The integration of AI into insurance is not just changing operational workflows—it's redefining leadership. Where control and standardization once reigned, today's leaders must orchestrate autonomy, resilience, and trust. True strategic advantage lies not in overseeing every process but in empowering others to act wisely without waiting for approval.

Leadership in the digital era is no longer about issuing instructions or reviewing every decision. The role is shifting toward:

• Creating a culture of responsible autonomy;
• Defining clear boundaries for human and algorithmic decisions;
• Supporting decentralized teams with tools and purpose.

AI doesn't eliminate leadership—it demands a better version of it.

A Test of Principle: Ukraine's Insurance Sector Under Fire

At Arsenal Insurance in Ukraine, we've long practiced a distributed decision-making model. That model was stress-tested under extreme conditions—wartime disruption, evacuations, infrastructure breakdowns.

Yet our teams kept operating with speed, empathy, and alignment. Why? Because the architecture of trust was already there. We didn't need to "respond"—we simply continued leading the way we always had.

No panic, no paralysis. Just empowered professionals acting in real time, guided by clarity and mutual confidence.

What Global Executives Can Learn

Many leaders are waiting for full AI adoption to rethink their operating models. But the mindset shift must come first:

• Trust must precede automation. Technology can scale good decisions—but it can't replace poor leadership.
• Transparency in decision boundaries builds accountability.
• Speed and quality improve when people feel ownership, not when they're micromanaged.

The lesson: AI is not the endgame—it's the accelerant. And leadership is the fuse.

In a world flooded with new tools, the most underrated competitive advantage might still be human. Not just human capital—but human judgment, multiplied by systems that support it.

Let's not just build smarter systems. Let's lead in smarter ways.

One of the most difficult decisions for companies seeking cyber insurance is whether to rely on a longstanding, trusted vendor or defer to the insurer's panel of pre-approved incident response providers.

Let's say a company has worked with the same local IT service provider for years. They have a strong relationship, favorable pricing, and the added benefit of fast in-person support when needed. Most importantly, the provider knows the company's systems inside and out. In the context of cybersecurity, that familiarity can be a real asset.

Last year, when a ransomware threat hit a client's network, the IT team noticed abnormal file behavior within minutes, with files duplicating and renaming themselves on a shared drive. Because they knew the company's architecture, they didn't waste time scanning the entire network. They knew which user accounts had access to that drive, which departments had mapped it locally, and which machines had been prone to phishing clicks in the past. Within 15 minutes, they traced the activity to a single compromised workstation in accounting, pulled it from the network, and blocked the affected credentials, stopping the spread before encryption even began.

After the event, the company's relationship with its local IT provider only deepened. This dynamic isn't unusual, especially among smaller businesses. It's almost a trope: the IT guy who becomes part of the family. Shared history, trust forged under pressure, even overlapping personal lives, kids in the same school, weekends at the same barbeques. So when an insurer pushes for replacement with a faceless, pre-approved alternative, it's not just a business decision. It can feel like a personal betrayal.

Yet when it comes time to secure cyber insurance, the company faces a harsh reality: Keeping their trusted IT provider could mean a significantly higher premium, a tough pill to swallow in a margin-sensitive environment. But replacing them doesn't just come with personal guilt. It means onboarding an unfamiliar team, starting from scratch, and risking misalignment across the broader vendor stack.

Friction or miscommunication between vendors isn't just a logistical headache, it's a security risk. Every vendor represents a potential attack vector, and many fall outside the scope of cyber insurance coverage. If one of them is compromised, the threat can propagate upstream. So, from both a business continuity and risk management perspective, sticking with the local IT provider may seem like the safest option. But if they're not on the insurer's list, what then?

Insurers Don't Know the Ins and Outs of Your Stack—You Have to Show Them

Cyber insurers are often more flexible than companies assume, but only if they're convinced that the non-listed vendor doesn't introduce undue risk. Demonstrating that the provider is a strong fit also demonstrates that they're a safe bet, which benefits both the insured and the insurer. The key is full transparency.

Communicate and emphasize your preferred vendor's strong security controls, relevant certifications, incident response experience, and track record with your organization. If possible, bring in a third party to validate your assessment and avoid the appearance of bias. Done right, this can lead to negotiated savings or at least help justify the added cost internally.

The struggle to get non-approved vendors accepted by insurers points to a deeper issue: Underwriters often don't fully understand your organization's specific risk profile. Their approach is blunt, built on rough estimates, static questionnaires, automated scoring tools, and informal backchannels. Cybercrime is fluid and volatile, but underwriters don't have the time or expertise to stay current, nor to understand how each security tool actually mitigates those evolving threats. A common remedy for this disconnect is bringing in a cybersecurity expert who can act as a translator.

Tangled Web of Vendors

For companies pushed to justify a trusted vendor to their insurer, the process can also clarify the bigger picture. It forces a closer look at third-party risk, that is, untangling the vendor ecosystem, figuring out which relationships are worth protecting and which are safer to replace with insurer-approved alternatives.

This is more pressing now than ever, because the number of vendors organizations manage today has grown exponentially. Executives who came up during the dot-com era often feel disoriented. What once involved an IT guy and a local internet provider has morphed into an ecosystem of hundreds, spanning cloud services, cybersecurity platforms, managed providers, and niche SaaS tools.

It's not just the number of vendors that's growing, it's their complexity. The risk isn't additive, it's multiplicative. Each new vendor connects to others, creating a web of dependencies that rarely map cleanly back to the organization. These threads tangle. And that tangle isn't just a logistical headache, it expands the attack surface, introducing vulnerabilities with every integration.

Cyberattacks are escalating at an alarming rate. In Q3 2024, companies experienced an average of 1,876 cyberattacks per organization each week—a 75% increase compared with the same period in 2023. One compromised vendor can trigger a cascading breach across an entire organization.

The 2022 Uber case set a clear precedent: CISOs no longer walk away unscathed. They now face personal liability. But that doesn't mean the CISO can simply absorb the blame and serve as a scapegoat. Regulatory agencies, including the SEC, FTC, and GDPR enforcers, investigate the entire chain of failure: governance, board oversight, breach response protocols, and internal communication.

When an organization is found liable after a breach, regulators armed with new disclosure rules and data privacy laws can impose crippling fines. The reputational damage can linger for years. This isn't the age of "these things just happen" any more. If it does happen, you're accountable—and potentially ruined.

Cyber Insurance Isn't a Safety Net—It's a Last Line of Defense

With vendor ecosystems growing more entwined and the consequences of failure more severe, many executives turn to cyber insurance as a form of comfort. The often unspoken thinking is: You'll make this go away, right? They treat the policy like a safety net and sign the check. But while cyber insurance can be essential, it's reactive by nature. It covers the fallout. It doesn't stop the breach. Preventing incidents in the first place means knowing which threads are frayed, which are tangled, and which vendors pose real risk.

That requires assessing vendor exposure early, i.e., before and during the procurement of insurance. Whether done in-house or through a trusted partner, due diligence leads to stronger vendor cohesion, lower premiums, and greater negotiating power with insurers. Companies shouldn't be forced to blindly accept pre-approved vendors that don't fit their operating model. When organizations take the time to understand their vendor landscape, insurers can better understand what makes the business distinct and work to preserve that uniqueness rather than overwrite it.

Stratify, Verify, Clarify

So where do executives looking to assess their vendor risk start? They should begin by stratifying their vendors, organizing them by tiers of risk. Not all vendors pose the same level of exposure. Technically, a landscaper is a vendor—but would fall into Tier 4. A website analytics provider might sit in Tier 3, cloud-based HR software in Tier 2, and an IT managed service provider (ITMSP) firmly in Tier 1.

But just as damaging is placing too much trust in certifications like ISO 27001 or SOC 2. These should serve as a baseline, not a seal of approval. The same logic should be applied to external scoring tools. A vendor might have an "A" rating online, but that could simply reflect a clean external footprint, not the quality of their internal controls, policies, or staff readiness.

Many organizations rushing to sign with vendors often overlook weak or missing contractual language around data security. The agreement shouldn't just say the vendor "follows best practices." It should clearly define which frameworks are being followed, what controls are in place, and how often they're reviewed. Similarly, contracts should spell out concrete security obligations such as encryption standards, access controls and audit rights, as well as specific incident response expectations, including response time commitments and breach notification timelines.

When vetting, don't just assess the vendor. Understand how deeply they integrate with your systems and how they intersect with other vendors. That's where hidden risk lives.

Stress-Test Your Vendors

Even after vendors are properly vetted, untangled, and justified (to both the insurer and the organization,) your work isn't done. Further steps are still required.

Run tabletop exercises: These are simulated incident response drills involving all key internal and external stakeholders. Clarify roles in advance of these sessions, build trust between vendors and internal teams, and reduce confusion when real incidents hit.

For example, an organization can simulate a ransomware attack that encrypts customer data across multiple cloud environments, forcing its ITMSP, breach counsel, and DFIR firm to coordinate under a tight 24-hour window. In doing so, it might uncover gaps in cross-vendor communication or delays in restoring critical systems, issues far easier to fix in a drill than in a live breach.

Tabletop exercises are just the first step toward what should be every organization's ultimate goal: continuous, real-time threat monitoring.

Hydrogen will play a crucial role in driving the green transition, with demand expected to surge in the coming decades. Around 60 governments have adopted hydrogen strategies, while the number of planned projects is already exceeding 1,500 globally compared with around 200 in 2021 – an increase of around 600%. To realize these projects, a total investment volume of $680 billion until 2030 may be needed, according to the Hydrogen Council and McKinsey, which will trigger a greatly increased demand for insurance to protect against risks as this investment is activated. Europe is leading the way by far, with 617 planned projects and the highest total investment announced, at $199 billion.

While the potential of hydrogen is undoubted, there are still challenges and headwinds to overcome. The potential size and scope of the hydrogen economy will depend on a range of factors, including the evolving political, trade, and economic environment, as well as demand. Policymakers and regulators need to address costs for the development of the infrastructure, so scaling up at a level competitive with other energy sources is possible.

Across all industries, stringent safety measures will be vital to manage hydrogen's inherent risks. This is where the insurance industry comes into play. According to Allianz Commercial's latest risk, "Hydrogen: Opportunities, Uses and Risks in the Energy Transition," as hydrogen becomes integrated into the global economy, insurers can expect to see a significant increase in demand for coverage,. Allianz Commercial expects the insurance market for hydrogen project coverage to grow to over $3 billion in premiums by 2030.

Hydrogen offers great potential but also challenges and risks

While it holds much promise and has been used in the chemical and refinery sectors for many decades, hydrogen's integration into other industries brings a range of challenges, with risks such as fire, explosion and embrittlement being already well-known. Energy production facilities will involve hydrogen storage and high-temperature combustion, which can lead to leaks and explosions. Currently planned mega projects require a scale-up of risk management. 

In transport, applications like hydrogen fuel cell vehicles will also face risks of hydrogen embrittlement and leaks. Port operators, bunkering facilities and fuel handlers will need to manage highly flammable and cryogenic hydrogen fuels, bringing accident and contamination risks.

Risk management and mitigation are crucial for hydrogen projects

Given hydrogen's unique properties and high combustibility, ensuring safety throughout the value chain is crucial. Analysis of hydrogen-related incidents shows that undetected leaks can easily lead to explosions. Equipment design, maintenance and training can help prevent the escape of flammable hydrogen gas. The risks of ignition can also be reduced by locating hydrogen facilities in the open. Embrittlement risks can be managed using hydrogen-compatible materials and specifically designed resistant coatings.

In addition to preventing incidents, organizations can take steps to limit the extent of property damage, business interruption, and third-party liability. Buildings and facilities should be designed and constructed to withstand natural hazards, fire and explosion, and limit damage to adjacent property and equipment. Robust hydrogen leak detection and isolation systems are paramount. Human error is also a common factor in large losses. Operational, safety, emergency procedures, and training should be frequently updated, including having robust and well-rehearsed plans in place for accidental releases.

Given the wide reach of the hydrogen value chain and its potential uses, the implications for insurance could be far-reaching, touching on multiple sectors and lines of business over the next decade. From an exposure and potential claims perspective, product lines such as energy, natural resources and liability are likely to see the biggest impact from hydrogen risks over the next five to 10 years, followed by property and marine.

To read the full Allianz Risk report on hydrogen, please visit: Hydrogen: opportunities, uses and risks in the energy transition | Allianz Commercial.

Paul Carroll

What are the key opportunities for operational efficiency in the insurance industry?

Olga Vinichuk

Automation opens up immense opportunities for insurance efficiency gains. But it’s not about throwing technology, well, everywhere. The biggest potential lies where inefficiency is curbing the insurer’s profits and hurting customer experience the most.

From what I’ve seen across ScienceSoft’s clients in insurance, three areas really stand out: claims processing, underwriting, and customer servicing. Claims is where delays are most visible to the customer and where inefficiencies quickly add up in payer profitability leakage. Prolonged and gapped claim fraud detection alone costs the sector more than $300 billion annually. Automating parts of the claims lifecycle, especially around document intake, evidence validation, and decision-making, has shown huge potential. That can start with rule-based automation but becomes really powerful when paired with artificial intelligence (AI).

Underwriting is another big one. The process is often slow and heavily dependent on human reviews and judgment calls. McKinsey estimates that around 40% of underwriter work time is locked in low-value manual routines like re-keying data and profiling risks. Software to handle lower-risk, rules-based underwriting tasks has been here for decades. With machine-learning-based analytics and generative AI, we now have the tools to automate data processing entirely and assist underwriters in risk scoring. This speeds things up and boosts risk profiling precision.

Customer servicing — everything from inquiry handling to reminders — is still manual at many insurers. This hinders both efficiency and customer experience: The level of customer satisfaction with digital servicing in insurance is the lowest among all finance domains. From my experience, automating those tasks cuts operating overhead, boosts customer satisfaction, and allows servicing teams to focus on complex relationship-building work, which is where they generate the biggest value.

Paul Carroll

As we go deeper into specifics, let’s start with underwriting.

Olga Vinichuk

Underwriting is the area where the impact of automation is especially tangible in terms of speed and capacity. Risk evaluation and quoting are the primary candidates for transformation as they drive the biggest efficiency gains. In some segments, software is now helping insurers underwrite up to 80% faster and handle 40% bigger volumes of tasks with the same headcount.

A few years ago, I worked as a consultant for an aviation insurance automation project where the client's underwriting process was completely manual. I was on-site at their office, and they were literally writing out quotes on paper, stamping documents, and walking them across the office. Their risk rating algorithms lived in Excel and required manual input and calculation every time. It was incredibly time-consuming. The worst thing is that the manual process led to inconsistent underwriter decisions and limited scalability.

We helped the carrier move that entire process into a digital environment. ScienceSoft’s engineers built a custom system where underwriters can now input data about the insured object, say a cargo or a fleet, and relevant parameters like usage type and coverage needs. The software calculates premiums using the same scoring logic, now running automatically. As some of the aviation lines assumed tailored arrangements and region-specific regulatory constraints, we left room for underwriters to check algorithmic outputs and apply human judgment.

The solution allowed the carrier to write more, produce quotes faster, and free underwriters to focus on complex risks and negotiations. I can’t disclose the impact for that particular project, but it’s more or less aligned with our benchmark findings: a twofold increase in underwriters’ productivity, quoting in minutes, and 10%+ operational cost savings.

Paul Carroll

How does AI fit in?

Olga Vinichuk

AI is the natural next layer of value once rule-based automation is in place. From my experience, the most impactful applications of AI in underwriting are automated risk data processing and intelligent decision-making.

Intelligent decision-making solutions can auto-process applicant data, profile risks, and produce personalized quotes in just a few seconds. For insurance lines that mainly deal with standard, low-risk cases, smart engines can underwrite over 90% of applications outright. This opens vast opportunities for freeing up underwriter capacity.

Fully automated risk profiling became possible with the rise of large language models (LLMs). LLM-powered tools can extract risk insights from heterogeneous documents and summarize them into writing-ready files within minutes, highlighting what’s relevant and flagging gaps. That’s especially helpful in commercial and specialty lines where submissions are bulky and unstructured. Early adopters of such tools report a 2x increase in underwriter productivity and 4x faster time to quote.

With LLMs, you get not just automated risk data processing but full-scale intelligent assistance. For instance, underwriters can ask an LLM copilot to evaluate the conditions of the insured object, map risks, and interpret their implications for coverage. The tool immediately provides relevant responses, freeing the team from tedious data search and analysis. Global risk technology leaders like Verisk already have LLMs in their stack, so you can leverage the capability with a familiar platform.

Paul Carroll

What are the major opportunities for efficiency you've identified in the claims process?

Olga Vinichuk

One major opportunity lies at the claim input and processing stage. Many insurers have already implemented self-service portals and mobile apps for insureds to submit claims and evidence digitally. ScienceSoft’s clients who did that achieved a nearly 50% reduction in employee workload and 5x faster claim processing. 

What’s driving the next wave of efficiency is the integration of intelligent technologies. Leveraging AI-fueled image analysis, natural language processing, and machine learning, you can automatically parse any sort of evidence, validate the data, classify severity, route the claim to the right handler, and in some cases, even settle claims outright without any human touch. From my experience, such intelligent automation helps establish a 5–10x faster claim cycle and boost adjuster productivity by around 30%.

Paul Carroll

I think there’s a lot of potential with fraud detection, if only because an AI can process a much higher volume of data than humans can.

Olga Vinichuk

Absolutely — the volume and complexity of insurance fraud make AI essential. We’re talking about billions lost annually. What really stuck with me was something I recently heard from an insurer during a project discussion: They said they were losing trust in their provider network and had to enforce stricter controls to avoid fraud leakage. That really brings home the operational impact of fraud, beyond just financial losses.

AI tools automate two critical tasks. First, they can analyze vast volumes of historical claims data to detect, say, unbundling, upcoding, or duplicate billing. These used to be handled by rule-based engines, but fraudsters adapt fast, so AI now plays a role in identifying anomalies and subtle correlations that would be hard for a human to spot in time.

The second is evidence fraud detection. For example, ScienceSoft recently helped an insurtech startup create a solution that automatically recognizes forged dental X-rays submitted by providers to justify inflated claims. We built computer vision and machine learning models to detect manipulated images. Our algorithms were trained to spot visual gaps that indicate alteration and mismatches between image content and patient data that hint at reuse. The models validate in minutes and flag nuances that seem plausible to human reviewers, all in a HIPAA-compliant manner.

Perhaps the best thing about AI-powered fraud detection is its scale. Insurers can check 100% of claims instead of just a sample without a heavy lift of manual review. This increases efficiency and dramatically reduces fraud-associated losses. And as fraud schemes increasingly become AI-supported, it’s only logical that AI becomes the tool we can’t avoid to fight back.

Paul Carroll

What opportunities for efficiency are you observing in customer service?

Olga Vinichuk

I see one of the most compelling efficiency opportunities in agentic automation. With today’s advanced large language models and cloud-based orchestration tools, insurers can deploy AI-powered agents that autonomously handle over 70% of routine service interactions, like policy lookups, claim status checks, and document submissions, without human intervention. Unlike the rule-based bots of the past, these agents deliver a smooth, conversational experience that rivals human service quality. You get your human agents freed for complex tasks and high-emotion claim cases where a real conversation is needed.

Speech-to-speech assistants are maturing, and they are especially helpful for older and less tech-savvy insureds who prefer to talk rather than use a screen. The R&D team at ScienceSoft has recently introduced an AI voice agent that can hold live calls with customers and automatically book appointments. Based on our estimates, the agent can speed the process by 40%, cut call abandonment rates by 30%, and lower operational costs by at least 50%. As it can handle multiple calls simultaneously, it can process 70% more calls per hour than a human service rep — a huge win from the efficiency standpoint.

Paul Carroll

Any final thoughts?

Olga Vinichuk

Whether rule-based or AI-driven, automation is a strategic lever for insurance efficiency, especially when deployed iteratively. Even with modest budgets, I’ve seen insurers achieve fast wins using basic tools for high-friction workflows like first notice of loss (FNOL) intake or quote generation. These early gains often justify broader investment in intelligent automation, like AI for risk scoring, fraud detection, or customer service. Once automation starts delivering measurable efficiency or improvements in insured experience, momentum builds. It’s not about a big-bang transformation — it’s about creating a scalable path from manual to intelligent operations.

Paul Carroll

Right. You can get a virtuous circle started. You spend a bit on automation, which frees up funds, which lets you invest more in automation, which… and so on.  

Thanks, Olga.

About Olga Vinichuk

Business Analyst and Insurance IT Consultant

Olga has built a vibrant career at ScienceSoft as a business analyst and insurance IT consultant. She participated in ScienceSoft’s 11 major insurance projects, guiding 8 of them as a leading business analyst. As an insurance IT consultant, Olga shapes the unique solutions that digitally transform underwriting, claim settlement, policy management, and compliance monitoring workflows. Olga is also involved in ScienceSoft’s outsourced product development projects, where she helps SaaS insurance companies turn their high-level product concepts to fully-functional solutions.

Artificial intelligence isn't settling for a foot in the door in 2025 – it wants to bust doors down for full commercial use, particularly with insurance companies.

A recent study by Digital Insurance shows 78% of insurance leaders saying they're expanding their technology budgets in 2025, and 36% of survey respondents said the bulk of their IT budgets are going to AI. That makes AI the highest IT priority among insurers, well ahead of top-tier issues like big data and analytics, cloud computing, and digital infrastructure.

"AI is now central to how insurers operate, affecting underwriting, claims, customer experience, and fraud detection," said Guy Gresham, a global capital markets board advisor and former director of investor relations at BNY.

Most insurers are in the speculative and development stage with AI, the DI study reports, but in specific insurance sectors like healthcare, "are already in full AI production."

So, what does the AI-led insurance innovation landscape look like in mid-2025? Here's an inside look.

How AI Is Changing the Insurance Landscape.

In mid-2025, the U.S. insurance sector is seeing AI fuel the rise of continuous underwriting, where pricing and risk exposure adjust dynamically rather than on an annual cycle.

"At the same time, intelligent agents are transforming service delivery by handling claims and customer interactions with increasing sophistication," Gresham said. "For insurers, this marks a shift from being product providers to becoming real-time risk managers. For investors, the value lies in firms that can embed AI across functions to drive efficiency, resilience, and scalable insights."

With those factors in play, major insurance companies have wasted no time in leveraging AI.

Allstate, for example, is helping agents prepare claim emails, while State Farm is deploying the technology to swiftly gather data on customers for improved outreach and is using natural language models to speed the contract analysis process.

Other insurance professionals say they're quickly getting comfortable with AI in the workplace.

"We're using AI to streamline underwriting, flag fraud faster and offer customer service that doesn't include canned elevator music," said John Espenschied, agency principal at Insurance Brokers Group, an independent agency.

Espenschied said predictive analytics is the "new" gut instinct. "AI can look at more variables than any underwriter, making quote accuracy better, pricing fairer (most of the time), and claim turnaround faster," he said. "Claims bots are getting so good they might start asking how your weekend was before denying coverage."

Is AI affecting policies and pricing?

AI is also helping insurers clarify risk and set up optimized price points.

"Live telemetry gives insurers a clearer view of actual risk, not theoretical posture," Espenschied noted. "That clarity lets good actors get better pricing. It also helps advisors push for coverage that aligns with what clients are actually doing, not just what they say on an application."

On the production side, insurers are using AI to develop new guidelines for operational risk assessment, which in turn affects pricing.

"Insurers today typically rely on broad data sets like historical loss data, demographic information, and general weather reports to assess risk," said Stan Smith, CEO and founder of Gradient AI, a fintech insurance company. "In the future, we will see AI models that incorporate more specialized data sources to improve the accuracy of risk assessments.

Cryptocurrencies and blockchain are being deployed.

Blockchain is no longer a peripheral experiment for insurers. Now, it's evolving into a core infrastructure technology with broad applications across the insurance value chain. From underwriting and claims to reinsurance and regulatory compliance, blockchain delivers transparency, automation, and data integrity on a large scale.

Take AXA, the France-based insurance and investment firm that's been challenged by a common roadblock in the insurance sector: lengthy and delay-prone claims processes related to airline flight delays. In response, AXA created Fizzy, a blockchain-based parametric insurance that delivers automated payouts to travelers on flights delayed by two hours, with no need for the traveler to file a claim.

"Blockchain in insurance has promise," Espenschied said. "Smart contracts are the big draw, especially automatic payouts when conditions are met," like weather parametrics.

There's also a growing industry concern that over the next 15 years, insurance may shift in part to a tokenized system where risk pools are moved and manipulated.

"That's where insurance coverage is tied to how many tokens within that pool have been staked," said Ted Patestos, CEO and founder of Tiger Adjusters. "Nexus Mutual is one example of a company playing in the DeFi insurance space."

Experts also note that alternative finance technologies have ample room for expansion and market share growth. "Blockchain for the first time has joined forces with the reinsurance sector to bring sufficient liquidity to the asset class, and a whole new avenue for alternative capital to enter the industry," said Ted Georgas, co-founder and chief technology officer at OnRe, a blockchain insurance company.

Backers argue that the tokenization approach democratizes access to insurance investments, accelerates capital inflows, and enhances product liquidity, particularly in catastrophe risk markets.

Fintech companies, such as Chainlink Labs, which developd the Chainlink network, a decentralized oracle network connecting blockchains to real-world data and off-chain systems, are collaborating with insurers to integrate real-world data feeds into decentralized insurance products, particularly in emerging markets.

"Regulatory risk is the biggest roadblock for insurance shifting to blockchain-based automated solutions, followed closely by the 'what if this token is worth eight cents tomorrow?' factor," Espenschied said.

What will AI-powered insurance look like in 10 years?

Insurance industry specialists anticipate significant technological shifts that will transform the insurance sector over the next decade.

"Risk will be priced in real time," Knuth said. "Protection will be embedded, not bolted on. And advisors will act more like financial strategists, guiding clients through risk exposure with dashboards, not paper apps."

More automation, however, may lead to more industry upheaval.

"We'll see deeper exclusions in policies, less human interaction, longer wait times to see true resolution or indemnification on claims," Patestos said. "We'll also see more 'Band-Aid models' from smaller insurance companies attempting to capitalize on the vacuum in the market from larger carriers with a lower risk appetite pulling out of certain markets."

Mergers and acquisitions (M&A) are occurring at a breakneck pace, with firms rushing to expand their footprint, unlock synergies, and gain a competitive edge. But success in M&A isn't measured by the number of deals closed or the splash they make — it's measured by what happens next.

The real work begins after the ink is dry. That's when teams face the hard part: integrating systems, aligning workflows, unifying cultures, and preserving the client experience. Too often, deals that look great on paper unravel in practice. Studies show that between 70% and 90% of M&A transactions fail to deliver the intended results, often due to poor post-merger integration.

Without a strong post-merger strategy, even the most promising acquisitions can lead to fragmentation, friction, and lost momentum. The deal isn't the win, it's just the starting line.

It's Not the Deal, It's the Alignment

Every acquirer comes with a playbook, and once the deal is signed, they'll expect new agencies to fall in line. That's not inherently a bad thing. But if you're not aligned on platform strategy, technology priorities, or cultural values, what seemed like a great opportunity can quickly turn into friction.

Misalignment on integration plans is one of the top reasons deals underperform or fail altogether. What begins as a growth story can end in stalled momentum, lost clients, and teams that feel more burdened than empowered.

Why Integration Stalls

Many agencies underestimate how hard integration really is. Even with the best intentions, focus can drift, priorities conflict, and progress stalls. The most successful integrations start with one thing: a clear, top-down definition of success. From there, leaders must set an adaptable path because mergers are rarely linear. Agility is key, but so is fortitude. You must finish what you start.

This is especially true when it comes to systems. Technology integration is one of the top two reasons M&A fails, followed by cultural misalignment. Yet most agencies still treat technology as an afterthought rather than a foundational pillar.

The Secret to Seamless Integration

Insurance operations are already complex, layered with regulatory nuance, product variability, and service obligations that differ from state to state. When two or more agencies merge, that complexity doesn't just double, it compounds. Disparate workflows, competing tech stacks, and varying definitions of "how things get done" can create a chaotic operating environment that bleeds time, trust, and profitability.

That's where standardization becomes a silent force multiplier. It isn't just about documentation or process control, it's about creating clarity across every layer of the business. Standardized processes reduce risk, drive consistency, and enable more confident decision-making at every level of the organization. When everyone is rowing in the same direction with the same tools, agencies can scale, not with chaos, but with confidence.

Tech as a Unifier, Not a Barrier

Before signing on the dotted line, agencies need to do more than analyze financials, they need to scrutinize their technology infrastructure. What systems are in place? Where do they overlap? What will be sunset, and what will remain mission-critical? Without clarity on these questions, integration efforts can spiral into costly delays, fragmented data, and staff confusion. It's like building a house on two different foundations — eventually, the cracks will show.

Technology should be viewed not as a post-merger clean-up task, but as a strategic enabler from day one. The most successful agency combinations prioritize tech alignment before the merger, ensuring shared systems can support everything from CRM and commissions to compliance and performance data. When technology is unified, it accelerates operational consistency, improves speed to value, and strengthens the client experience.

Trust Is the True Currency

In times of transition, transparency and delivery are everything. While leadership teams are focused on internal alignment, systems integration, and operational change, clients are watching for one thing, consistency. They don't care about your new logo, your press release, or how many agencies were acquired. They care that their questions get answered, their policies stay intact, and their experience doesn't suffer.

That's why transparency and follow-through are vital during a merger. Clear communication goes a long way in preserving loyalty. But communication alone isn't enough. What cements trust is execution. When you say you'll deliver (and you really do), your credibility strengthens at every touchpoint.

Positioning for What's Next

Consolidation isn't slowing down, and neither is innovation. New entrants will continue reshaping the landscape, bringing fresh solutions to long-standing inefficiencies.

The future belongs to agencies that align early, standardize boldly, invest in the right technology, and never lose sight of the trust that keeps clients coming back.

Lemonade, a digital-first insurance company known for selling renters, pet, and term life coverage, reimagined the entire insurance experience as a productized user experience (UX) flow. Want renters insurance? 90 seconds. Need to file a claim? Done in three. No agent. No paperwork. No "please hold."

Where legacy brands treat digital as a convenience layer, Lemonade treats it as the core product. From chatbot onboarding (hello, Maya) to claims resolution (thanks, Jim), every step is built for clarity. Speed and simplicity here are differentiators. Lemonade claims its sign-up takes just 90 seconds, with claims processed in as little as three minutes. And when 87% of your customers are buying insurance for the first time, that's growth through innovation.

The Psychographics That Matter Now

Lemonade is creating demand, not just siphoning customers from State Farm. Their core audience is first-time buyers who are:

• Startup-minded
• Values-driven
• Subscribed to TED and Coinbase
• Fluent in fintech, allergic to friction

RightMetric's research found three dominant audience clusters: Future Finance Heads, Modern Money Minds, and Start-up Realists. These are people who treat insurance as a smart tool and expect it to behave like one. This group lives in NYC, SF, and Reddit. They don't want brands that "understand Gen Z." They want brands that think like them.

Why Absurdity Outperforms Authority

Lemonade's tone is part pink soda, part TED Talk, and part meme. One of their blog posts is literally titled 'We Suck, Sometimes.' Their TikToks feature child CEOs answering red desk phones. Their ad copy says things like "insurance for your skincare shelf."

The approach shouldn't work. But it does because the absurdity is for laughs and for strategy. Lemonade builds emotional resonance through tone that disarms. Humor is the differentiator in a category drenched in fear and formalwear.

The TikTok Gap: What They're Underinvesting In

How Lemonade Stacks Up: Side-by-Side Estimates with State Farm

Paid media estimates reflect tracked digital ad placements across major platforms from May 2024 to April 2025. Figures are based on third-party intelligence and may not capture all direct or offline spending. Raw video view data for YouTube, Facebook, and TikTok is sourced from Tubular Labs. Instagram video views and analysis are based on proprietary analysis using third-party ad intelligence tools.

While State Farm enjoys broader scale, Lemonade maximizes creative resonance per dollar, driving nearly 800 million impressions through highly targeted, tone-driven campaigns. The opportunity is in closing the platform gap, especially on TikTok and with influencers.

For all of Lemonade's cultural fluency, there's one platform where the disruptor isn't...disrupting: TikTok. Despite having the tone, audience, and aesthetic for virality, Lemonade's presence here is small. Really small.

Compared with legacy brands like State Farm, which generated over 2.7 billion impressions on just one campaign, Lemonade's presence is modest, despite having top-performing campaigns that drove nearly 800 million impressions on just $11 million in spending. Their TikTok presence and influencer engagement remain limited. That's an opportunity.

For brands competing in the same space: TikTok is wide open. Influencer partnerships, testimonials, and remixable user-generated content (UGC) are all growth levers just waiting to be pulled.

What to Steal From the Legacy Brand Playbook and What to Stop 

Here's what legacy brands can and should steal from Lemonade:

• Design for speed: If your quote flow takes longer than a Chipotle order, it's too long.
• Lead with values: Lemonade's Giveback program is nice and sticky.
• Talk like a human: Enough with "trusted coverage for your family's future." Try something like Lemonade's approach: "insurance for your skincare shelf" or "renters insurance for your gaming rig."

And what to stop?

• Stop mistaking legacy for loyalty.
• Stop hiding behind legalese.
• Stop thinking people will care just because it's important.

In an industry built on risk, Lemonade proved the real risk is sounding like everyone else.

Footnote: All campaign data referenced, including spend, impressions, and CPM, was sourced from RightMetric's proprietary teardown of Lemonade's digital marketing strategy. Data was collected between April 2024 and May 2025 using tools including Tubular Labs, AdClarity, and additional third-party analytics platforms. Figures are modeled and directional

In 2025, artificial intelligence (AI) is not just reshaping consumer interactions—it's fundamentally reinventing how insurers assess, predict, and manage risks. With AI risk detection tools becoming smarter and faster, the once-static process of underwriting and risk profiling has evolved into a real-time, dynamic system.

In this article, we'll explore how AI detection is changing risk assessments, the technologies driving the change, and what it means for insurers, policyholders, and the entire financial ecosystem.

From Traditional Methods to AI-Driven Solutions

For decades, risk assessments in insurance relied heavily on historical data, manual reviews, and human judgment. While effective to an extent, this approach was time-consuming, prone to error, and often generalized, ignoring individual nuances. This model is rapidly becoming obsolete.

In its place, AI-driven risk detection offers a data-driven, real-time, and hyper-personalized alternative that promises better accuracy, lower costs, and enhanced customer trust. AI detection leverages a combination of machine learning algorithms, natural language processing (NLP), and big data analytics to assess risk more accurately than ever before. Here's how it functions in modern insurance:

• Data Ingestion: AI systems pull structured and unstructured data from various sources such as sensors, social media, medical reports, and transaction records.
• Pattern Recognition: Algorithms analyze this data to identify hidden patterns, anomalies, and correlations.
• Risk Scoring: AI tools generate real-time risk scores, offering detailed insights that can trigger automated policy decisions or human review.
• Continuous Learning: AI systems learn over time, continuously refining their accuracy and recommendations.

Key Technologies Transforming Risk Assessment

Predictive analytics engines use historical claims and behavioral data to forecast future risks and loss probabilities with uncanny precision. Computer vision in claims and underwriting allows AI models to analyze photos and videos to assess property or vehicle conditions instantly, improving claim verification and reducing fraud.

Natural language processing (NLP) tools can parse through massive volumes of textual data, including claims descriptions, call transcripts, and social media posts, to detect fraud indicators or emotional sentiment. Meanwhile, telematics and IoT integration enables AI to interpret data from smart devices like fitness trackers, vehicle sensors, or home monitors to assess real-time behavior, health, or environmental risks.

Consider Lemonade Insurance, an AI-driven insurtech firm that processes claims through its AI bot, Jim. In 2025, it's reported that over 40% of Lemonade's claims are resolved within seconds. The system uses behavioral analysis and NLP to detect potential fraud and approve low-risk claims immediately—showcasing the power of AI in real-world scenarios.

Insurance Segments Embracing AI Risk Detection

• Health Insurance: Wearables and biometric data help AI models predict potential health issues, enabling personalized premiums and preventive care models.
• Auto Insurance: AI detects risky driving behavior through telematics data and adjusts premiums or coverage accordingly.
• Home Insurance: Smart home systems integrated with AI can identify potential fire, flood, or intrusion risks, allowing real-time risk scoring and alerts.
• Commercial Insurance: AI assesses operational data from factories, supply chains, and employee behavior to detect safety risks or cyber vulnerabilities.

Benefits and Challenges of AI Risk Detection

The advantages are compelling. AI minimizes human bias and error, leading to more precise risk profiling and increased accuracy. Automated systems can analyze thousands of data points in seconds, enabling instant policy decisions and faster underwriting. Risk scores update in real time, allowing insurers to adjust pricing based on current behavior, not outdated statistics, through dynamic premiums. AI also detects inconsistencies, anomalies, or repetitive claim patterns that may suggest fraud, often before payouts are made. Policyholders appreciate personalized pricing and faster service, which leads to higher satisfaction and retention and improved customer trust.

However, significant challenges remain. With AI pulling data from personal sources, insurers must navigate tight regulations and ethical boundaries around data privacy and security. If not properly trained, AI models may replicate societal biases, unfairly affecting certain groups through algorithmic bias. Customers and regulators want clear explanations of AI decisions, and "black box" algorithms pose a risk to accountability regarding transparency and explainability. Many insurers still rely on outdated software, making seamless AI integration with legacy systems difficult.

What the Future Holds

As more insurers embrace AI detection tools, expect a dramatic shift in the industry toward fully autonomous underwriting, blockchain-backed AI data sharing, and global regulatory frameworks for AI governance. In essence, the convergence of AI and insurance will drive a new era of proactive risk management rather than reactive claims processing.

AI risk detection is not just a tech trend—it's a business imperative. Insurers that leverage these tools will gain unmatched agility, accuracy, and trust from customers. As algorithms grow smarter and more intuitive, the line between technology and traditional underwriting will blur, setting new benchmarks in how insurance operates.

AI isn't replacing humans—it's enhancing human judgment with machine precision, redefining what it means to assess risk in a modern world. 

Mick Moloney is a Partner at Oliver Wyman, based in New York, and is the Global Head of the Insurance & Asset Management. Mick spends his time working with leading insurers, asset managers, and advisory firms on a range of strategic and execution topics with a particular focus on growth, innovation, and efficiency in retail and institutional life and retirement markets. Mick has over 25 years of experience in financial services. Prior to joining Oliver Wyman, Mick spent 15 years at Mercer where he was a Senior Partner and global head of the firm’s Financial Strategy Group (FSG) – the team that leads on risk advisory work for large clients. In addition, he served as a member of Mercer Investment Consulting’s Global, US and European Operating Committees and the European Operating Committee for the firm’s Retirement, Risk and Finance business. He is a Fellow of the Institute of Actuaries in the United Kingdom, a Fellow of the Society of Actuaries in Ireland, an Associate of the United States Society of Actuaries and has an Executive MBA from the Smurfit Business School, University College Dublin.

Paul Carroll

You've been on a thesis for a while, and I’ve wanted to explore it with you for a long time. That's this idea of private equity getting into life insurance as a way to grab assets and then invest them. Would you lay that out for me?

Mick Moloney

Sure. As you probably know, Paul, it's not a new phenomenon, if you trace the history of Apollo Athene, which is kind of the poster child of what we're about to talk about. I mean, the Athene piece of that is probably from the late 2000s in terms of when that initial transaction happened. I may have that off by a couple years or so. [Apollo established Athene, a provider of annuities and retirement services, in 2009, then, in 2022, acquired the 65% of the equity that it didn’t already own.]

We're talking something that's been playing out for over a decade. The critical piece is that that trend has been accelerating materially.

It took a little while for the dynamic to kind of come together. What has been happening over the whole period of time, just to kind of set the broader scene, is that, as you know, as interest rates came down, life insurers, in particular, got constrained for capital. And the public insurers, in particular, either began to or had made cash flow commitments to shareholders about returning capital.

So what you had was a dynamic where, in a number of cases, capital became constrained in various ways for the public life insurers. And at the same time, private capital began to look at a life insurance balance sheet as a means of raising permanent or semi-permanent capital, which as you know, for a private capital player is a very attractive thing in that you know you're going to get a certain amount of flow. You have insight as to when that capital is going to arise….

Marry that with a machine that originated structured [credit] and managed private credit, in particular. As you know, the vast majority of life insurance balance sheets consist of credit in some form – private credit, in particular, as the thesis has played out, by providing loans to more underserved parts of the landscape, where the risk-adjusted spread on those loans was greater. If you could pass some but not all of that risk-adjusted spread through to policyholders when you priced insurance liabilities, there was effectively what became a win-win there. You were more competitive, and the policyholder ended up doing somewhat better. You had assets that were very well suited.

Then, two things happened. The growth in the private capital-held life balance sheets, in the U.S., in particular, far outstripped the growth at some of the more traditional carriers, and that balance sheet component allowed the private capital business to accelerate.

The numbers really speak for themselves. Look back over the last 10 years and put a composite together for the private capital players: Apollo, Blackstone, KKR, Carlyle, Ares. Their market caps over the last 12 years have increased by, let's say,13 times. It's been huge growth. At the same time, if you construct a composite for life insurers, they're up about 1.2 times over the same period of time. That's not saying that they haven't returned capital to shareholders, but what they have kept in and the value of it, which if you think of it as a measure of relevance, you're saying that these private capital players have increased 13 times at a time when the public life carriers have increased just a little bit over one.

At the same time, the traditional asset managers have not had a great time, either. If you take the traditional asset manager set and look at what's happened to them, that is up about 1.3 times. What's been happening there is that there has been a big shift to passive investment. There's been a lot of fee pressure. Sure, assets have gone up, but the profitability of the model hasn't gone up. And investors are saying, Look, I think you're going to lose share to private capital going forward in the more long-only, traditional space, and you're going to come under increasing pressure as that occurs.

So the private capital guys have increased earnings and have gotten re-rated for future earnings, which is the other piece. The story that all the private capital players are telling moving forward is very consistent. It is that there's a move going on from public to private capital overall.

There's a big demand for debt funding for things like infrastructure-building and digital economies being built. There is also global aging going on, which means that as people shift into retirement, their risk appetite goes down. They move money from equities into bonds. So there are these big structural tailwinds that they're pointing to in terms of their ability to continue to grow going forward.

And if you look at it, one of the tests is that the private capital firms are trading at forward price earnings multiples in the mid-20s. The traditional life carriers are doing pretty well if they're seeing nine or 10 times multiples. What it's telling you is that investors like the private capital story, and that private capital story, in a good number of cases, also has a significant leg to it, which is the life insurance balance sheet.

And anywhere life insurers are writing spread business, they're facing these firms. A lot of incumbents currently say, Do we think we can replicate elements of that model? And if we do, how do we want to do it? And how do we want to go about it?

The key is the acceleration, at least in my view, in the last three or four years. We’ve seen the model really come together because of the share of the kind of spread business that the new private capital-backed players have been successful in getting.

Paul Carroll

In some ways, as I've read what you've been writing about this, I've thought, what took so long? I mean, Warren Buffett showed the value of a Berkshire Hathaway-sort of model, where you grab some assets and you use them to invest, and if you're a smarter investor…. I assume a lot of the reason for the delay is comfort level. Life insurers have to be able to return money to somebody 50 years down the road. And I assume some regulators were uncomfortable with the idea of turning the money over to these sorts of cowboys who were starting hedge funds in the ‘90s.

Mick Moloney

There’s a little bit of that, I would say. I can point to a few things playing out. And the parallels to the P&C side of things are interesting. You are right about Buffett, and you could look at some others there and say that it's about a model in which I'm getting these assets and I can kind of invest them.

That model, though, particularly on the P&C side, is generally a little bit different in that they're generally shorter-dated liabilities. On the property and casualty side of things, because of volatility and various other things, I don't usually spend quite as much time worrying about precision and asset liability matching as I do on the life side of things.

On the life side, what regulators are always concerned about is whether the capital buffer is sufficient against the liabilities. The capital buffer itself is a function of the degree of match between assets and liabilities and how much capital I need to hold for a mismatch. And there is a high liability number and asset number that is a little bit higher. So you do need a lot of precision in the capital buffer to make sure that you're not going to lose if something happens.

But coming back to your question as to why has it only happened over the last decade: Why isn't it a 30-year-old phenomenon? I would say it is somewhat opportunistic. The other phenomenon that's been happening behind the scenes is that the volume of credit demand being served by banks has been decreasing systematically. It's been decreasing for a long time, but particularly in the post-financial crisis, as more regulation was put in and the kind of risk capital framework for banks shifted in a certain direction, you can see that that trend continued.

So what you've had is a shift of lending that moved from bank balance sheets to non-bank balance sheets. It's that set of non-bank balance sheets that are the players that we're talking about. And you can say at some point in the evolution of that, those players recognize that given that the volume of lending is increasing, one of the obvious places to raise funding for some of that lending is in these longer-term vehicles. And Apollo Athene, not to say they were the only ones that thought of that, but they really put a lot of muscle behind it. I mean, they bought Athene, then they floated Athene, then they kind of bought Athene back.

I forget the exact date, but it was maybe late 2000s when they did that initial transaction. It's not that there weren't things happening before that, but I think that one, and the degree to which they went progressively further and further into having that model, was pretty catalytic.

Paul Carroll

Trees don't grow to the sky, but it seems like there's a lot of room left for this trend to continue, right? I mean, some years’ worth anyway.

Mick Moloney

Yeah. I think there's a lot of room left for it to play out, frankly, and in different dimensions. There is the private credit story itself playing out. I would say there is tremendous upside in that for all the reasons that that I’ve talked about: the move from public to private credit, the global retirement wave, the shift of funding from banks to non-bank balance sheets, the infrastructure, the demand for debt. The secular tailwinds to private credit, I think, are unarguable. I would also agree with you that the piece of this that is the private credit intersecting with insurance balance sheets still has a long, long way to play out.

What we're seeing now is a lot of the incumbents mobilizing around shifting their model. You've heard me talk about it before. I call it the asset management-led insurer rather than an insurer that happens to have a general account and that happens to have an asset management capability. It's kind of turning the model on its head. And with that shift I see a lot of the large incumbents looking to mobilize properly around this trend. It will be their opportunity to generate growth going forward and to get a re-rating in their own growth prospects.

I mean, if you're a carrier sitting on eight times forward PE and looking at the private credit shops trading at 25-plus times, you know there are very few ways to get that kind of growth re-rating outside of that avenue, I would say. And you can see them beginning to do it. They're saying, Look, we're going to put more effort into our third-party asset management business. We're going to make it more visible to the outside world. We're going to change

the leadership. We're going to signal to investors that we're going to want to do more kinds of transactions as we build out capabilities.

The trick for the incumbents, I think, is that right now, in terms of their currency for buying things that are trading, very few of the incumbents have all of the capabilities that they need sitting inside their asset management business. One of the ways that they're going to solve for that is to buy boutique private credit managers or capabilities or platforms. Right now, it's very hard for them to justify that when they're trading eight times and they're buying something that's trading north of 20. It looks dilutive to shareholders unless you have a very careful rationale around it.

So I think the carriers will try and take a little bit from the playbook of the private capital carriers and say, we need to mobilize around this. We need to put points on the board in terms of showing people that we can do it. That will get us a re-rating. And as we get a re-rating, we can kind of accelerate into where we want to go. So I think it's going to be a super interesting five-plus years for the industry.

You can see it in the themes coming out in investor day presentations. A good example is Sun Life, who had an investor day presentation where they were majoring on asset management, insurance, and, in their case, kind of health and various other things. But the degree to which they were talking about their asset management business, I thought, was very notable. I think we'll see that as we see other investor days playing out.

Paul Carroll

Do you think the incumbents can change fast enough, or are private equity guys going to come in and buy a lot of the insurance companies?

Mick Moloney

That is the question, I think. I mean, as you know, the private equity guys have already bought a lot. And, they have platforms now and are buying more by way of liabilities and scaling up. The question of whether the incumbents will move fast enough, I think, is the big unknown.

I have to say that we're in a lot of these conversations with a lot of parts of the landscape. I'm much more optimistic now than I would have been if you'd asked me that question two or three years ago. In my mind, the discussion has shifted a little bit.

If you were talking to some of the incumbents, let's say three years ago, the conversation then was, I don't like the fact that these guys are disrupting my business. I'm not sure I agree with the model. I would very much like it if the regulators would change the rules such that what I think these guys are doing isn't going to be as possible going forward, etc.

I actually think that has changed a bit now. The mindset shift that I've seen is that executive teams have really come around to saying, Look, maybe regulation needs to change at the margin, but we are in the private credit business, and this is the means by which we can chart a course to a better future for the organization.

Now it's still a question in my mind as to whether the mobilization happens fast enough. But the other thing happening at the same time is that the firms that have been successful at it have grown massively and are themselves larger organizations. And as you know, you don't find very many supertankers that move at speedboat pace.

So you have this thing going on where that newer component of the industry is kind of maturing and scaling up. And it's having to look into more complicated places for growth going forward.

I think it's going to be a big feature of the industry, for quite a while to come. And I think it is one of the most interesting things playing out globally in the insurance landscape.

Paul Carroll

I do think that is the big question. I sometimes tell people I've been watching the same movie for more than 35 years because that’s how long I've been covering technology. And I had conversations like this with a guy who has become a friend, who was the senior VP of strategy at GM in the late 1990s and early 2000s. He actually is the guy who gave the world OnStar, so good for Vince. But he would talk about how GM was changing, and I would say, You're not changing fast enough. He'd say, We're changing as fast as we can, and I’d say, The market doesn't care how fast you can change. It's going to dictate what happens – and they had their problems not long after those conversations.

Mick Moloney

I agree. I've had that conversation with one executive, in particular, that I'm thinking of right now about how I think that the incumbents have executive teams, or at least some of the executive teams, that want to move very quickly. But as you know, you've got an elastic band that you can kind of pull so much in terms of getting the momentum to kind of shift in the direction.

So I think change is imperative, and the need is clear. The question is about the ability to move the ship in a different direction.

Paul Carroll

We'll see. It'll be interesting to watch.