A policyholder calls after a flood. She has three feet of water in her kitchen, two kids on the couch, and nowhere to go until she understands what her coverage actually means right now. She has been on hold for 11 minutes. She has already provided her policy number twice. When she finally reaches an agent, she has to start from scratch and do it a third time.

This is the moment the industry exists for. And it's where too many insurers are still failing—it's costing them billions, according to Accenture.

Not because insurers lack technology. Most have more technology than they can connect—voice, messaging, claims systems, and decades of customer data. The problem is that none of the systems know about the rest. Data is fragmented. Context gets lost. And when that happens, the experience feels like nobody cares.

Insurers Solved the Easy Problems. The Hard Ones Are Still There.

There's no shortage of success stories of transformation in this industry. Quotes that once took days now take seconds. Documents arrive instantly. Policies are issued, updated, and managed entirely online. These were hard problems that took real investment to solve.

But they were also easy problems, in one important sense: they could be solved by adding technology. New system, new process, done. What remained unsolved—and what most digital transformation narratives quietly skip—is what happens when a customer actually needs help. When the claim is complex, the situation is urgent, and the stakes are high enough that the experience leaves a mark.

These customer experience (CX) moments don't operate on quiet days. They cluster. A major storm, a regional crisis, a sudden surge in claims, and the entire engagement infrastructure gets tested simultaneously. That's when the gaps between disconnected systems stop being an annoyance and become a liability. KPMG sees fragmentation as one of the biggest challenges to modernization.

The Insurers Moving Fastest on Cloud Aren't Talking About Cost

When insurers talk about cloud migration, the framing is almost always about cost. Total cost of ownership. License consolidation. Infrastructure savings. These are legitimate conversations. They're also the wrong ones to lead with.

On-premise environments were designed for predictability and control. They were never built for real-time, multi-channel engagement at scale, and they can't be retrofitted to handle it. When volumes surge or conditions change, legacy environments don't flex. They fracture.

The insurers making the most meaningful progress aren't asking "can we afford to modernize?" They're asking, "can we afford what happens when we can't scale?" and finding that the math looks very different from that angle.

The insurance industry is spending heavily on AI right now. The ambition is real. So is the disappointment when the demos don't survive contact with actual operations. According to the Boston Consulting Group, only 7% of insurance companies have successfully brought their AI systems to scale.

The reason is almost always the same: AI has been layered on top of fragmented systems and asked to compensate for what those systems lack. When data is inconsistent across platforms, when context doesn't travel with the customer, when the same information lives in four different places. AI doesn't resolve any of that; it amplifies it. The inconsistencies become more visible, not less.

AI performs well in a specific role: removing friction from routine interactions, capturing and routing information early, and surfacing the right context at the right moment so agents can focus on judgment rather than administration. In a connected, well-structured environment, that's genuinely transformative. In a fragmented one, there's more noise on top of existing noise.

How Integrated Communications Changes the Customer Experience

Here's a diagnostic test that reveals a lot about the state of an insurer's communications infrastructure: ask what happens when a customer starts a conversation on the website, moves to chat, and then calls.

In most cases, the answer is: they start over. Three times. No context carries. No continuity exists. Just repetition and the implicit message that the insurer's systems matter more than the customer's time.

When communications infrastructure is genuinely integrated, this changes. Interactions route based on intent rather than just inputs. Context moves with the customer from channel to channel. Conversations pick up where they left off. The difference in resolution speed, first-contact rates, and customer perception is significant, but the more important outcome is harder to quantify: customers feel like the insurer actually knew who they were.

It can also be profitable. McKinsey found that during 2017-2022, insurers with superior CX posted higher total shareholder returns—by between 20 and 65 percentage points.

What It Actually Takes to Modernize Insurance Communications

Modernizing insurance communications is not about adding more technology. Most organizations already have enough. The work is simpler and harder than that: connect what already exists.

When that happens, the contact center stops managing problems and starts anticipating them. Agents stop reacting and start advising. The experience stops being a test of the customer's patience and starts being evidence that the insurer takes seriously the one thing they actually promised: to be there when it matters.

Customers don't remember your technology stack. They remember the call that went well when everything else was going wrong. That's the product.

What if your car insurance reflected how you actually drive, not just who you are? That question is no longer hypothetical. In 2024, more than 21 million U.S. policyholders shared telematics data with their insurer, according to IoT Insurance Observatory research. That reflects a 28% compound annual growth rate since 2018. Usage-based insurance (UBI) is no longer a niche; it’s a mainstream strategy reshaping our industry.  

For years, competitive pricing drove adoption. But today, something deeper is at play: Trust and perceived value are fueling the next wave of growth. This isn’t just about saving money; it’s about believing the insurer will use sensitive driving data responsibly and deliver tangible benefits in return. 

Source: IoT Insurance Observatory field customer surveys

According to a recent consumer survey by Arity and the IoT Insurance Observatory - sampling 2,059 personal auto policyholders representative of the U.S. market - 82% of policyholders would recommend a telematics app that rewards safe driving, offers feedback, provides crash assistance, and delivers other valued services. Among drivers under the age of 53, that number exceeds 90%. Positive sentiment toward telematics has steadily increased over the past decade, as shown in the chart above.

Trust isn’t a buzzword here, it’s the foundation of adoption. Consumers share data only when they believe insurers will protect it and use it to create real value. In fact, 53% of respondents expressed high trust in insurers’ handling of personal data, ranking insurers second only to banks. That trust translates into action: willingness to switch plans, share driving scores, and pay for connected services. 

The willingness to adopt UBI is strong: 60% of policyholders are open to switching, rising to 72% among younger drivers. This level is consistent with the evidence from recent TransUnion surveys showing that 60% of people reported being offered telematics opted-in.

When consumers see clear benefits, privacy concerns fade. They want pricing that reflects lifestyle, rewards for safe driving, and features like automatic crash assistance. Three-fourths are open to sharing their driving score for a personalized quote. More than half of those willing to switch prefer pricing models that offer bigger potential savings, even if it means some risk a surcharge. 

This is where telematics shines. Insurers can deliver compelling offers because telematics unlocks incremental economic value by sensing events, transmitting data in real time, and applying AI-driven analytics to understand, decide, and act. This enables smarter underwriting, faster claims processing, and more proactive risk management. By sharing part of this value with policyholders, insurers create a win-win scenario that makes UBI not just viable - but mainstream.

Source: IoT Insurance Observatory

Over the past decade, insurers have proven the power of telematics data to transform core functions: 

• Continuous Underwriting: Telematics data enables more accurate risk assessments and selection, allowing insurers to better match rates to actual risks. This leads to more sophisticated pricing, improved retention and effective acquisition of good risks, and reduced premium leakage from riskier drivers. Insurers can also use telematics-based data to make portfolio-level decisions regarding risk appetite and reinsurance.
• Enhanced Claims Management: Real time crash detection is a game-changer for claims management. Insurers can trigger proactive responses, notify emergency services, and initiate the claims process. Insights about crash events support timely claim handling and help minimize potential fraudulent or inflated requests.
• Connect and Protect: International telematics-based experiences demonstrate effectiveness in mitigating risks by identifying risky situations in real time and intervening before accidents occur.  Behavioral change programs promote safer driving, leading to fewer accidents and lower loss ratios. 

Policyholders are willing to reconsider their insurer when pricing reflects how they actually drive and live: More than half of policyholders would switch for a product with the premium defined by telematics data. 

Consumers aren’t just looking for lower premiums; they want features that matter. Rewards for safe driving and automatic emergency assistance in severe crashes rank among the top preferences across all generations - from Gen Z to Traditionalists. And the appetite for innovation doesn’t stop there: More than half of policyholders would pay $4.99 per month for a connected dashcam service that offers emergency assistance, video recording for protection against unfair complaints, and real-time safety feedback.

The impact goes beyond individual policies. When the usage of telematics data is holistically adopted across the entire insurance organization, this improves pricing accuracy, reduces losses, and makes insurance more affordable, all while promoting safer roads. Fewer accidents mean fewer injuries and lives saved. That’s why telematics is more than a business strategy, it’s a social good.

The time to invest in telematics mastery is now. Insurers that fully embrace the connected paradigm in all their core processes and responsibly use data with customer consent can unlock greater value—delivering fairer pricing, personalized experiences, and safer roads. This broader usage of data enables higher value creation and sharing, benefiting policyholders and society as a whole.

Fraud is hardly a new problem, but it is a serious issue, and recent fundamental changes in societal norms are exacerbating fraudulent conduct and making detection and deterrence less of a priority than warranted. The scope and scale of fraud are truly shocking, especially among government-funded medical and social programs currently under scrutiny, where enormous costs are somehow tolerated.

Fraud not only creates significant economic loss but also undermines confidence in its public and financial institutions, including insurance. Yet preventing and combatting fraud is seemingly episodic and random. 

All of this serves to bring renewed attention to the long-standing concerns about ever-expanding fraud in general – and specifically insurance fraud. Insurers need to heed the wake-up call.

COST OF INSURANCE FRAUD

Quantifying insurance fraud's impact is difficult and spans from premium fraud to claims fraud, whether opportunistic or through deliberate scheme. According to the Coalition Against Insurance Fraud (CAIF), insurance fraud costs American consumers more than $300 billion a year. This amounts to an individual policyholder $900 annual “tax,” as insurer costs are passed on in form of premiums. Claims fraud is said to occur in about 10% of property-casualty insurance losses. Medicare fraud alone is estimated to cost $60 billion every year.

There are also several limitations when it comes to detecting fraud. According to the National Association of Insurance Commissioners (NAIC), there are key differences between “hard” and “soft” types. Soft forms of fraud are widespread and can be a common exaggeration of a legitimate claim. Hard types are described as intentional acts to create or fabricate “damages” and claims. Still, these general headers fall short of telling the whole story. 

Claim fraud can be perpetrated by an individual or involve others including organized crime rings recognizing there are entire ecosystems designed to inflate, embellish and even fake an accident. Billing for unperformed medical procedures pales in comparison to fake “victims” being paid to undergo surgery. A single case in New York uncovered a $31 million scheme between a doctor and lawyer in trip-and-fall “accidents,” paying "victims" to endure surgery, simply to initiate a claim, justify damages or both. So-called runners are paid finder-fees to produce participants.

Further, many frauds go undetected for long periods or are missed altogether because there is much reliance on the “honor system,” whether at point of sale in which premiums are based or when making a claim. Although any healthy system checks and verifies, it is impractical, unnecessary and risky to deeply investigate a large percentages of cases. Insurers balance customer service, state regulatory requirements involving timeliness and potential complaints that can escalate to lawsuits. 

Meanwhile, internal special investigative units (SIUs) likewise have finite resources and bandwidth, only concentrating on the most actionable cases. Law enforcement agencies have similar constraints, and insurance fraud is a lesser priority than other crimes. Altogether, this dilutes the efficacy of combatting fraud, leading to uncaptured and under-reported figures.

Instead, anecdotal case examples tend to do the best job of illustrating the magnitude of fraud. Phony medical clinics, staged auto accidents, even faked deaths demonstrate the amounts at stake and the lengths fraudsters will go. More frustrating is how obvious some of the schemes are, revealed as in the infamous empty day care center stories. 

But what happens when technology pushes the boundaries beyond such traditional fraud methods?

The Yin and Yang of AI and Insurance

The rapid emergence of artificial intelligence has brought greater business risks, and the financial services industries are among the largest victims of related fraud. Ironically, business is quickly learning to harness the power of AI to fight fraud more effectively – but so are the fraudsters. 

The potential of AI in claims fraud detection is among the most powerful applications, and particularly so in life & health and accident, according to a February 2026 report from Gallagher Re and CB insights: "Global InsurTech Report."

AI has many benefits. It can improve efficiency, help make better decisions, and encourage innovation across different industries. But these advantages also come with serious risks – especially the potential for misuse in fraud or deception.

Like any powerful technology, AI can be used for both helpful and harmful purposes. This makes strong and thoughtful governance essential to maximize its benefits and protect against misuse.

Hackers and other criminals can easily commandeer computers operating open-source large language models (LLMs) outside the guardrails and constraints of the major artificial-intelligence platforms, creating security risks and vulnerabilities, researchers said.

Hackers could target the computers running the LLMs and direct them to carry out spam operations, phishing content creation or disinformation campaigns, evading platform security protocols, the researchers said. Roberto Copia, director at IVASS Inspectorate Service, spoke about this issue at the 4th National Congress of the CODICI Association in 2025. He pointed out a growing concern: While AI can improve the efficiency of the insurance industry, it can also give fraudsters more advanced tools to commit fraud.

AI and Insurance: An inseparable alliance

AI is cautiously becoming an indispensable tool in the insurance sector. Its applications range from risk assessment to customer services, claims processing and fraud detection. Predictive algorithms, neural networks, and machine learning models allow the processing of vast datasets, improving underwriting accuracy, accelerating claim settlements and strengthening insurers' anti-fraud capabilities.

But these very tools – powerful, scalable and increasingly accessible – are also being weaponized by fraudsters. “Those who seek to commit fraud are often skilled innovators – frequently one step ahead of those tasked with stopping them,” Copia has said.   

A quantum leap in criminal sophistication

Insurance fraud has always been a structural problem in the sector. Yet today, it’s undergoing a qualitative shift. We’re no longer dealing solely with fraudulent damage to property or fictitious claims. Modern fraud is digital, automated and highly sophisticated. AI has become a powerful enabler for those seeking to manipulate data and images, forge documents or create false digital identities.

A March 2026 report, Verisk State of Insurance Fraud Study, finds, based on surveys of 1,000 U.S. consumers and 300 insurance claims professionals:

• 55% of Gen Z say they would consider editing a claim photo or document
• 98% of insurers say AI editing tools are fueling digital fraud
• Only 32% of insurers feel very confident about detecting deepfakes
• 69% of consumers believe fraud will raise premiums for all policyholders

A paradigmatic example is the Ghost Broker scam: insurance websites that appear legitimate, often employing advanced social engineering techniques, real logos, and data stolen from unwitting intermediaries. AI allows these fraudulent portals to appear increasingly credible, complete with chatbots simulating customer service, AI-driven profiling of potential victims, and the delivery of highly personalized fake offers. The result is a seemingly flawless customer journey. But the buyer is left uninsured and unknowingly defrauded until subsequent inspection reveals the deception.

Another example involves "synthetic" identity fraud, in which fraudsters create an identity with a mix of fabricated credentials. According to Lexis Nexis, fraudsters may create synthetic identities using potentially valid Social Security Numbers (SSNs), with accompanying false personally identifiable information (PII). This newer challenge raises the bar for insurers to validate identity at point of sale and other policy lifecycle stages.

THE FRAUD FIGHTING IMPERATIVE

We believe that insurers have an obligation to prioritize fraud detection and avoidance in this growing, too-big-to-ignore dynamic. This obligation is moral, economic and legal. An insurer’s duty to its policyholders includes protecting their investment while managing fair and accurate premiums alike.

Paul Carroll 

With AI transforming underwriting, some say the function is entering a new phase. Do you agree? 

Katie Klutts Wysor 

When you look at what property and casualty carriers are saying and what brokers are starting to say, there’s broad recognition that generative AI could reshape risk assessment and underwriting in meaningful ways. It’s becoming an enabler. It can help underwriters make better decisions and work more effectively. 

Carriers are focusing on underwriting as a function and investing heavily in it. They’re talking about that focus in investor days and earnings calls, and they’re doing significant work internally to organize data and update processes—improving speed, increasing automation, accelerating turnaround times and supporting more informed decisions with better data. 

What’s even more significant than the process improvements is what AI could do more broadly. Think of the underwriter as managing capital and trying to direct it to the ideal place. How can AI help define the parameters around what the carrier wants to write so it can deploy capital where it is targeting stronger returns? From there, you can align appetite and business mix with the underwriting process. 

For example, if I’m a regional workers’ comp player and I want to expand into other lines of business or other states, how can I use AI to support that portfolio management decision and direct capital more effectively? Then, how do I identify the necessary distribution partners to find the business I want? How do I create the proper incentives for distribution partners to bring forward that business, so I have a submission to underwrite? And then, how do I make sure the underwriting process, and decision-making aligns with my appetite? 

I think that’s where a great deal of value could be created from an underwriting perspective, looking at how AI can help inform research on the front end, and how you then use something like a GenAI-enabled underwriting platform to begin systematically embedding strategic capital decisions into appetite, process and guidelines at scale, so underwriters evaluating risks are working from more relevant information. 

Then you can use AI to respond to new business decisions more quickly, respond to renewal decisions more effectively, and potentially take certain actions during a policy’s term to support risk mitigation conversations. 

If you can start mastering that link—how you’re deploying capital and setting appetite, all the way down to those micro process decisions—that represents a new level of maturity. 

Paul Carroll 

Speed has become a significant competitive factor in insurance underwriting. If you’re slow to quote, even with a slightly better price, you may lose the business. What’s happening in terms of speed in the underwriting process, and how do processes need to change—not just the technology—to take advantage of the speed AI can offer? 

Katie Klutts Wysor 

Speed to quote and bind means something very different across varying lines of business. In auto insurance, you need to be able to deliver a quote in seconds. So, you see many personal lines players focusing on quote simplification. In auto, it is close to a mature problem, and many carriers are following established market patterns to stay competitive. 

But the speed question gets harder as you move into more specialized or complex lines in personal insurance and small commercial, middle market and large commercial. 

What we’re seeing there is impressive. Capabilities are now emerging to triage submission intake. From a technology perspective, carriers increasingly can take a submission no matter how it comes in—via email, a platform or another channel—and combine it with what they already know about that risk, along with relevant third-party data. 

At that point, it becomes an execution challenge. How can you more systematically get to a quick yes, no, or maybe on appetite, and then move effectively  toward a quote? 

How fast that can happen depends on the line of business. But that point is right: Carriers should move as quickly as their competitors. If you’re slow, distribution may not be willing to wait. 

And the benchmark will continue to move. 

Paul Carroll 

How far along is the insurance industry in using technology to allocate capital more intelligently, and what needs to happen to reach the next level? 

Katie Klutts Wysor 

It’s less a technology challenge and more a business decision-making challenge. Some players in the market are especially strong at this, and you can see that by looking at underwriting returns over time. Those companies have consistently used technology and data to manage their portfolios and allocate capital with greater precision, and they will likely continue to adopt new approaches as the tech capability improves. The timeline for broader adoption of newer technology, including generative AI, is harder to predict because it comes back to how quickly "the pack" of carriers can  evolve how they manage the profit and loss across the portfolios. 

Paul Carroll 

What is an example of how insurers can improve their capital allocation? 

Katie Klutts Wysor 

The fundamental approach is to look at your underwriting returns against the capital you’re deploying to the business, map that out, compare outcomes, and decide where you want to grow and where you may need to pull back. Improvements in technology may allow carriers to do that analysis more frequently. Instead of doing it once a year as part of strategic planning, you could be looking at a refreshed view every month using more current data. 

Many carriers may be able to move from annual reviews toward monthly or weekly review cycles, depending on how they make decisions. They may also be able to do the analysis in a more automated way and make decisions more intentionally on micro-segments of the business (by geography, class, line, etc.) that would have been too time-consuming to identify and react to previously. 

Maybe a competitor enters the restaurant space aggressively and undercuts on price. You may decide not to follow them down that path because you believe the pricing is unattractive, so you slow growth in restaurants. 

Or take the opposite scenario: A trend affects restaurants and causes the market to become more cautious. You may conclude that the market reaction has gone too far and decide if this is the right time to pursue restaurant business. 

Today's leadership reviews may only look at class code-level data monthly or quarterly, and at frequency and severity trends in a backward-looking way. But if you can automate how, you assess that information at a portfolio level, then you can decide whether to lean in or lean out of a class like restaurants more quickly.  

Paul Carroll 

What about the execution side of this—how do insurers actually act on these faster insights once they’ve identified an opportunity? 

Katie Klutts Wysor 

The second half of the equation is exactly that. Say you’ve been able to automate and generate more timely underwriting data, so you can make portfolio decisions weekly or monthly instead of quarterly or annually. That’s a meaningful shift. The next step is execution. 

Say you decided to lean into restaurants. You want the market to know. You want your agents and distribution partners to know you’re interested in that business, particularly if another carrier has started to pull back or take rates. That’s the business you want to enter the pipeline. 

Then you want to set up your underwriting process so you can pivot quickly. Maybe you were not prioritizing that business to get it to an underwriter’s desk and streamline escalation paths to support faster turnaround. 

Of course, once submissions are flowing in, and the process is in place to evaluate and price the business you want competitively, you also need the proper governance and controls, so you don’t end up writing risks that fall outside appetite. 

The big difference this year versus a year ago is the ability to put agentic AI workflows in place and that support faster transaction-level decisions. Humans are still in the loop, but they are not necessarily slowing down the process in the same way they did when carriers relied more heavily on manual referral and escalation processes to respond to market changes. The next frontier I expect to see in the coming year is using agentic AI workflows to help improve portfolio-level decisions.

Paul Carroll 

Would you talk a bit about some of the process efficiencies from generative AI as underwriters make their decisions? While those efficiencies aren’t as strategic as the portfolio-level decisions you’ve described, they still seem substantial.  

Katie Klutts Wysor 

Underwriters face a series of yes, no, and maybe decisions, and much of the friction sits in the maybes. You can automate obvious yes-or-no decisions. The maybes are the gray area where you bring in a person. 

Over time, we may be able to bring in a person less often because of agentic AI and other decision-support tools, while maintaining appropriate human oversight. 

Some maybes exist simply because a piece of information is missing. A file gets routed to an underwriter to obtain one additional data point. Once that data point is available, a rule can be applied, and the case can become a yes or a no. In many cases, that is increasingly solvable today.  

You should identify those cases in your portfolio, then use AI to obtain the data point and apply the rule. 

There are also maybes that are more judgment-based, where you’ve created a manual review because you want someone to look at it who has seen this kind of case many times before. Maybe they’ve seen a six-figure loss in a similar situation, so you ask, “Would you write this again knowing what you know now?” 

Agentic AI workflows can help by bringing more context to the situation and supporting more informed underwriting judgment.  

Paul Carroll 

Based on what you’re seeing, how much are underwriters working with brokers and clients to provide guidance on risk reduction—essentially telling them, “you’re getting dinged for this, why don’t you fix it to reduce your risk?” 

Katie Klutts Wysor 

Right now, it’s predominantly brokers and distribution partners that are providing that first line of risk management advice. But there’s also a meaningful role for underwriters and carriers. 

The concept is there, the question is how consistently it can be translated into actionable guidance. 

Paul Carroll 

What final advice would you offer to readers? 

Katie Klutts Wysor 

What my clients care about is taking some of the bigger ideas and translating them into what to do right now and how to respond in practical terms. So, what I’d leave readers with is this: Keep thinking about the art of the possible but also focus on what you can implement now to strengthen performance this year, and start bringing those two together. 

Understand what technology, data, and AI capabilities are available to you. But more importantly, identify which ones you can deploy quickly while you continue building toward the more complex architecture and data challenges you should address over time. 

Paul Carroll 

So you can create a cycle: make targeted investments that create near-term efficiencies, then use those gains to support the next wave of investment. 

Katie Klutts Wysor 

Exactly. Don’t spend three years trying to build the perfect solution. There’s a lot you can do right now. Deploy something practical that can create value, then use those gains to support larger investments over time. 

Paul Carroll 

Thanks, Katie. 

About Katie Klutts Wysor

Katie Klutts Wysor is a Principal with PwC who advises insurance leaders on strategy, growth, and transformation. She focuses on analyzing evolving market dynamics to shape perspectives on the future of insurance and translating those insights into practical, outcome-driven growth strategies and transformation programs for carriers and brokers/distributors.

Here's a thought experiment for you: What if Elon Musk ran an insurance company?

Just imagine how regulators would react to his brash, visionary ideas wrapped in disdain for tradition and a belief that rules don't apply to him. 

But what if you could bottle the good parts of his iconoclasm and leave out the parts that would scare policyholders about the reliability of their insurance carriers? A former president of Tesla just published a book that might allow for that. It describes the five-part "algorithm" that he and Musk used to manage the company during a transformative stretch in the mid-2010s. 

I don't think insurers should go full force, a la Musk's "hardcore" mode--you could wind up with an embarrassment like DOGE and never recover--but his algorithm does offer a playbook for radical simplicity and for what I think is the right way to approach automation. 

Jon McNeill, author of "The Algorithm: The Hypergrowth Formula That Transformed Tesla, Lululemon, General Motors, and SpaceX," says the method has five steps:

• Question every requirement.
• Delete every possible step in the process.
• Simplify and optimize.
• Accelerate cycle time.
• Automate.

Question Every Requirement

McNeill writes about how Tesla, for instance, questioned China's requirement that it own a piece of any company operating in the country and eventually negotiated a deal that let Tesla own 100% of its Chinese subsidiary. He also writes about deciding that cars didn't need to be assembled out of so many parts, even though they had been since the days of Henry Ford. Instead, Tesla began experimenting with casting bigger and bigger pieces of the car and eventually succeeding, greatly reducing the need for assembly.

For insurers, though, I'm thinking the real benefit would come in more modest ways that track more closely with an anecdote McNeill told in a podcast with the Wall Street Journal. He talked about how much trouble Tesla had designing and manufacturing a part that was supposed to sit between a battery and the chassis. The problem became so important that Musk got personally involved and haunted the factory for weeks. Eventually, Musk and McNeill asked if the part was really necessary, and the battery people told them it had been mandated by the folks responsible for damping noise. When McNeill went to them, he was told that, no, the battery folks had mandated the part to minimize danger in the case of a battery fire. McNeill decided to track down the engineer who had signed the order requiring the part -- and learned he couldn't reach the person because he was a summer intern who no longer was at Tesla.

Insurers already question what they believe to be undue regulation, but I think they could benefit more broadly from asking employees across the business to question everything they're told to do, whether by someone inside the organization or outside it. Even if you just do this as a one- or two-month exercise, I'd bet you'll find you're doing lots of things just because they've always been done that way, not because they deliver any value.

Delete Every Possible Step in the Process

At Tesla, McNeill said in the podcast, he deleted several steps, and Musk asked whether he'd broken the process as a result and received some severe pushback. When McNeill said he hadn't, Musk told him he hadn't gone far enough. He needed to keep pushing until he not only got close to the bone but cut into the bone -- at which point, he should back off and find a sustainable approach.

McNeill said the rule of thumb was to only deliver what the customer directly paid for: the car. Customers didn't pay directly for manuals, for documentation, and so on, so Tesla would spend as little effort as possible in those areas.

Again, I don't think that approach would survive at an insurance company. Cut-until-you-break-something can happen in a manufacturing process, behind the scenes, but it didn't even work at the Department of Government Efficiency (DOGE), which Musk ran in the early days of the second Trump administration. Even with the slash-and-burn ethos of Trump 2.0 a year ago, Musk cut too deeply and caused problems both for those receiving government services and for Trump. 

Still, insurers can suffer from a sort of data and process bloat. Given the industry's abundance of caution, it's easy to ask for more questions, to gather more data, and to require another guardrail in the process. Life insurers have shown that it's possible to do the same with less, given the success of fluidless underwriting, and other lines could surely scale back some requirements, too -- becoming more efficient while making customers happier.

Simplify, Accelerate, and Automate

I'm combining the last three parts of the Tesla algorithm because, at least for insurers, they all fit under one mandate: "Automate last."

McNeill said Tesla learned the value of these three steps when it was having so much trouble manufacturing the Model 3 that it was running out of cash and was in danger of bankruptcy. The company stopped running its highly automated manufacturing line, set up a big tent outside the factory and started making the cars by hand. Once management figured out the best process, it began speeding up. Only once they saw that they could run the process at speed did they start bringing in the machines that would automate it -- scrapping the entire production line that they'd set up before fully understanding what was needed.

"Automate last" fits with the approach the computer industry has taken for decades: Once a manual process is fully mapped out, it can move into software and then, when you're sure you have everything nailed down, you can hard-wire the work by moving it into the silicon. 

That approach makes sense for insurers, too. When you see the possibilities of AI, for instance, you should map out a potential new process, implement it manually, speed it up -- and only then let the machines take over.

There are plenty of things about Musk's approach to business that I wouldn't recommend. For more than a decade now, I've been mocking his annual claims that he'll have millions of Teslas functioning as robotaxis, that he's going to colonize Mars (we won't even land someone on Mars in his lifetime), that he's about to unleash an army of humanoid robots, and so on. Those of us without his massive wealth would lose all credibility overnight if we pushed a similar sort of sci-fi dream. Insurance, as an industry built on trust, can't afford anything close to the wild claims that Musk makes routinely.

But I do think it's worth giving his algorithm serious consideration because it can reduce complexity and lead to effective automation. If nothing else, reading about the bold moves at Tesla might inspire some new thinking and resolve in the insurance industry. 

Cheers,

Paul

P.S. "The Algorithm" reminds me of one of my favorite geek jokes:

Q. How do we know that Al Gore actually invented the internet?

A. It runs on Al-Gore-ithms. 

Digital insurance agency platforms are the cornerstone of the insurance ecosystem. With digital first consumers demanding seamless experiences, agencies and carriers need technology that delivers speed, insight, and engagement. Zywave received the highest rank of all vendors in the strategy category, and maximum possible scores in the innovation, vision, and roadmap criteria in this report. 

The report provides a comprehensive evaluation of insurance technology platforms, analyzing current offering and strategy. See how Forrester evaluated 10 top platforms, and why Zywave earned the highest score in the strategy category and the highest possible scores in the criteria of vision and innovation. 

The Forrester Wave™ report noted:

• "Zywave’s vision is to facilitate the growth of insurance distributors by becoming an integrated, open API software suite powered by agentic AI".
• “Its impressive roadmap and innovation aim to bolster quoting and carrier connectivity as well as use AI-powered agents to automate workflows.”
• “Zywave’s SaaS platform offers agencies a robust marketing toolkit, quoting and proposal tools, and product analysis and comparison for personal, commercial, and benefit lines — aided by a highly intuitive UI and extensible platform architecture.” 

Access the Report

Sponsored by ITL Partner: Zywave

The U.S. healthcare system wastes between $760 billion and $1.6 trillion every year. That range comes from a landmark 2019 JAMA study and updated 2025 expenditure data from CMS. If you work in insurance or risk management, that number should stop you cold. It is larger than the GDP of most countries. It represents roughly 25-30% of total national health expenditure. And the researchers who quantified it also confirmed something important: proven interventions could save $191 billion to $282 billion annually.

This is not a projection based on theoretical models. It is a documented opportunity sitting inside the claims data of every self-insured health plan in America.

Where the Waste Lives

JAMA identified six categories of waste: billing errors and fraud, administrative complexity, unnecessary services, pricing failures, failure of care coordination, and other inefficiencies including underuse of preventive care. Each category is quantifiable. Each has known interventions. None of them require inventing technology or waiting for policy reform.

Consider the pricing problem alone. An echocardiogram can cost $350 at one facility and $2,700 at another in the same market, with no corresponding difference in quality. The Purchaser Business Group on Health (PBGH) found that commercial negotiated rates for identical procedures vary by more than 100% between regional markets. The primary driver of excess U.S. spending is higher prices, not greater usage. Americans use many healthcare services at lower rates than peers in other developed nations but pay far more for the same services.

On the pharmacy side, brand-name drugs are routinely dispensed when therapeutically equivalent generics exist at a fraction of the cost. PBM spread pricing, where the pharmacy benefit manager charges the plan one price and pays the pharmacy a lower price, persists because most employers never examine their claims data and their vendor contracts at the line-item level. Organizations that shift to transparent, pass-through pharmacy pricing models are documenting savings of 15-30% on pharmacy spend.

These are not outlier cases. They are structural patterns that repeat across virtually every health plan I have analyzed over the past decade. These analyses and corroborating studies consistently indicate that about half of all employer-sponsored health plan spending is inefficient or wasteful.

Why the Problem Persists

The most dangerous aspect of healthcare waste is that it hides in plain sight. It does not appear as a line item labeled "unnecessary" or "inefficient." It is buried in inflated claims, redundant procedures, opaque vendor clauses, and recurring overpayments to providers.

Many employers rely on their chosen third-party administrators, insurance brokers, or pharmacy benefit managers to manage most aspects of their plans. In a system riddled with misaligned incentives, that trust is often misplaced. When PBMs, for example, profit from higher usage of certain high-cost drugs or maintain deliberately opaque rebate arrangements, waste is not just tolerated. It is the business model!

The traditional cost-control approach is entirely reactive. An employer negotiates rates during contracting season, processes claims throughout the year, and then hires an auditor to review a random sample once or twice annually. By the time anyone identifies an overpayment or a suspicious billing pattern, the money is long gone. Recovering it requires time, administrative effort, and often a fight with the provider or vendor. Most employers never recoup the full amount, if they recoup anything.

The Intervention Point Has Shifted

AI and advanced analytics have moved the intervention point forward. Instead of reviewing claims after they have been paid, AI-powered platforms can analyze claims before payment is released. Every claim is fed through thousands of logic checks based on CMS guidelines, billing codes, plan-specific terms, and more. When a claim triggers a flag for a duplicate charge, an upcoded procedure, unbundling, or a charge exceeding contracted rates, the system can pause or deny payment until the issue is reviewed by a human. That is a fundamental shift from recovering waste after the fact to preventing it from occurring in the first place.

Predictive modeling takes this further upstream. Predictive engines analyze historical claims data, clinical indicators, pharmacy usage, and demographic profiles to identify plan members likely to become high-cost members in the coming months. When the model flags a member as high-risk for a cardiovascular event or a deteriorating chronic condition, care managers can intervene proactively. They can coordinate outreach, adjust treatment plans, and connect the member with resources before a $200,000 hospitalization shows up in the claims data. Prevention at that scale was never achievable through manual review.

Price transparency data, now available through federally mandated machine-readable payer files, gives employers another tool to act earlier. AI transforms that raw pricing data into market-by-market comparisons. Employers can identify where a plan is overpaying for specific medical services and direct members toward higher-value providers before care occurs, rather than negotiating discounts after the bill arrives.

The Fiduciary Dimension

Self-insured employers now provide health coverage for more than 160 million Americans. Approximately 67% of insured U.S. workers are covered by self-funded arrangements, and among large employers with 5,000 or more employees, adoption rates reach 95%. These organizations collectively spend hundreds of billions of dollars annually on health plan costs.

Under ERISA and the Consolidated Appropriations Act (CAA), these employers have explicit fiduciary obligations. They must ensure health plan dollars are spent prudently, demand transparency from TPAs, PBMs, and other vendors, and act in the best interest of plan participants. The CAA reinforces this by mandating that TPAs and PBMs disclose detailed claims and pricing information. Elizabeth Mitchell, president and CEO of PBGH, has stated clearly that self-insured employers need to take on a significantly larger role in selecting and managing their health care vendors and partners.

This is a critical point for insurance professionals. Stop-loss carriers, group health underwriters, brokers, and consultants all operate within an ecosystem shaped by employer health plan performance. When waste drives up claims, it drives up stop-loss premiums, reduces margins, and creates volatility that makes risk harder to price. Conversely, employers who actively identify and eliminate waste produce a cleaner, more predictable claims experience. That benefits everyone in their value chain.

What the Numbers Look Like in Practice

When an employer deploys AI-powered claims auditing on a $50 million health plan and identifies a 14% payment inaccuracy rate, typical for most small to mid-size plans, it recovers more than $7 million annually. That money would otherwise flow to vendor margins rather than employee benefits. Organizations using this approach routinely achieve 20% to 30% cost reductions in the first year.

The waste is not distributed randomly. It accumulates in specific, repeating patterns: duplicate charges, inflated facility fees, upcoded procedures, PBM spread pricing, and avoidable usage. These patterns are identifiable, measurable, and correctable with the tools available today.

The Only Remaining Question

The convergence of regulatory requirements, data transparency mandates, and AI-powered analytics gives self-insured employers unprecedented ability to identify and eliminate waste. The tools exist. The fiduciary mandate is clear. Healthcare costs have exceeded 5% in annual increases for three consecutive years, with 2025 projections at 5.8% and 2026 at 6.5%.

Every dollar recovered from waste is a dollar that can fund better benefits, lower premiums, or reinvestment in the workforce. The employers who act on this with data, transparency, accountability, and the right technology will control their costs effectively. They will set the standard for how healthcare should be managed in this country. The rest will keep paying for waste they could have prevented and harboring risk they could have eliminated.

The insurance claims industry sits at an inflection point. Medical records are more complex, nuclear verdicts are rising, and the workforce is changing faster than most organizations can adapt. AI promises to help — but most implementations have fallen short. We sat down with Mark Tainton, senior vice president of data solutions at Wisedocs, to talk about what's actually working, what isn't, and why the industry needs to move from document management to true decision intelligence.

Paul Carroll

The insurance claims industry has been talking about digital transformation for years. What's actually changed in the last 18 to 24 months, and what's still stuck?

Mark Tainton

Having worked in the insurance industry for over 30 years at the intersection of technology and claims operations, I've certainly seen infrastructure change. But the bigger question now is the operating model that can actually leverage that infrastructure. And the operating model is not so much around storing documents in claims management systems or document management systems—it's about how we take advantage of that data asset. We’re essentially moving from document storage into effective decision-making.

Over the last five years, there has been an acceleration in the technology, in particular with large language models. Technology is not the problem.

It's really about taking advantage of the individual pieces of information in the world of unstructured data. That's the next wave we should be focusing on: How do we operationalize the assets so they’re part of the DNA of insurance processes?

Paul Carroll

Medical record review is at the heart of so many claims decisions, yet it still appears remarkably manual at most organizations.

Mark Tainton

I’ve certainly seen large carriers that have introduced AI but haven't introduced the process changes or changed how people can take advantage of the insights as the claim goes through its lifecycle. Carriers are using ineffective decision making approaches that continue to mirror what we saw 10, 15, 20 years ago. 

There needs to be a conversation around how adjusters work, especially because of the change in their age demographic. New people coming into the claims industry consume data completely differently. We have to adjust. 

You have to also understand the psychosocial aspects of the workforce, where COVID accelerated change. You need to cut across multiple claims at any given time and look for triggers that are prevalent by a treatment provider, or at risk indicators that suggest psychosocial issues—they are top of mind for a lot of claims teams right now.

Paul Carroll

There's always a tension between speed and defensibility in claims, especially given the high stakes. How do insurers resolve that tension?

Mark Tainton

Claims are getting more complex, and we've seen a lot of legislation that makes it very clear that if someone's making a decision solely based off AI output with no human in the loop, that's going to be a problem.

When you tie that concern into the expansion of traditional fraud and increases in nuclear verdicts, the defensibility question becomes critical. There needs to be a human in the loop.

Several states are already drawing that line legislatively. California's SB 574 and a growing number of AI governance frameworks now require that AI-assisted decisions in insurance and legal contexts be documented, auditable, and explainable. That is not a future concern; it is a present operating requirement for carriers doing business in those jurisdictions. The organizations that build defensibility infrastructure now will not be scrambling to retrofit it later.

Paul Carroll

There are a lot of solutions out there these days, but they seem to largely be point solutions—summarization tools, triage tools, document processors, and so forth. What's missing from the point solution approach?

Mark Tainton

First, they don't fit into the ecosystems of clients and large carriers. They don't work alongside platforms like Guidewire where they can function as a module and help make those decisions effective.

The point solutions also aren’t really end-to-end. They're focusing on a point in time on a particular claim. That produces what I call a silent failure. The AI processes the document and returns a summary, and the claim moves forward. But the anomaly that should have triggered a flag, the treatment pattern that does not match the diagnosis, the billing inconsistency that signals a problem: None of that surface because the tool was never designed to look across the lifecycle. The claim does not fail loudly. It just quietly travels in the wrong direction for months. 

Think about first notice of injury as a claim goes through the life cycle, and all of a sudden you get a demand package or a treatment package coming in. What are the decisions you want the adjuster to make?

You need intelligence that cuts across the full lifecycle of the claim in terms of other claims with certain characteristics. And I think that's where point solutions really come up short.

Paul Carroll

I assume that thinking is why you took a platform approach with WiseShare.

Mark Tainton

Very much so. We have the sorting and summarization solution that we just renamed WisePrep. It includes WiseChat, where users can save all the insights they generate from a large language model. We've introduced WiseInsights looking at litigation trends, looking at treatment patterns and how they develop, looking across claims that an adjuster who's got a workload of 200 or 300 claims cannot identify on their own. These insights reveal similar characteristics across claims. For example, we looked at one portfolio and identified that a particular treatment provider, over a 12-week program, consistently prescribed a higher and more severe medication at the four-week timeline. 

WiseShare is important, too.  Far too often, a summarized document gets passed from the adjuster to inside counsel, then to external counsel, and eventually to an IME [independent medical examiner]. A lot of the time, we see slip-ups—documents go missing, misinterpretations occur, different versions of the truth emerge. WiseShare brings everything together into one consolidated environment where all of those entities can actually share, review, and export the claim file. 

From a legal defensibility standpoint, that consolidation is not a convenience; it is a chain-of-custody argument. The defense bar needs to see a complete, unbroken record: the medical record chronology, the time series of decisions made, and documented consistency in how AI processed the underlying materials. When a claim ends up in litigation, the question is not just what decision was made; it is whether that decision can be reconstructed, sourced, and defended at deposition. WiseShare is built for that standard.

You have to be able to wrap intelligence around a decision, and that requires a platform. 

Decision intelligence needs to be comparative. You have to be able to see the claim you're dealing with in the context of other claims. The intelligence also needs to be sequential. Are we seeing similar patterns starting to develop on other claims in certain jurisdictions? Are we starting to see certain seasonal trends? Are we starting to see different types of treatment coming through? Finally, the intelligence must provide accountability. Is every inference sourced and every decision point documented? 

The defense bar needs to see that audit trail. They need to see the medical record chronology, the time series, and the consistency in terms of best practices for how AI actually processes documents and insights for better outcomes. From 2023 to 2024, nuclear verdicts rose 52%. Thermonuclear verdicts are up 81%, and overall verdicts are up 116%. 

You need one single environment where you store the materials, one single process that's consistent across an organization.

Bottom line: if you can't show defensibility, you're in a world of trouble.

Paul Carroll

There's discussion about AI replacing many human workers in the insurance industry. What is your perspective?

Mark Tainton 

There's this notion that AI is going to replace people at the desk. From my perspective, that's totally inaccurate. And I think that mindset sets back adoption.

But here's the inflection point: We're dealing with an aging workforce. Insurers and TPAs are struggling to attract talent. Why? Because some of the tools and technology have not evolved as quickly as in other industries. When you can walk hand in hand with AI and the person at the desk and show them all the benefits, that’s exciting. 

Paul Carroll

If you could change one thing about how the insurance industry is currently approaching AI adoption in claims, what would it be?

Mark Tainton

For me, it's what I call the evolution framework. AI is a journey, not a one-time event. Far too often, what I've seen is large organizations—mid-tier, tier two, tier three—treating this as basically an implementation. It's almost like they're going in, turning the light switch on and walking out.

I spend quite a bit of time working with clients all the way from inception to asking: Where are we actually going to implement this? What's the impact we're expecting? How does this align with strategic objectives? What are some of the key measurements we want to see in terms of adoption, change, and, ultimately, having the AI start to hit the hard dollars—reduction in litigation, average duration, and things like that.

I'll give you an example. I worked with a large carrier that wanted to implement AI across the entire organization. But they have an aging demographic in certain lines, and getting them to adopt AI would be difficult. They've also captured a lot of information very poorly in their systems—it's very much in their heads.

I said, Let's focus on the younger generation. They’ll adopt AI, and we’ll create a best practice, one that we can use when we bring in new talent. So we built a three-year program focused on them. Ultimately, the program was so successful that the older generation said, We want to be part of that, too. 

For me, the next window for anyone embarking on an AI journey is to focus on embedding it upfront—knowing, of course, that the process will evolve over time. 

Begin with what we call an EDA—exploratory data analysis—to determine what the baseline is. That way, you can prove that you’re opening and closing claims far more quickly and can see the change quarter over quarter. That data helps sell the journey. We've also done quite a bit of work around what we call data quality programs, where we assess the quality and change behavior at the desk in terms of how people are capturing data—all the way from structured to unstructured and, more importantly, in the adjuster call notes. That program embeds the solution into the fabric of the organization.

I think that's the next wave. 

Paul Carroll

Thanks, Mark.

Sponsored by Wisedocs

About Mark Tainton

Mark Tainton is the SVP of Data Solutions at Wisedocs, bringing over 30 years of AI, data and analytics transformation expertise in insurance and financial services. Having served as Chief Data Officer at multiple leading organizations, Mark understands the critical intersection of medical intelligence, litigation strategy, and claims outcomes. He advises Wisedocs on data and product strategy, go-to-market positioning, and the deployment of AI-powered solutions that address the most pressing challenges facing claims and legal professionals today.

Today's healthcare and insurance organizations manage vast amounts of highly sensitive data across an increasingly complex ecosystem. Clinical providers, insurers, and partners rely on shared data from diverse sources—including electronic medical records, imaging systems, and billing platforms.

However, many lack a clear understanding of where this data resides, who owns it, and how it's accessed. Without this foundation, they cannot confidently classify or protect sensitive information—leaving them vulnerable to compliance violations, regulatory fines, and legal risks. This lack of visibility also affects critical operations. In insurance workflows such as underwriting, decisions may be based on incomplete or inaccurate data, increasing risk—especially in high-stakes scenarios like mergers and acquisitions, where evaluating the data security posture is crucial.

These challenges are intensified by outdated, fragmented environments that are difficult to integrate and modernize. Sensitive data is scattered across disconnected systems and formats, leading to duplication, inconsistency, and reduced visibility. Meanwhile, excessive permissions remain a constant risk, increasing the likelihood of misuse, insider threats, and accidental exposure.

As organizations accelerate AI adoption, including generative AI, to enhance clinical and operational efficiency, they introduce a powerful new capability alongside a significant increase in risk. When sensitive data is used without proper governance and controls, exposure can grow quickly and unpredictably.

Operationalizing data security has long been a challenge. Despite significant investments, many organizations still lack complete visibility. Traditional tools that rely on regex, trainable classifiers, and other pattern-based methods identify only a small portion of sensitive data and often overwhelm teams with false positives.

The good news is that modern data security governance platforms have moved beyond these limitations. Healthcare and insurance organizations should seek solutions that leverage context-aware AI for discovery, risk monitoring, and remediation—delivering outcomes such as:

Gain better visibility into data: To effectively protect sensitive information, providers first need to understand exactly what data they possess, where it is stored, who accesses it, and how it is shared.

Context-aware AI scans each data record thoroughly and can identify not only personally identifiable information (PII), protected health information (PHI), and payment card information (PCI), but also detect other important business records that other tools might overlook. It also recognizes duplicate or near-identical data and determines the category and subcategory for each record. For instance, it differentiates between a HIPAA authorization and a workers' compensation document. This detailed level of information helps security teams make smarter decisions when assigning classification labels, choosing where data should be stored, or setting access and retention rules.

Prevent sensitive data leaks: Security teams must ensure that employees and third-party contractors do not access data they shouldn't and verify that authorized users do not share it. They need a solution that enables them to contextually discover, monitor, and protect their sensitive data—not only at rest but also in transit—to prevent it from being shared with unauthorized users, personal email addresses, file-sharing applications, social media, or GenAI tools.

Enable GenAI without expanding the attack surface: Generative artificial intelligence (GenAI) is reshaping our world in real time. Tools like Microsoft Copilot, ChatGPT, Perplexity, and Claude are changing the way we make decisions, solve problems, create content, and interact both at work and at home. While they offer greater operational efficiency, better decision-making, and lower costs, they also introduce significant data security risks for insurers.

Providers need a solution that helps them detect when employees use unsanctioned or "shadow AI," so they can maintain control and protect their data. They also need to ensure that, no matter where data is stored, it is accessed by the correct identities, at the appropriate times, and for the intended purposes. A comprehensive data security and governance solution will allow them to set guardrails on which data should be blocked or redacted by groups and for each GenAI application, and help them curate data when training their own proprietary GenAI models.

Excel in regulatory compliance audits: Regulatory frameworks help healthcare and insurance companies reduce risks, implement processes, and maintain customer trust. However, mapping security controls to these frameworks can quickly become overwhelming. An additional challenge is that different regions may have vastly different data handling and classification requirements.

Organizations need a clear overview of their compliance status, tools to resolve issues, and peace of mind that they aren't one audit away from disaster. They should seek a solution that offers a dashboard displaying their current compliance status across all relevant regulations and security controls, as well as support for custom frameworks. Additionally, they require granular visibility into all data records that violate compliance, with the ability to remediate them directly within the platform.

Improve the effectiveness of current security tools: Tools like zero trust network access (ZTNA) and cloud access security broker (CASB) don't analyze data to determine whether to allow or block access. Instead, they enforce policies based on labels, so if those labels are wrong or missing, they could either expose sensitive information to unauthorized users or prevent access necessary for productivity. Context-aware AI and autonomous classification help ensure that sensitive data is labeled correctly and remains accessible only to authorized individuals.

Experience faster ROI, smarter policies, and less stress: Context-aware AI significantly accelerates the data discovery process and saves countless hours that administrators used to spend on tuning algorithms and chasing false positives. However, since new data is constantly generated and continually changing, capturing only a snapshot at a single point in time is insufficient.

Security teams can save time and enhance data protection by implementing a solution that continuously monitors data, flags risks, and automates remediation steps. Picking a provider that offers managed services can also reduce the workload on overstretched security teams by providing data security experts to assist with tasks such as deploying the platform and training their teams on it, building a data governance roadmap, mapping classification labels, and reporting on and tracking progress toward their goals.