed systems: Many use data models that evolved haphazardly over time, which hinders external integration, limits automation, and makes large-scale changes slower and more costly than organizations would like.

This creates a frustrating paradox. To leverage AI-assisted development or intelligent automation, insurers must first invest in foundational data cleanup and restructuring. These efforts are costly, time-consuming and out of sync with the pace of innovation today. For technology leaders, the question is no longer whether to modernize, but how to sequence it without destabilizing the business.

The data mindset that determines success

Modern, open systems help deliver faster underwriting, improved claims outcomes, sharper risk selection and scalable automation. However, these outcomes depend heavily on the quality of the underlying data, which, for many insurers, is the main limitation.

Specialty insurers working with diverse distribution networks across many lines of business encounter partners spanning a wide spectrum of technical maturity. From small, focused underwriters with spreadsheet-based toolsets to large organizations with dedicated engineering teams, each engagement brings its own data structures, conventions and integration requirements. The challenge is not only ingesting that data, but normalizing and validating it to support actuarial analysis, financial reporting and program oversight across a complex book of business.

When data foundations are weak, the consequences appear across everyday operations:

• Program onboarding processes stall because agents and brokers cannot quickly answer questions that existing data should already resolve.
• Claims adjudication is fragmented, with processes and details scattered across systems and inaccessible to all stakeholders in real time.
• Bordereau files remain the standard, with limited adoption of modern data integration methods such as APIs, leaving validation manual and error prone.
• Reporting remains rigid, depending on static PDFs and IT assistance for even minor updates.

These are not merely edge cases; they are the natural result of platforms built before today’s data and integration requirements fully took shape.

Forward-thinking insurers are already addressing these issues by validating data earlier in the submission flow, streamlining ingestion pipelines, and offering program-level analytics that improve transparency for distribution partners. The ability to exchange accurate, timely data is becoming a meaningful competitive differentiator.

Knowing where, and where not, to apply AI

One of the most consequential decisions technology leaders face during modernization is not which AI tools to adopt, but where to deploy them. AI delivers outsized returns in specific contexts and introduces risk when applied in the wrong ones.

The highest-value, lowest-risk applications tend to cluster around workflows and customer interactions: automating bordereau validation, surfacing claims anomalies, generating underwriting summaries, accelerating document review, or guiding agents through submission requirements. These are areas where AI augments human judgment, reduces friction, and operates alongside existing systems without requiring those systems to change.

Replacing core transaction systems is a different conversation. Policy administration, billing, and claims settlement involve regulatory compliance, audit trails and financial integrity requirements that demand extreme care. Applying AI directly to these systems, without strong data governance and testing frameworks, introduces risk that often outweighs the short-term gain. The better path is typically to modernize the underlying architecture first, then build AI capabilities on a stable foundation.

Organizations that conflate “apply AI everywhere” with a modernization strategy often find themselves with sophisticated models sitting on unreliable data, or automated workflows breaking at the points where legacy systems assert themselves. Discipline about where AI creates value, and where foundational work must come first, is what separates effective transformation from expensive experimentation.

How AI changes the modernization equation

AI is not only speeding up platform modernization in insurance; it is transforming how it occurs. In the past, transformation has often been seen as a large-scale, multi-year project to replace core systems. For platforms handling high transaction volumes, the cost, complexity and operational risk of this “big bang” method often outweighed the advantages.

AI shifts that calculation in two distinct but complementary ways: how new applications and tools are built and deployed and how AI is embedded directly into workflows to support and automate decisions. These are not the same thing, and conflating them leads to poorly sequenced investments.

AI development tools: Building and deploying faster

The first wave of AI impact for most technology organizations is on the build side: using AI-assisted development tools to compress the time it takes to design, build, test and ship new internal applications. Tools that generate code, write tests, scaffold architectures and accelerate documentation review are not marginal productivity improvements. They are changing what a small team of engineers can deliver in a quarter.

For insurers, this means that internal tools, which previously required months or years of development, in addition to a vendor and system integrator relationship, can now be prototyped in weeks by a small internal team: a partner portal that consolidates program reporting, a claims intake tool that pre-populates fields from submitted documents, and a bordereau ingestion utility that catches data errors at intake rather than surfacing them days into the processing cycle. These applications do not require replacing the core system; they sit alongside it, connect via APIs, and deliver immediate operational value, if the core system supports it.

Technology teams that embrace AI development tooling can reclaim capabilities that have historically required large vendor programs or costly system integrators. They can move faster, iterate based on user feedback, and build institutional knowledge rather than external dependency. The organizations deploying these tools today are already compressing timelines that once seemed fixed.

Embedding AI in workflows: decisions at scale

The second wave is more fundamental: embedding AI directly into operational workflows to improve and automate the decisions that drive the business. This is where the economic case for modernization becomes clearest, and where the data foundation matters most.

Workflow-embedded AI is not a tool a user opens and closes. It is:

• Judgment built into the process itself;
• An underwriting workflow that scores submission quality before a human reviews it;
• A claims triage model that routes cases by complexity and coverage signals in real time; and
• A renewal pricing engine that incorporates loss history, external data, and portfolio exposure without requiring manual assembly. 

These are structural changes to how decisions get made, not incremental improvements to existing processes.

 The distinction between these two modes matters for sequencing. AI development tools can deliver value relatively quickly, even in environments with imperfect data, because they accelerate human work rather than depend on it. Workflow-embedded AI, by contrast, is only as reliable as the data it operates on. A claims-routing model built on incomplete or inconsistently coded data will produce inconsistent decisions. Getting the data foundation right is a prerequisite for this second wave, not a parallel workstream.

Together, these shifts fundamentally change the economics of modernization, lowering barriers to entry and expanding what is possible for more organizations.

Choosing the right retirement strategy for legacy systems

How an organization exits its legacy systems matters as much as what it builds next. The right strategy depends on transaction volume, regulatory complexity, partner dependencies and appetite for operational risk. Three patterns emerge repeatedly in practice.

The strangler pattern

Rather than replacing a legacy system wholesale, new functionality is built alongside it. The modern system gradually takes over individual capabilities — a microservice here, an API layer there — until the legacy platform is functionally surrounded and can be decommissioned without a disruptive cutover. This approach minimizes operational risk and is particularly effective for large, tightly coupled systems where a full replacement is not feasible.

Microservicing and modular decomposition

Some organizations carve specific domains out of a monolithic system and rebuild them as independent, API-driven services, such as claims intake, document generation, or rating, while leaving the core transaction engine intact for now. This creates optionality: Each domain can evolve independently, integrations become cleaner, and the organization builds modern engineering capability without betting the business on a single transformation program.

Sunsetting and runoff

For legacy systems supporting books of business with short or reasonably short policy periods, managed wind-down is often the most pragmatic answer. New business moves to the modern platform immediately; the legacy system is maintained, but not invested in, for the life of the in-force policies. This approach is less visible than transformation but is frequently the most cost-effective and operationally sound path for systems that are not worth rebuilding around.

A mature modernization strategy typically combines elements of all three: strangling core transaction systems, decomposing specific domains into services, and sunsetting legacy platforms that no longer justify investment. Recognizing which pattern applies where is itself a strategic discipline.

The right conditions for change

Since the insurance ecosystem will never be entirely uniform, achieving complete alignment across platforms or data models is neither practical nor essential.

What is achievable is better data exchange. More interactive, near-real-time data integration can deliver measurable value without requiring a complete system overhaul. Progress depends as much on collaboration as on technology, emphasizing the need for open, practical discussions about current data flows and how they can be enhanced for the future.

Ultimately, success will not be measured by who creates the most advanced platform, but by who develops the most adaptable one. Open integration, flexible data structures, and the ability to meet partners where they are will define the next wave of market leaders. The industry has spent years addressing this challenge. With the right tools, patterns, and organizational discipline now in place, the conditions for meaningful change are finally within reach.

About the author

Joe Lettween is Chief Innovation, Data Science, and Technology Officer for global specialty insurer Fortegra. 

Sponsored by: Fortegra

Everyone is talking about AI, automation, and how fast insurance needs to move. Those are important conversations to have. In my role, none of that matters if the fundamentals are not in place.

That was one of the central themes in Send’s recent INFUSE webinar, “Getting the Foundations Right: Building an Underwriting Engine for 2026,” that I participated in recently. The discussion brought together perspectives across technology, consulting, and underwriting, from me, Matt Carter from Altus Consulting and Daryn Upil of The Hartford. What stood out most was how often we came back to the same point: If you want underwriting to be faster, smarter, and more scalable, you must get the foundation right first.

That foundation is not just about technology. It is about underwriting discipline, clarity of decision-making, trustworthy data, and making sure the organization is focused on the things that create value.

The market is changing, and underwriting must respond

Underwriting has always evolved alongside the market, but the pace of change now feels different. In the U.S., we are dealing with increasingly complex catastrophe exposures and continuing legal system abuse. Across markets, customer expectations are rising, data is expanding, and the pressure to make better decisions faster continues to grow. That creates real opportunity, but it also creates risk.

It is easy to get caught up in the promise of new tools. Every insurer is hearing about what AI can do, what automation can unlock, and how quickly operations can be transformed. But underwriting is not improved by technology alone. It improves when technology strengthens sound decision-making.

That, to me, is the key distinction. We should absolutely be looking at how to use AI, data, and automation to build an underwriting engine. But the engine only works when it is built on a solid underwriting foundation.

Technology can accelerate decisions, but it cannot replace underwriting fundamentals

Great underwriting starts with the fundamentals. Do you have clear underwriting rules? Do you understand your appetite? Do your people know what good business looks like? Can you make consistent decisions and explain why they were made?

If those things are not in place, adding more technology does not solve the problem. It just speeds up the wrong process.

That is why the conversation around modernization should begin with the underlying operating model and not just the tools. We need to ask if our processes are designed the right way, is the data useful, and are the outputs we are generating trusted by the people making decisions every day.

There is a lot of noise and urgency in the market right now. Leadership teams are asked every day how they are using AI and how quickly they can implement it. We need to remember that underwriting is a balance of art and science; you can change backwards and forwards, but the art will never go away. We need to stay focused on what matters most to the business and build from there.

Legacy systems are not just a technology problem

Legacy systems remain a challenge for many carriers both operationally and from a people perspective.

A lot of insurance organizations, especially long-established carriers, have systems that have been around for decades. In many cases, those systems have done exactly what they were designed to do. But the industry is now asking more from them…more data, insight, integration, and flexibility in how we deliver underwriting. This is where the strain starts to show.

We also can't ignore the talent piece. When new people enter the industry, they are used to modern technology in almost every part of their lives. If they join a company and immediately work on outdated systems that feel disconnected from how they expect technology to function, it creates friction from day one. Modernization is not just about efficiency. It is also about creating an environment where talented people can do their best work.

Data should support decisions, not create distractions

Data came up repeatedly during the webinar, and better data is one of the biggest unlocks for underwriting.

The goal is not to collect as much data as possible. The goal is to have the right data to support better decisions.

As underwriters, we have more technical information at our fingertips than ever before. We can find out what a building is made of, when it was built, if it is in a hail zone, or sits in a higher crime area. Those, and other risk indicators that may apply, are incredibly valuable. They help us work faster and with greater precision.

We can get all the technical data and risk about a property and still not know enough about the person or business behind it. You may not know how seriously that business owner takes safety. You may not know the quality of their management practices. You may not know how they operate day to day. Those things still matter. They are often what separates an acceptable risk from a great one.

That is why I don't believe technology will replace underwriters. I see it changing where they spend their time. The more we can automate routine tasks and surface technical data quickly, the more valuable underwriters become in the areas where judgment, conversation, and commercial understanding matter most.

The future of underwriting is still human

There is understandable concern in the market about what AI means for the underwriting profession. My view is that the role is not disappearing; it is evolving.

The science side of underwriting is going to become stronger, faster, and more accessible. We will have better tools, broader data sources, and more intelligent workflows helping us evaluate risk.

But underwriting is still a business of judgment. It still requires negotiation, relationship management, pattern recognition, and the ability to see beyond what is immediately visible in the data.

The human element is not going away because as the technical aspects of underwriting become more automated, the softer skills are going to be even more important. Underwriters will need to ask better questions, challenge assumptions, interpret signals, and make thoughtful decisions in situations where there is no perfect answer. That is not something you can simply hand over to a model.

Leadership has to create focus

One of the questions raised during the webinar was how leaders make time to understand the real problem when there is so much pressure to move quickly. I think the answer comes back to focus.

Every leadership team today has more opportunities than they can pursue at one time, so they need to prioritize and decide what matters most to give comfort and confidence to their teams.

The differentiator could be service for some, underwriting expertise, product design, or distribution for others. Technology should help strengthen those advantages and not distract from them.

Leadership should always encourage innovation, but remember they need to be aligned around the right kind of innovation.

Foundations create flexibility

My main takeaway from this webinar is that building an underwriting engine for 2026 and beyond starts with getting the foundations right. If not, technology will just add complexity. This is an exciting time to be in the industry, and we all need to stay focused, prioritize, and bring people along on the journey.

Paul Carroll

GenAI is reshaping insurance. Let’s start there—what’s changing in workers’ compensation?

Tirath Desai

It’s becoming a central conversation. Carriers are asking a fundamental question: what’s next? Many are coming out of a soft market and rethinking growth. Workers’ compensation insurers across the globe continue to navigate common issues related to the changing nature of work, rising medical costs, changing workforce, increasing litigation and regulatory changes.

That’s especially true for state-affiliated funds transitioning into mutual models. Historically, they’ve been focused—single state, single line. Now growth is harder to find. That creates pressure. 

Besides competition, there is a need for expanded capabilities. Differentiation in a crowded market. So, the questions shift. How do we grow? Where do we collaborate? What makes us stand out? AI is at the center of that discussion. Not the only answer—but a critical one.

Paul Carroll

Workers’ comp has long relied on predict-and-prevent strategies. Now we’re seeing new pressures—medical costs, social inflation. What’s changing?

Tirath Desai

Pressure is built on multiple fronts. Costs are rising. Risk is harder to manage, and expectations are shifting. Many carriers have operated within defined regulatory frameworks for years. Now they’re expanding—into larger risks, more complex products, newer distribution models. They’re asking practical questions. Can we improve fraud detection? Strengthen medical management? Deliver a better experience? Reach new channels? 

At the same time, many are holding significant capital while their core book contracts. That tension—capital available; growth constrained—is driving urgency.

Paul Carroll

GenAI clearly improves efficiency. But where does it create real advantage beyond cost?

Tirath Desai

It starts with better decisions. Stronger underwriting. Earlier fraud detection. Faster, more consistent claims handling. Take claims processing. Today, it’s still heavily manual. Notes, documentation, back-and-forth across multiple parties. It slows everything down. AI changes that. It can extract and synthesize information in real time. Build a clearer view of a claimant’s history. Support faster, more informed decisions. 

Payments are another example. Complex. Fragmented. Often difficult to track. With the proper technology, you can streamline that process end-to-end. Fewer delays. More visibility. So yes—efficiency improves. But the bigger shift is quality. Better outcomes. Better experiences.

Paul Carroll

You’ve spoken about a more worker-centric model. What makes that a shift?

Tirath Desai

Today’s experience isn’t built around the worker. Start by reporting an injury claim. Awareness isn’t always there. The process can feel unclear, slow, and disconnected. Now imagine something different. A digital entry point where a worker can report an incident, check eligibility, upload information, claim status and payment—in one centralized location. That data flows directly into core systems. It’s confirmed, summarized, and ready to act on. Compare that to today. Phone calls. Manual entry. Multiple handoffs. Delays at every step. 

We can help remove a lot of that friction. And we can go further. Real-time guidance. Instant answers to simple questions. Support without always needing human intervention. That’s a meaningful shift—for both the worker and the carrier.

Paul Carroll

What happens when you improve responsiveness for injured workers?

Tirath Desai

You can reduce friction. And that matters. Delays and poor communication often cause dissatisfaction. Dissatisfaction can lead to disputes. And disputes can escalate to litigation. More responsive, more transparent interactions help change that dynamic. Now, AI isn’t a holistic solution. It still requires oversight. Judgment. Human involvement is where it matters. But it can remove many of the pain points in the process.

Faster responses. Clearer communication. More consistent experiences. That’s where the real value shows up.

Paul Carroll

Can AI help prevent accidents?

Tirath Desai

There’s potential—but it’s nuanced. Workplace monitoring isn’t new. What’s changing is how data is captured and used. Some approaches rely on wearable devices. Adoption can be a challenge. Over time, employees may resist if it feels intrusive. Other approaches are less invasive. For example, using existing visual data—images or video—to help identify risks. Detect unsafe conditions. Trigger alerts before an incident occurs. That’s promising. But the results are still evolving. 

Many organizations are still working to define the return on investment. So, the opportunity is real. But it requires balance—between insight and trust.

Paul Carroll

Does GenAI accelerate collaborations and ecosystems?

Tirath Desai

Absolutely. No carrier can—or should—build everything alone. The pace of change is too fast. We’re seeing more ecosystem-driven models. Carriers combining internal capabilities with external innovation. Selecting targeted solutions where they can add greater value.

For example, some organizations are building their own AI capabilities. But in areas like litigation support or document processing, they may choose to integrate external solutions instead. It’s about focus. Invest where it differentiates you. Collaborate where it accelerates you. That’s how you can scale effectively.

Paul Carroll

What’s your advice for carriers getting started with AI?

Tirath Desai

Start broader. Not smaller. Many organizations began with isolated use cases. That made sense early on. Now it’s time to step back. Ask a bigger question: How does AI fit across the value chain and a holistic lifecycle—underwriting, claims, billing? Then build from there. 

Three priorities stand out. First, governance. Clear frameworks. Responsible use. Defined accountability. Second, technology. Flexible platforms that can evolve. Integrate new tools. Adapt quickly. Third, data. This is often the hardest part. Many organizations still lack a unified view of their data. Without that, progress slows.

There’s a real opportunity here. But you don’t need to do everything at once. The focus should be clear. Build a road map. Move with intent. Position your organization for what’s next.

Paul Carroll

Thanks.

About Tirath Desai

Tirath Desai is a seasoned leader in the insurance technology space, with deep expertise in insurance core platforms, digital solutions, and large-scale transformation programs. As PwC’s Insurance Core Transformation and Digital Leader, he partners with carriers to modernize policy, billing, and claims operations, enhance agent distribution, and implement innovative cloud and AI-driven solutions.  With over two decades of consulting experience, Tirath has led numerous large-scale transformations, particularly within workers’ compensation and commercial lines. His work emphasizes strong IT service management practices to drive service reliability, governance, and continual improvement across the enterprise. His track record includes delivering end-to-end transformation strategies that generate measurable business value, accelerate speed-to-market, and improve operational efficiency and customer experience. Tirath is particularly focused on integrating artificial intelligence into operational frameworks—leveraging predictive analytics, intelligent automation, and machine learning to optimize claims management, enhance decision-making, and proactively manage risk in workers’ compensation. By combining structured service management methodologies with AI innovation, he helps insurers build resilient, scalable, and future-ready operating models.

Nearly 80% of improper Medicaid payments are tied to insufficient documentation rather than confirmed fraud or abuse. This distinction is critical. It suggests that many of the system's most costly inefficiencies are not rooted in claims processing or fraud detection but in how eligibility data is captured and verified at the very beginning. At the same time, hospitals, in 2025, accounted for approximately $43 billion in care that was delivered but not reimbursed. This reflects a system under strain, where denials, delays, and repeated documentation requests have become routine. Taken together, these trends point to a simple but often overlooked reality: Insurance performance is largely determined upstream, at intake, long before a claim is submitted.

The Overlooked Shift from Claims Optimization to Data Integrity

For decades, insurers have invested heavily in optimizing claims workflows and strengthening fraud detection. These efforts have produced results, particularly in identifying anomalies and recovering funds. However, they largely operate after the fact, once data has already entered the system.

Eligibility intake has not kept pace. In many cases, it is still treated as a compliance checkpoint designed to collect information, rather than a dynamic decision layer responsible for validating it. This distinction matters. When data entered at enrollment is incomplete or inconsistent, those issues do not stay contained. They move through the system, showing up later as claim denials, payment delays, and administrative rework. What begins as a small gap at intake often turns into a larger operational issue downstream.

The scale of the system amplifies this effect. Medicaid alone covers roughly one in five Americans, yet nearly 8% of the U.S. population remains uninsured. Within that group are individuals who are likely eligible but not successfully enrolled, creating both access gaps and financial inefficiencies across the system.

The Strategic Opportunity at the Point of Entry

The growing gap between where insurers invest and where errors originate creates a clear strategic opportunity. Improving data quality at intake offers a more direct path to reducing downstream inefficiencies than continuing to focus solely on post-claim optimization.

Unlike claims processing, which reacts to errors, eligibility intake has the potential to prevent them. By strengthening how data is collected and verified at the outset, payers can improve accuracy, reduce rework, and enhance overall system performance.

Several approaches are gaining traction across industries that face similar verification challenges:

• Verified data inputs: Leveraging trusted, third-party data sources to prefill and validate information reduces reliance on self-reported inputs and improves consistency across records.
• Upfront data support: Providing complete and accurate information at the time of submission helps reduce gaps that would otherwise delay processing or trigger follow-up requests
• Automation of routine checks: Reducing manual review for standard cases allows staff to focus on exceptions, improving both efficiency and accuracy.
• Structured data standards: Ensuring that information is captured in consistent, auditable formats improves traceability and reduces disputes over missing or insufficient documentation.
• Continuous data reconciliation: Periodically validating and updating eligibility data across systems helps maintain accuracy over time and reduces discrepancies that can surface during claims processing or audits.

These strategies reflect a broader shift toward treating intake as infrastructure rather than administration. The goal is not simply to collect data, but to ensure that it is accurate, complete, and usable across the system from the start.

From Reactive Correction to Preventive Design

The current model of insurance operations remains largely reactive. Errors are identified after claims are submitted, and significant resources are allocated to correcting them. This approach is both costly and inefficient.

Manual verification processes contribute to this challenge. They are time-intensive, prone to human error, and difficult to scale. They also introduce variability, as outcomes may differ depending on who reviews the information and how it is interpreted. These inconsistencies increase the likelihood of both payment errors and audit discrepancies.

In contrast, preventive models focus on reducing the likelihood of error at the point where data enters the system. By improving verification at intake, insurers can reduce the volume of issues that require downstream correction.

This shift has implications beyond cost. More accurate intake processes improve the experience for both members and providers. Coverage can be confirmed earlier. Onboarding becomes smoother. Access to care becomes more predictable.

For insurers, it creates a more stable operating environment. Fewer surprises. Fewer exceptions. More consistency across the lifecycle of a claim.

The Future of Insurance Performance Starts at Intake

As healthcare systems continue to evolve, the importance of data integrity will only increase. Coverage models are becoming more complex, regulatory requirements are expanding, and expectations for efficiency and transparency are rising.

In this environment, the performance of insurance systems will depend less on how effectively they process claims and more on how accurately they establish eligibility from the outset. Intake is no longer a peripheral function. It is a foundational layer that influences every downstream outcome.

Reframing eligibility intake as a core performance lever requires a shift in mindset. It means recognizing that the quality of data at entry determines the efficiency of everything that follows. It also means investing in processes and systems that prioritize accuracy, consistency, and verification from the start.

For states and healthcare providers, the opportunity is clear. By fixing the front door, they can reduce administrative burden, improve payment accuracy, and strengthen the overall performance of the system.

Why do drivers in Louisiana pay an average of $4,180 annually for full-coverage car insurance while Vermont drivers only pay $1,504? The answer is simple: territorial ratemaking.

Traditionally, auto insurers have used a policyholder's geographic location as a core input in determining premiums. Variables like historical claims losses, traffic density, and weather patterns are used to estimate the risk profile of a given territory, which in turn, determines pricing.

However, driving patterns now shift faster and vary more locally than the traditional signals used in pricing decisions. Many of the data sources used for territorial ratemaking update too slowly to spot emerging risk shifts and enable timely corrective rate action. At the same time, auto insurers often miss meaningful variations in driving behavior at the ZIP code level due to limited claims information.

In other words, the importance of territory hasn't changed, but the nature of the risk it's meant to represent has.

To more confidently model risk and set accurate rates, auto insurers need a current, granular view of how people in specific ZIP codes actually drive today — not how they drove months or years ago.

Why traditional data alone can't fully reflect today's driving risk

Auto insurers rely heavily on historical claims and loss data to assess territorial risk, but this data is inherently backward-looking and often takes months or years to reflect changes in driving behavior.

This lag is problematic due to the fluid nature of driving patterns. For example, Arity research found that after rising 30% from 2019 to 2023, overall rates of distracted driving declined in 2024 and early 2025.

Driving behavior also varies significantly among ZIP codes within the same state, or even the same county. Consider a residential neighborhood versus a busy commercial area. While the residential area may have steady, low-volume traffic, the commercial area may be a hot spot for stop-and-go driving.

When analyzed at the ZIP code level, claims data alone is often too sparse to produce statistically credible insights. As a result, auto insurers may not detect localized differences and group drivers from the same territory into a single risk profile, potentially overcharging safer customers.

The issue isn't territorial ratemaking itself, but rather the limitations of the data used to inform it. With greater access to driving behavior signals, auto insurers can capture dimensions of risk that many traditional ratemaking factors weren't designed to observe at a territorial level.

How mobility data can transform territorial ratemaking

As driving behavior continues to shift across geographies, auto insurers can't rely on static historical data alone — and fortunately, they don't have to.

With mobility data, insurers can use driving behavior signals like braking, speeding, phone distraction, and time-of-day exposure mapped to specific ZIP codes to enhance territorial pricing strategies.

For actuarial and pricing leaders, this shift does more than introduce a new rating factor. It helps close the visibility gap between how risk is priced and how people are actually driving today.

1. Strengthen data credibility in low-volume areas
   
   Because claims are relatively infrequent events, data at the ZIP code level is often too sparse to be statistically credible. Likewise, commonly used third-party proxies, like surveys or census data, are updated infrequently and may not reflect the most current driving conditions.
   
   These blind spots affect model accuracy, along with file and use confidence, competitive pricing decisions, and how defensible a carrier's territorial assumptions are to regulators.
   
   In contrast, mobility data enables auto insurers to identify local changes in risk before they aggregate to state-level loss trends. This can help supplement sparse loss experience, especially for regional carriers with more limited data.
   
   By incorporating a regularly refreshed dataset that captures current driving patterns mapped to ZIP codes, auto insurers can identify misalignment with historical territorial assumptions and build a more accurate view of risk.
    
2. Increase pricing precision at the local level
   
   Driving behavior is becoming increasingly variable across ZIP codes within the same state or rating territory. Consider developments like return-to-office mandates that affect roadway usage and reshape how, when, and where people drive.
   
   When auto insurers rely exclusively on inputs like third-party data and claims and loss ratios, pricing decisions may not accurately reflect current risk trends. In contrast, mobility data offers context on how driving behavior is evolving, providing an additional layer that helps validate whether similarly priced territories actually share similar risk profiles.
   
   With ZIP codes serving as a practical and familiar linking key, auto insurers can integrate these insights into existing models and workflows, making it easier to adjust segmentation as needed.
    
3. Identify emerging risks to improve rate responsiveness
   
   The use of historical claims data to assess risk introduces a time lag, since changes in driving behavior often take a year or more to appear in loss experience. This delay limits auto insurers' ability to respond in step with evolving driving behavior, leaving them to react after the fact.
   
   Mobility data supports more proactive decision-making by capturing risk shifts as they develop. Because driving behavior is continuously observed and regularly refreshed, it can serve as an early indicator of emerging risk, supporting timely rate decisions without forcing insurers to react to short-term noise.
   
   Additionally, teams can spot emerging risk shifts by tracking year-over-year changes in driving behavior. Those insights can then be built into actuarial narratives, giving pricing decisions and regulatory filings more current, data-backed support.

The future of territorial pricing

Territorial ratemaking has always depended on the quality of the data behind it. But as variability across ZIP codes increases, carriers that rely solely on historical signals risk falling behind trends that competitors can already see.

The gap between auto insurers' geographic risk assessments and actual driver behavior will only widen unless pricing and actuarial teams adapt their approach.

Going forward, auto insurers that embrace mobility data to supplement traditional rating factors can strengthen their territorial models, make more confident pricing decisions, and better identify emerging pockets of risk before shifts appear in claims or loss ratios.

Picture the call.

A state insurance commissioner's office. Your legal team. A customer's attorney. An AI-generated claim denial that affected someone's home, their health, and their livelihood. The question on the table is not whether your model was accurate. The question is who in your organization reviewed that specific decision, what they actually checked, and where the documentation is.

You look around the room.

The data science team points to the risk function. The risk function points at the business unit. The business unit points at the model. The model has no name. The model cannot be deposed. The model's directors and officers (D&O) liability policy does not exist.

Yours does.

The question moving through every insurance boardroom right now is not whether your AI works. It is whether you can prove a human being — a named, accountable, documentable human being — was genuinely in the loop when it didn't.

I have spent two decades working inside financial services organizations across North America, Asia Pacific, and EMEA — in insurance, banking, and enterprise technology. I have been in the rooms where this question lands. The silence it produces is not incompetence. It is the sound of an industry that built extraordinary AI capability and forgot to build the accountability architecture around it.

That silence is becoming expensive.

Your Accuracy Dashboard Is Not a Defense

Here is what your AI governance documentation almost certainly shows: model performance metrics. Accuracy rates. Loss ratios. Straight-through processing volumes. Fraud detection rates. These numbers are real, and the investment behind them is genuine.

Here is what your AI governance documentation almost certainly does not show: the name of the human who reviewed the decision that is now in dispute. What they were trained to look for. How long they spent on it. Whether they had the authority — and the actual expectation — to override the model's recommendation.

Those are two entirely different documents. Most insurers have the first. Almost none have the second.

Under the EU AI Act, OSFI B-15, and SR 11-7, the second document is what matters. Regulators are not asking whether your model performs well in aggregate. They are asking whether a specific decision — the one in front of them — had meaningful human oversight. Meaningful. Not ceremonial. Not a click-through.

Accuracy metrics tell you how often the AI is right. They tell you nothing about whether the human in the loop actually understood what they were approving.

Most insurers have the checkbox. Very few have a defensible record. That gap — between the checkbox and the defensible record — is where the liability lives.

What Happened in the Netherlands Will Happen Here

In 2020, the Dutch government's benefits AI flagged 26,000 families as suspected fraud. Most were innocent. The algorithm ran for years. The humans trusted it. No one built a mechanism for those humans to meaningfully question what the system was telling them.

By the time the full picture emerged, families had lost homes. Children had been taken into care. Careers had been destroyed. The prime minister resigned. The government fell.

Not because the AI was malicious, but because no one could name the human responsible for any specific decision. The accountability architecture was missing. And when it was missing at scale — across 26,000 families — there was no one to hold accountable except the institution itself.

That story is not a European warning. It is a preview.

The same structural failure exists in US healthcare AI, in automated claims systems, in credit decision making, and in hiring algorithms. The technology performs as designed. The human layer — the named, documented, trained, empowered human layer — is absent or ceremonial. When something goes wrong at scale, the institution absorbs the liability because no individual can be identified as responsible.

Unfair AI doesn't just break trust between a customer and a machine. It collapses trust across your entire organization — retroactively. And the collapse travels up the chain until it finds someone with a name.

That name will be on your org chart. It may be yours.

Run This Test Before You Read the Next Section

Pull three recent AI-denied claims from your system. Any three.

For each one, answer these questions: Who is the named human reviewer in the audit trail? What specific aspects of the AI recommendation did they evaluate? Is there documentation showing they genuinely interrogated the output — not just approved it?

If you can produce complete, defensible answers for all three in under 10 minutes, your AI governance is in reasonable shape.

If you cannot — if the trail goes cold at "the system flagged it" or "the team reviewed it" — you have just identified your exposure. That is not a criticism. It is a diagnostic. It is also, increasingly, what plaintiff attorneys run on insurers before they file. What D&O underwriters are beginning to check at renewal. What state insurance commissioners are starting to request in market conduct examinations.

The gap you just found is the gap this article is about.

Three Ways to Close the Gap — Before Someone Closes It for You

Name the human — in the system, in the record, in the audit trail. Every high-stakes AI decision — claim denial, underwriting declination, fraud escalation, pricing exception — needs a named individual reviewer, not a team, not a role, not a function. A person. Because when the commissioner's office calls, they will ask for that person. If you cannot produce a name, you cannot produce a defense.

Build the authority to say no — and document when it is used. The difference between meaningful oversight and rubber-stamping is whether your reviewers have explicit authority to override the AI, training to know when they should, and time to exercise that judgment. If your straight-through processing rates are above 95%, ask yourself honestly: is that efficiency, or is it the absence of human judgment? Regulators are beginning to ask the same question.

Audit fairness separately from accuracy. Your model validation process measures performance. It does not measure whether the outcomes your AI produces are perceived as fair by the people affected. Consistency of treatment across demographics. Accessibility of recourse. Clarity of explanation. These are legitimacy measures and they require a different audit. The insurers who build this capability now will be positioned as leaders. The ones who wait will be building it during an investigation.

The Verdict Is Already Being Written

The insurance industry did not get here through negligence. It got here through speed. AI capability moved faster than governance frameworks. Deployment timelines outran accountability infrastructure. The checkbox appeared because it was faster than the defensible record. None of that was malicious.

But 2025 is not 2019. The EU AI Act is not a distant concern — it is setting the global documentation standard, and US regulators are actively incorporating its logic. D&O underwriters are beginning to ask about AI governance at renewal. state insurance commissioners are starting to include AI decision audit trails in market conduct examinations. Class action attorneys are looking for patterns in AI-driven denials.

The verdict on your AI governance is being written right now — by regulators, by courts, by customers who received a decision they couldn't understand or challenge. It is being written in the audit trails you do or do not have. In the names, you can or cannot produce. In the documentation that proves a human being was genuinely, meaningfully in the loop.

The algorithm will not appear in that verdict. It cannot be deposed. It cannot be held accountable. It does not have a name.

You do.

"The algorithm decided" is not a name. It's a future deposition headline.

Given my education, experience and, I'll admit, personality, mistakes in writing jump up and bite me on the nose. Once, as I flipped through a book, I stopped because something felt vaguely wrong. I read the page I had just glanced at and found a typo about two-thirds of the way down.

Given how much copy I see every day, I see a lot of mistakes, and I think some patterns are worth pointing out. Today I'll focus on the repetition that creeps into our phrasing (no, you shouldn't say people "mutually agree"; by definition, any agreement has to be mutual) and undercuts the crisp confidence we want to project.

These aren't the kinds of mistakes that spellcheck or even Grammarly, in most cases, will flag for you, but they're like termites in a wooden structure. They weaken our writing, while insurance needs to be projecting competence and strength.

Let's have a look.

To me, phrases such as "mutually agree" are like a record with a scratch in it. The phrases quickly repeat themselves, and they hit me with the same sort of screech that a record player can. I realize my reaction is unusually harsh — an occupational hazard and perhaps a personality defect — but such phrases are still worth purging. When you say people mutually agreed to do something, you sound defensive — "Honest, when I say we agreed, I meant it. Really." In fact, in a lot of cases, "mutual agreement" is a euphemism. A coach "mutually agreed" with a team that it was time to part? Yeah, he was fired. Just say "agreed" and get on with it. Your readers will sense your confidence, even if they don't react as viscerally to language as I do. 

If you look a bit, I think you'll mutually agree that there are lot of such screechy phrases. Here are just some that have crossed my desk since I started keeping a list a couple of weeks ago:

• Two people share a common trait. If you share a trait with someone, you have that trait in common, by definition.
• Some number of different people. Why different? You can't have more than one of the same person. But I see "different people," "different businesses," "different" this, "different" that.
• Closely scrutinize. To scrutinize is to look closely at something. You can't look closely closely.
• Major crisis, major catastrophe, major disaster. Can there be a crisis/catastrophe/disaster that isn't major?
• Advance warning. Warning after the fact isn't actually warning.
• Pre-planned. Planning after the fact isn't actually planning.
• Proactive risk management. Reactive risk management isn't actually risk management, at least not for whatever loss you just suffered.
• Someone successfully accomplished something. If you accomplished something, you succeeded. There are many variants of this issue. A New York Times column yesterday, for instance, redundantly said that something "successfully came to fruition" — a new one for me. "Successfully" gets sprinkled into articles and bios like fairy dust. Some aren't inherently repetitive. For instance, bios often say that someone "successfully launched" a product or business. It's certainly possible to launch a product or business that flops, but you wouldn't be telling us about a flop. "Success" is overrated. The word feels needy.
• Speaking of being used like fairy dust, I'll re-up my disdain for new, which I've expressed in earlier rants on language. I appreciate the temptation. We're trying to stir up excitement and move the industry forward, but not everything is new and shouldn't be labeled as such. I'd say the most common (mis)usage I see is "created a new" something (as though you can create an old something). The phrases that most set my teeth on edge are "new record" (as though you could set an old record) and "new innovations" (the root of "innovation" is "-nov-," which means new). Talking about new innovations makes us sound like an old late-night commercial — This product "is new, new, all new. And wait... there's more!"
• Proven track record. The whole point of a track record is that it's proven. It's written down. It's verifiable. You don't need to trust what the tout is telling you about a horse. You can see the track record for yourself.
• Most-well-known. This isn't a redundancy, but it's bizarre, and I'm seeing it a lot, so I'm tossing it in here. The progression goes "good," "better," "best." It doesn't go "good," "better," "most well." So why would the progression about how famous something or someone is go "known," "better-known," "most-well-known"? It doesn't. Yes, "well-known" is a legitimate phrase, but "most well" isn't a thing, so "most-well-known" surely isn't. I think people chicken out because "best" seems like an endorsement. They don't want to use "best" in connection with, say, a notorious criminal, but the only superlative available to you is "best-known." "Most well" simply doesn't exist in the English language, not even if you're describing how done you want your steak to be.

You get the idea. You probably even already go through the sort of self-editing I'm suggesting. You were probably harangued in elementary school to avoid the passive voice and may have been counseled to delete "very" every time you used it. I'm merely suggesting adding something to your to-don't list. 

Your writing will come across as more confident if you eliminate the weak redundancies I've listed — and the million others you'll spot once you start looking.

Fixing these redundancy issues may feel like a small thing, and even a grump like me will acknowledge that the changes will fly under the radar for most people, but I'm reminded of a saying that was my mantra when I used to take long bicycle trips and was packing: "If you take care of the ounces, the pounds will take care of themselves." Customers are demanding that insurance become more understandable, even friendlier. No more of the "whereofs" and "wherefores" in arcane documents that only a lawyer could love. So I don't think it's possible to pay too much attention to the language we use. Every little thing we do becomes part of how customers perceive us.

You now have your advance warning. You can proceed with your proactive pre-planning.

Cheers,

Paul

P.S. Here are some of my favorite previous rants on language: "Can We Please Tone Down All the 'Inflection Point' Talk?"; "Let's Stop With the Gibberish"' "May I Rant for a Moment?"; and "Two Words We Must Stop Using."