The insurance industry is at a crossroads. While technology has been reshaping the sector for years, the pace of change is now exponential, and the next 18 months will separate the innovators from the laggards.

The AI playing field is flattening

AI is democratizing access to advanced analytics, leveling the playing field between incumbents and challengers. But that doesn't mean everyone wins. In 2025, AI is no longer a differentiator – it's a baseline. The real edge now lies in how insurers apply AI: embedding it into underwriting, claims, and customer engagement in ways that are proprietary, explainable, and scalable.

The key is to combine AI with deep domain expertise and unique data assets. Insurers must also navigate the ethical and regulatory implications of AI. As AI becomes more embedded in underwriting, pricing and claims, scrutiny over how these systems make decisions is intensifying.

AI systems can unintentionally replicate or amplify biases present in historical data. In insurance, this could lead to proxy discrimination - where seemingly neutral variables correlate with protected characteristics like race, gender or income level. For example, using postal codes or credit scores in pricing models may inadvertently disadvantage certain communities.

This has triggered a wave of regulatory attention. For example, 24 U.S. states have adopted the NAIC's model AI governance framework, requiring insurers to implement robust oversight of AI systems, including documentation, testing and accountability mechanisms.

If insurers fail to act, the consequences could be severe – from legal risk and reputational damage to market distortion and regulatory backlash. The value of AI lies not just in automation but in intelligent, responsible application. That means building explainable models, auditing outcomes for bias and ensuring human oversight in high-impact decisions.

For large insurers, building proprietary models trained on their own data is becoming a key strategic asset. But not all insurers will have the scale or resources to do this alone. Those without the financial or data assets may need to buy, share or co-invest. Turning unstructured data into structured insights - especially in claims, pricing and underwriting - will be a priority. Re-thinking core processes around the possibilities of agentic AI could unlock a new level of value creation.

A strategy for underinsured risks

The protection gap - between insured and total economic losses - remains stubbornly wide. From climate change to cyber risk, many exposures remain underinsured or entirely uninsured.

Technology offers a path forward. By using granular data and advanced analytics, insurers can better understand emerging risks and tailor products to underserved segments, reducing the need for an uncertainty premium and thus improving affordability.

Insurers understanding the risks better also allows them to communicate those risks to policyholders better, providing personalized risk management advice and mitigation techniques. This is especially critical in regions where insurance literacy is low or affordability is a barrier.

Insurers must use data to identify protection gaps, then co-create solutions with communities and partners to close them - profitably and sustainably.

Playing in a world with more diverse capital sources

The capital landscape is shifting. Private equity, insurance-linked securities (ILS) and other alternative sources are flooding into the market, attracted by uncorrelated returns and new risk transfer mechanisms. This influx is reshaping the economics of insurance. The first quarter of 2025 has been as busy as never before, with investors eager to take on insurance risk.

For traditional insurers, this means adapting to a multi-capital world. It's no longer just about balance sheet strength - it's about capital agility. Insurers must learn to partner with, compete against and differentiate themselves from these new players. Ultimately, insurers must develop capital strategies that blend traditional and alternative sources, optimizing for flexibility, cost and risk appetite.

Re-energize digital distribution

Technology continues to transform the distribution landscape. Digital has long been a necessity in most markets, but all distribution channels will profit from higher degrees of automation, more insights at the point of sale and the power of predictive analytics.

Despite this, many insurers are still stuck in "digitized" versions of analog processes. The next frontier is intelligent distribution: using AI, behavioral data and omnichannel platforms to deliver personalized, seamless experiences. In emerging markets, mobile-first platforms are unlocking new customer segments. In mature markets, embedded insurance and API-driven ecosystems are redefining how and where insurance is sold.

In India, for example, mobile technology has expanded reach dramatically. At the same time, however, digital doesn't solve affordability. The challenge is to pair digital reach with product innovation that meets the needs - and budgets - of diverse populations. That means investing in digital ecosystems that go beyond transactions, building platforms that educate, engage and empower customers across the lifecycle.

Do your data homework

Data is the foundation of every transformation mentioned in this article, yet many insurers still struggle with fragmented systems, poor data quality and unclear governance.

In 2025, the bar is higher. Synthetic data, real-time analytics and AI-driven decision-making are becoming table stakes. Insurers must treat data as a strategic asset, investing in infrastructure, governance and culture to unlock its full potential. And that means breaking down silos, standardizing data models and embedding analytics into every decision. It also means upskilling teams and fostering a culture of data literacy across the organization.

Right now every insurer should be conducting a data maturity assessment, then building a road map to elevate data from operational input to strategic differentiator.

The next 18 months will define the next decade of insurance. The tools are available. The capital is flowing. The risks are evolving. The only question is: Who will act boldly enough to lead?

Infusion therapy has become a cornerstone of modern healthcare, transforming treatment for many chronic and complex conditions. By delivering medications and nutrients directly into the bloodstream, infusion therapy offers rapid absorption, precise dosing, and the ability to bypass the digestive system—advantages that make it indispensable for a wide range of patients.

The Clinical Value of Infusion Therapy

Infusion therapy provides fast, effective delivery of medications and nutrients through a needle or catheter. This direct approach allows for larger or more controlled doses, which is critical when precision matters. For example, patients with digestive disorders can receive vitamins, minerals, and medications intravenously, avoiding gastrointestinal side effects and ensuring consistent absorption.

The therapy's flexibility enables individualized treatment plans, with tailored dosages and schedules that optimize outcomes. It is instrumental in managing chronic illnesses such as autoimmune disorders, inflammatory bowel disease, and cancer—helping to control disease progression, relieve symptoms, and improve quality of life.

Beyond chronic disease management, infusion therapy supports a wide variety of treatments: antibiotics for severe infections, chemotherapy for cancer, epidural pain management during childbirth, and immune support during flu season or recovery from illness. Many medications that cannot be taken orally, or lose effectiveness in the digestive system, are best delivered through infusion, which ensures rapid relief and sustained therapeutic effects.

Barriers to Timely Care

Despite the proven benefits, infusion therapy is often hindered by administrative delays. Providers nationwide report lengthy waits for prior authorization and slow, inadequate reimbursement from health insurers.

One board-certified physician described waiting three months for prior authorization for a patient in urgent need of therapy, only to be reimbursed at a rate far below the cost of care—resulting in a significant financial loss to his practice. Another provider faced denial when an insurer labeled infusion therapy "experimental," despite clear medical evidence to the contrary.

These delays not only compromise patient health but also drive up indirect costs. Each day treatment is postponed, patients suffer while employers absorb millions in expenses tied to sick leave, short- and long-term disability, paid time off, workers' compensation, and the Family and Medical Leave Act (FMLA).

A Call for Reform

Health plans must dramatically improve their processes for prior authorization and provider payment. The highest priority in healthcare should be a patient- and provider-friendly system that ensures prompt diagnosis and treatment, grounded in the best medical evidence and research.

To achieve optimal outcomes and control both direct and indirect costs, infusion therapy should be carved out from standard health plans and administered independently within pharmacy-benefit claims systems.

The global risk landscape is changing rapidly – and not gradually or predictably but in sharp, destabilizing jolts in new directions. Political tensions are inflaming conflict zones, reshaping trade routes, and upending regulatory regimes. From Ukraine to the Red Sea, from sanctions on hostile states to the constant threat of industrial espionage, the rules of the world are being rewritten, much less by markets than by geopolitics.

Traditional insurance is struggling to keep up with this for the simple reason that it is designed to protect against events with historical precedent, such as wildfires and floods; it is not built to insure the fallout from human decision-making. Geopolitical risk is difficult to insure. Yet in the era of polycrisis, this is exactly where some of the gravest risks lie.

The new frontier: risks without precedent

The polycrisis, which refers to the combination of overlapping and interlocking risks, none of which can be treated in isolation, has created two distinct classes of threat. The first, which includes natural catastrophes, can still be addressed through conventional underwriting, though the severity and gravity of these crises is forcing insurers to move to a preventative insurance model, as I have argued elsewhere. The second class of threat is far murkier: the rising tide of cybercrime – which can be insured against – and, most relevant to this article, geopolitical risk, which can't. These threats are rooted in decisions made by people. They are not "acts of God" but acts of governments, factions, or individuals. Their consequences are no less severe, however. The problem is that because they are harder to model, they have traditionally been deemed uninsurable.

Insuring a company against these risks is still impossible; but we can give clients the means to mitigate their risk of disruption or worse from geopolitical shocks. That's why any comprehensive risk management approach must now include geopolitical-focused intelligence. By intelligence, I mean deep knowledge and analysis of the geopolitical landscape and how it is shifting that enables organizations to adapt ahead of time to avoid the worst.

Intelligence-led prevention

The insurance industry must move, and is moving, toward a Predict & Prevent model. In the context of geopolitics, this entails using what we call "enriched external intelligence" – real-time analyses of geopolitical developments, hostile state activity, regulatory change, and economic instability. This gives clients the clarity they need to make informed decisions. A new round of sanctions, a change in leadership, or an outbreak of unrest could trigger factory closures, border delays, or resource blockages; but with the right intelligence, businesses can assess their exposure and act early: by rerouting shipments, relocating personnel, or even withdrawing from volatile markets, for instance.

Companies must also consider insider threats. Hostile actors, including some state-backed groups, are increasingly targeting companies not just through cyberattacks but also by embedding operatives within their organizations. State-sponsored corporate espionage is increasingly common, which is why any geopolitical intelligence provision must include the vetting of hires and third-party firms, along with their continuing monitoring. This helps businesses to root out those human vulnerabilities before it's too late.

Geopolitical risks require corporate action

What I've described entails a change in approach from the world's insurers. But defending against geopolitical risks also asks of businesses that they think differently about the risks to which they are exposed. Intertwined unrest, sabotage, espionage – these could, in theory, be mitigated, but if and only if businesses are willing to arm themselves with the very best, most timely intelligence, and transform from passive recipients of cover to risk partners.

This has been the case for some time in the world's most volatile environments – in Iran, Russia or Ukraine, for instance. Political risk here isn't just a background concern. It's a key daily consideration. Understanding it is vital for doing business in or near these regions. Businesses need to factor in hostile-state tactics, from industrial espionage to cybercrime, and adopt a posture of strategic vigilance. The difference between fragility and resilience depends on having the right information at hand and the means to use it.

The path forward

Modern businesses don't need more paperwork. They need timely insight and foresight grounded on deep experience and expertise. They want answers to questions like: Where might conflict erupt, and what options do I have? Is this regime likely to collapse, and with what impact? Could someone in my professional network, perhaps a third party, be linked to a sanctioned entity, and how will it affect me? These are, unfortunately, not hypothetical risks. They're live concerns, and they demand live, high-quality intelligence and options to act on them. Increasingly, clients are turning to insurers not only for capital protection but for clarity and other integrated services to understand and manage their interlinked risks.

The wider point here is that insurers must move past quantitative data, which, crunched in the right way, issues in those neat and tidy numerical analyses that promise peace of mind. Today, we need to help our clients model the unmodelable and negotiate waters when no one can be certain what lies ahead. Our job is to support clients to navigate uncharted waters and weather the storms. The best intelligence is key to understanding and managing today's volatile geopolitical environment. It lets clients act early, act wisely, and go about their business with full knowledge of what lies ahead and with options to manage it. The larger insurers could also work hand-in-hand with governments and government-adjacent agencies, such as think tanks and resilience academies, to strengthen the resilience and preparedness to geopolitical threats not just of their clients, but of the country on the whole.

As experienced insurance professionals retire and fewer newcomers enter the industry, agencies are being pushed to do more with less. For independent agencies, this isn't just a hiring challenge—it's a shift in how business gets done. 

That's where technology comes in. Modern agency management systems (AMS) aren't just digital filing cabinets. They work as force multipliers, helping lean teams stay organized and efficient. These tools don't replace human expertise. Instead, they help teams move faster, make smarter decisions, and stay ahead in a competitive market.

Here's how independent agents can use technology to make the most of their workforce and stay competitive, even with fewer boots on the ground.

1. Smarter Cross-Sell Opportunities Without Extra Work

Tracking the client journey after a quote can be tough when leads are managed manually or spread across different platforms. Today's agency management tools solve this by centralizing lead tracking. Agents can easily follow prospects through every stage, from first contact to quoting, policy review, and final conversion.

Bringing all client interactions and sales progress into one system gives agencies better oversight and reduces missed opportunities. By analyzing client profiles, agency management systems can uncover valuable cross-sell and upsell opportunities within your current book of business so you can quickly and easily take action at the right time. For example, systems can identify auto clients who don't have homeowners' coverage or spot business owners missing cyber liability protection, opening the door for new business.

Customers tend to only think about their insurance when they have to—reviewing these opportunities with clients right before renewal, when they are most likely to be thinking about their coverage, can increase the likelihood of making a sale. For leaner teams, this technology is a game changer. It helps agents stay organized and maximize every revenue opportunity, doing more with less without sacrificing client service.

2. Renewal Service That Drives Retention

Retention is never guaranteed, especially among small business clients. According to new data from J.D. Power, only 55% of small business insurance customers say they'll "definitely renew" in 2025.

For lean teams managing a high volume of accounts, delivering the kind of communication that keeps clients coming back can be a challenge. That's where the right technology makes a real difference. Agency management tools that automatically track coming renewals help flag clients at risk of churning and provide agents with clear, data-backed summaries of premium changes. This gives agents the insight they need to have informed conversations with clients.

The result? Stronger retention, even with a smaller team.

3. Stay in the Loop With Instant Client Account Summaries

When a teammate is out or a client calls unexpectedly, finding time to get up to speed on an account can be tough. Instant AI account summaries within your agency management system can help by consolidating recent emails, texts, and agent notes into a single, easy-to-read view. No need to waste time digging through inboxes or message threads.

You can review client activity from the past week, month, three months, or even up to a full year. Whether you're stepping in for a colleague or just need a quick refresh, AI-powered account summaries help lean teams deliver consistent service without burning employees out on administrative work.

4. Help When—and Where—Your Team Needs It

As more experienced agents retire, agencies aren't just losing people—they're losing deep institutional knowledge. Onboarding new talent quickly and efficiently is now mission-critical. Fortunately, AI-driven virtual assistants within agency management systems offer real-time, conversational support, allowing users to ask questions about their system directly within the platform.

These assistants are available 24/7 and can guide users through tasks like sending automated emails or updating client records. Instead of waiting for support or digging through documentation, agents can pull up a chat box and ask, "Guide me in setting up new automations" and get an instant, step-by-step walkthrough.

This always-on training speeds up onboarding and helps new or part-time staff hit the ground running. It frees your team to focus on revenue-driving activities instead of navigating multiple systems or searching endless help forums and YouTube videos for basic troubleshooting.

Tech as the Teammate That Never Sleeps

As the talent gap widens, independent agencies must learn to do more with less. But that doesn't mean compromising service or burning out your staff. By embracing AI-powered tools and smart AMS systems, agencies can uncover new growth and support their teams like never before.

Technology won't replace agents, but it can empower them to serve faster and stay competitive, even as teams grow leaner. That kind of flexibility is what helps agencies keep up and thrive.

Helping clients manage risk is more than selling policies. It is about understanding and anticipating exposures that could derail their coverage, increase premiums, or lead to costly lawsuits. One risk often underestimated, especially by small to mid-sized businesses and commercial property owners, is noncompliance with the Americans with Disabilities Act (ADA)—a legal issue that frequently overlaps with premises liability exposures.

ADA noncompliance and premises liability center on the condition and accessibility of the property for everyone. Structural or design flaws can often impose barriers to those protected by the ADA, trigger ADA lawsuits, or create physical hazards to everyone, including those not protected by the ADA, that lead to injury claims. This dual exposure requires careful attention, clear communication, and proactive client education.

The Hidden Risks: ADA and Premises Liability

The ADA carries real legal and financial consequences, but many times clients do not realize or understand that standard general liability (GL) insurance policies typically do not cover ADA lawsuits. These claims are considered intentional acts of discrimination, not accidents or "occurrences," and are therefore excluded under most commercial policies.

Meanwhile, premises liability claims, such as trip-and-fall accidents due to inadequate ramps, broken walkways, poor lighting, or uneven flooring, are typically covered under GL policies. However, if the root cause of an injury is tied to ADA noncompliance, it could blur the lines of coverage. A court or insurer could interpret the exposure as an excluded civil rights issue rather than a standard bodily injury claim.

It is the broker's obligation to educate clients and help them understand that structural compliance issues are not just legal problems, but insurance liabilities.

Best Client Advice

The most critical step for clients is conducting a formal accessibility and premises audit. Relying on general contractors or architects to do this is not enough. A certified ADA compliance expert should be brought in to identify ADA violations and physical hazards that could result in injury or litigation.

Audits can mitigate risks by identifying design or maintenance flaws that violate the ADA. These audits demonstrate good-faith effort in compliance, which reduces legal exposure, provides documentation for courts, supports safer premises (which reduces GL claims), and informs custom underwriting for bespoke insurance solutions.

A comprehensive audit is a key element of a risk management strategy. When it is regularly conducted, it helps ensure ADA compliance, improves safety, and lessens the likelihood of lawsuits (both civil rights and personal injury).

ADA compliance is not all-or-nothing. The law requires "readily achievable" modifications to remove barriers (i.e., improve accessibility), directly reducing premises liability risks. Many structural compliance modifications are readily achievable (and needed) because the wrong slope on a ramp or improper curb heights can impose additional barriers, harm customers, and cause irreparable damage to your clients' businesses.

Other easily performed structural modifications include the addition of features to increase accessibility and improve safety, such as restroom grab bars, appropriate counter heights, and improved lighting. If additional, more substantial modifications are needed, a phased, documented plan, with the changes budgeted for in capital expenditures, will improve compliance and show good faith efforts to improve accessibility should an incident occur, significantly reducing liability.

While GL doesn't typically cover ADA compliance issues, there may be a supplemental insurance solution. Brokers should be ready to discuss:

• Legal expense insurance – Covers legal defense costs in civil or regulatory claims, including ADA and zoning issues. It doesn't cover damages but helps cover attorney fees.
• Employment practices liability insurance (EPLI) – Though designed for employee discrimination cases, many EPLI policies offer third-party coverage for customer-based ADA claims.
• Custom manuscript policies – Custom endorsements or surplus lines may offer additional coverage in higher-risk industries like hospitality, healthcare and retail. These may include legal defense coverage or special policy wording for accessibility-related lawsuits.
• Umbrella or excess liability policies – While these are not a substitute for ADA-specific protection, they typically have higher limits in case bodily injury claims stem from noncompliant-related hazards.

These discussions allow clients to make informed insurance decisions before a lawsuit happens, not after.

Brokers need to protect themselves as well, documenting all recommendations, especially when advising about audits, legal expense coverage, or ADA-related insurance gaps. If a client declines recommended coverage or skips an audit, put it in writing.

ADA violations and unsafe premises are easy targets for litigation, but they are an easy fix (and morally imperative) with some foresight. Brokers are uniquely positioned to protect clients from risk through their expert guidance.

ADA and premises compliance are no longer optional; they are smart business, sound risk management, strong insurance strategies, and ethically proper decisions.

By 2030, all Baby Boomers will be over the age of 65 and make up 21% of the U.S. population. By 2060, that percentage is projected to go up to 25%. For more and more aging Americans, the question has become: Where will I live? This growing cohort creates an increasing burden on both the healthcare infrastructure and on housing for the elderly, with not enough assisted-living or in-home caregiving options available to accommodate the vast number of seniors who need it. This limited access to care is now causing many older adults to move in with their children.

Enter the sandwich generation.

Currently, close to a quarter of American adults between the ages of 35 and 54 are caring for both a child/children under the age of 18 and a parent over the age of 65. They're called the sandwich generation, and according to a study conducted by the Journal of the American Geriatrics Society, they're more than twice as likely to report financial difficulty than their peers without children who are caring for an aging parent.

It's also likely that adults in this generation are experiencing the most stressful time of life, balancing career, household management, and all the tasks that come with raising kids — transportation, cooking, errand running, homework support, activities, and more. There are money stressors, too. Providing for themselves, their children, and their parents can create a financial burden that hurts all three generations.

For insurance providers, the sandwich generation represents a strong potential growth market. Providers can educate them on the many benefits that can help ease their burden both now and in the future. According to experts, they're underinsured. Here's why:

1. Many don't understand the true impact of losing a family member, because they haven't experienced it yet.

Because of their relative youth and the fact that at least one parent is still alive, the sandwich generation isn't completely aware of all the costs that come with a death, studies find. Beyond the funeral and burial or cremation costs, there may also be medical bills and estate lawyer fees to pay. Forbes states that, "Overall, the average direct costs related to the death of a loved one can reach $20,000. That's before factoring in lost income from taking time off or healthcare costs required to manage health and mental health symptoms."

Insurance providers can help by explaining that there are costs beyond medical bills and lawyer fees. The sandwich generation may be faced with expenses that come after the dust has settled, like lost income due to bereavement leave or the emotional repercussions of grief. A study from Empathy found, "Without proper support, the emotional toll of grief — compounded by the demands of daily life and the logistics of loss — can erode both mental and physical health. In our survey, members of the sandwich generation reported the most emotional and physical symptoms." The right life insurance plan can help offset these costs.

2. The sandwich generation may overestimate what life insurance costs.

While a life insurance policy may seem like an unnecessary expense in the tumult of daily life, it can be a huge source of financial relief when measured against the toll of caring for a loved one — even before they pass.

Empathy's survey shows that many millennials and Gen-Xers "...reported using their own financial resources to pay death-related bills; 42% used their own credit cards or checking accounts and 36% used their savings. Just 14% were able to tap into funds specifically designed for these purposes, such as life insurance or last arrangements insurance."

Providers looking to grow business in this demographic can point out the necessity of protecting one's savings, credit score, and future financial security. For the relatively affordable cost of a life insurance policy, the sandwichers can get support to help manage an aging parent's medical bills, funeral and burial or cremation costs, and legal fees, leaving breathing room to help with other expenses: like taking time off from work to give care and building savings for their children. It can be a way to avoid the massive financial toll that comes with a parent's passing and help provide for the survivors they leave behind.

3. Americans are afraid to talk about death. 

For the sandwich generation to lay a solid financial foundation, they have to be willing to have clear, honest conversations with their family members. But, according to Psychology Today, around 80% of Americans have a birth plan in place, while only 22% have a death plan. This has been linked to our growing physical separation from death. Until the early 1900s, people usually died at home, surrounded by loved ones. Today, most natural deaths take place in hospitals. As a result, our fears around death have grown and have made necessary conversations more difficult to initiate.

Honesty around death is essential to coping with it. Insurance providers can encourage adult caregivers to prioritize open dialogue, expressing their love and concern for their parents. Insurance providers can also help explain the many ways a policy can ease some of the fears associated with death and offer stability for those left behind.

There are a number of life insurance plans designed to help the sandwich generation cover the costs associated with caregiving and raising a family at the same time, and insurance experts can help them find the right solution for each person's situation and budget. The time to start planning is now.

Footnotes

1 Vespa, Jonathan. “The Graying of America: More Older Adults Than Kids by 2035.” United States Census Bureau. Census.gov. March 13, 2018. https://www.census.gov/library/stories/2018/03/graying-america.html

2 ”Sandwich Generation Study Shows Challenges of Caring for Both Kids and Aging Parents.” University of Michigan Department of Psychiatry. December 9, 2022. /https://medicine.umich.edu/dept/psychiatry/news/archive/202212/%E2%80%9Csandwich-generation%E2%80%9D-study-shows-challenges-caring-both-kids-aging-parents

3 Lei, Lianlian; Leggett, Amanda N.; Maust, Donovan T. “A National Profile of Sandwich Generation Caregivers Providing Care to Both Older Adults and Children.” Journal of American Geriatrics Society. November 25, 2022. https://agsjournals.onlinelibrary.wiley.com/doi/10.1111/jgs.18138

4 Rupe, Susan. ”Why the Sandwich Generation May Be Underinsured.” InsuranceNewsnet.com July 28, 2025. https://insurancenewsnet.com/innarticle/why-the-sandwich-generation-may-be-underinsured

5 Gordon, Deb. “Dying Can Cost Loved Ones $20,000 Before Lost Wages and Worse Health, New Report Says.” Forbes.com. January 31, 2023. https://www.forbes.com/sites/debgordon/2023/01/31/dying-can-cost-loved-ones-20000-before-lost-wages-and-worse-health-new-report-says/

 6”The Grief Tax: Empathy’s Annual Research Report.” Empathy.com. Jan 31, 2023 https://www.empathy.com/thegrieftax

7 Sarazin, Stephanie. The Reality of Mortality: Why We Aren't Talking About Death: Storytelling Can Flip the Script On Death and Dying Narratives—Including Yours.” PsychologyToday.com. October 27, 2023. https://www.psychologytoday.com/us/blog/soulbroken/202310/the-reality-of-mortality-why-we-arent-talking-about-death

8 Ashcroft, Rachel. “Americans Are Bad at Talking About Death, and It's Hurting the Environment.” TheWeek.com. April 7, 2022 https://theweek.com/feature/opinion/1012220/talking-about-death-is-good-for-us-and-the-environment

As volumes of unstructured data explode across legacy platforms, cloud apps, and shadow IT, many insurance carriers carry data risk that can undermine their mission.

Siloed platforms and manual recordkeeping allow information to accumulate unchecked across organizations' data estates. Obsolete files collected over decades mingle with sensitive, business-critical data, making it difficult for an insurer to understand what data they possess, where it's located, who should have access, and how long it should be kept.

The growing risk of unstructured data sprawl

The risk is real — a disjointed, chaotic data estate makes regulatory penalties more likely and increases cybersecurity vulnerabilities.

In turn, lawmakers and regulators are pushing for insurers to responsibly manage their data.

In the U.S., 28 jurisdictions have adopted the National Association of Insurance Commissioners (NAIC) Insurance Data Security Model Law (MDL-668); New York separately enforces 23 NYCRR 500, which sets rigorous cybersecurity requirements for insurers. Meanwhile, privacy laws like California’s CCPA/CPRA or the EU's GDPR compels firms to disclose how long they retain each category of personal information and to delete it when no longer needed. All three regulations push companies to govern the data they keep and defensibly dispose of what they don't need.

If the threat of regulatory penalties won't compel companies to manage their data, the threat of a costly data breach might: The average cost of a data breach in financial services reached $5.6 million in 2025, according to IBM. Minimizing and governing personal data directly reduces exposure.

But the difficulty is high: With 90% of organizational data being unstructured, firms have a hard time understanding and correctly classifying it. Unlike data that has a pre-defined structure or data model, unstructured data (documents, emails, media files, for example) is inherently free-form, with no pre-defined shape or format. Until you open that Word document, the only clue you'll have as to its contents will be its metadata, which includes high-level details like name and file size. As a result, teams spend a lot of time searching for the content they need or replicating it unnecessarily.

Analyst firm IDC saw 22% of IT decision-makers polled say unstructured data is unnecessarily replicated, and just 58% of unstructured data is ever reused after initial use/creation.

With AI, governance becomes an enabler

Data governance has traditionally been an afterthought, necessary purely for the sake of reducing risk. Organizations may only consider data governance in the wake of a data breach or a failed audit. But with the advent of AI, it has a new selling point: innovation and growth.

For organizations adopting AI (which is to say, most of them), they need to focus on their data, because to get the best results with AI, it needs high-quality, trusted, compliant data. Organizations that prioritize strong data governance can provide their AI platforms with data they're confident is authentic and reliable, free of bias and error, and respects individuals' privacy.

This is particularly important in the financial services industry, which is heavily regulated and deals with both personally identifiable information (PII) and payment card information (PCI). An insurer looking to adopt AI, even for a limited use-case, needs to be able to trust its data, and demonstrate compliance with privacy and financial regulations. The NIST AI Risk Management Framework (AI RMF) offers guardrails.

A modernization blueprint: establishing enterprise "data trust"

So, there you have it, two arguments for the centrality of data governance to the financial services industry: the stick (reduce risk) and the carrot (gain AI innovation). For insurers nervous about their compliance or looking to grow with AI, what does this look like in practice?

1. Inventory and classify at scale

Good data governance starts with developing an understanding of your data, both structured and unstructured, across file shares and SaaS platforms, so you can trust it and ensure its provenance. This isn't a point-in-time review — you need to do this continuously, at scale. Tools can help with this challenge, minimizing the risk of human error.

2. Codify retention and legal holds

Once you know your data, and you understand the regulations you are subject to, you can apply relevant retention schedules and implement legal holds when required.

3. Review data access and sharing

A study by Concentric found 15% of business-critical resources were at risk of oversharing. You need to audit your teams' access to data and implement a zero-trust, least-privilege approach that grants users access only to the tools and data they need to perform their role, and no more.

4. Minimize data and remove the ROT

Privacy regulations like GDPR or CPRA require entities to record and implement data minimization measures. Once your sensitive customer data reaches the end of its retention period, remove it. You can also remove the ROT (redundant, obsolete and trivial data) clogging your systems. ROT makes it harder to comply with privacy or records regulations, increases the attack surface for a data breach, and means AI models may provide substandard or noncompliant outputs.

How to start: a 90-day, high-impact pilot

This is an organization-wide effort, but it needn't be daunting. Get started by attacking one high-risk data issue: an excess of claims documents, tangled producer mailboxes, or SharePoint sprawl. Prove value fast by making an obvious difference in the storage volumes and help one business unit to achieve their goals faster.

Trusted, minimized data lowers risk and funds transformation. By taming the unstructured data beast, insurers lower their own data risk and position themselves to succeed in an AI-focused landscape, while preserving the trust of their customers.

In 2023, insurance companies spent $210 billion on IT — largely to keep outdated legacy infrastructure running. With that figure projected to grow 9% annually through 2027, funding the status quo is increasingly unsustainable and inefficient.

Despite investing billions to maintain legacy systems, most insurers fail to take full advantage of the valuable data they hold. After decades of collecting information across claims, underwriting, policy administration, and distribution, many still struggle to access and apply it in ways that deliver true business value.

Data modernization offers a better path — not just to reduce costs, but to unlock the value of the data they already have. Modernization turns siloed information into a shared asset that supports faster decision-making, stronger alignment, and outcomes that are easier to measure and scale.

If your organization views data as a burden rather than a breakthrough, you're not alone. But it doesn't have to stay that way. Now is the time to pinpoint what's holding your organization back and start using data more strategically.

Why data modernization stalls

When data is accessible, contextualized, and consistently maintained, it becomes a powerful driver of business performance. You can apply it wherever your business needs it most, from personalizing policy recommendations to refining risk models. Modernized data pipelines, for example, can reduce claims cycle times by up to 30% and improve combined ratios by several points.

Well-maintained data also lays the foundation for the broader modernization of insurance tools and platforms — a shift that can help carriers increase revenue by as much as 25%. A commercial lines carrier, for example, can use data to help underwriters prioritize high-conversion quote requests, generating more premium for their efforts.

Yet many insurers remain stuck in early maturity stages — not for lack of tools but due to outdated mindsets. Insurers have long treated data as a byproduct of operations rather than a product in its own right. This perspective limits investment in the infrastructure, talent, and governance required to make data usable across the business.

Without a strong data foundation, carriers struggle to forecast risk with precision, often defaulting to historical snapshots instead of real-time insights. Even if data scientists are in place, their ability to deliver advanced modeling or predictive analytics is limited.

And while AI promises to transform core insurance functions like underwriting, claims triage, and fraud detection, that potential remains largely unrealized. In fact, 92% of organizations have encountered rollout challenges with generative AI, with data quality cited as a leading barrier. The tools to modernize are available — but without a shift in how data is valued and managed, they're rendered useless.

The first step toward meaningful transformation is reframing data as a core asset — one that's accessible across the organization, not confined to technical teams.

Three steps to modernize your data

Modernizing data requires a fundamental transformation in perspective and a clear road map to guide the way. Here are three ways to modernize data and reap the benefits that come with it.

1. Set the right foundation

Before investing in tools, clarify why you're modernizing your data in the first place. Are you aiming to improve forecasting? Reduce underwriting leakage? Accelerate claims turnaround? Define the strategic drivers for both your business and your data practices, and make them shared priorities that anchor every step of the work ahead.

Equally important is ensuring cross-functional alignment. While modernization may be executed by IT, it's fundamentally a business challenge — and you should approach it as such. Long-term success depends on standardized practices and establishing shared ownership and responsibilities from the start.

2. Democratize your data

Even the most advanced data systems fall flat if data can't be securely shared and used across the organization. To make modernization meaningful, data democratization must be a core part of your strategy — standardizing access, consumption, and accountability across business functions. This isn't just an IT initiative; it's a company-wide responsibility.

That starts with ensuring your data is:

• Accessible: Teams should be able to explore, query, and analyze data without submitting tickets or relying on IT. Most carriers have centralized data stores and user permission frameworks in place, but access alone doesn't guarantee usability.
• Contextualized: Employees across the organization need more than definitions of the data — offer guidance on how it varies across geographies, systems, and use cases. Adding quality scores, usage guidelines, and business rules help make data interpretable and trustworthy, even when it's imperfect.
• Versioned: Data without timestamps or update history is unreliable. It's essential to know when a dataset was last refreshed or whether different teams are working from the same source. Version control and lineage tracking help keep analysis aligned and decisions accurate.

When data is accessible, contextualized, and versioned, it becomes a useful product that every team can rely on to drive insight and action. This shift lays the groundwork for scalable, cross-functional decision-making grounded in shared understanding.

3. Make the cultural change

Too often, data remains siloed within IT, with little ownership or visibility across the business. Data must be treated as a collective responsibility that's woven into the workflows and priorities of every department.

That starts with redefining data as a product — not a passive byproduct. Business units must actively shape, maintain, and apply data, with accountability for its quality and relevance integrated into daily operations. This means building data literacy, monitoring quality at the point of origin, and empowering teams to take ownership. When those closest to the data also manage its context, it becomes more trusted, usable, and strategically valuable. In a sales-driven industry, data becomes a true differentiator when every team is equipped to act on it.

This cultural shift must start at the top. Executive buy-in is essential to sustaining the long-term effort modernization requires. The resources are there, and the tools exist. But without business-wide commitment, progress will stall and your data will remain a missed opportunity.

Unlock the full value of your data

Modernization is a business imperative that can't afford to be sidelined. When every department can interpret and act on data with confidence, the result is faster decision-making, more accurate risk assessments, and stronger customer outcomes.

You don't need perfect conditions to begin. What matters is committing to the structures, habits, and mindset that turn data into a business-wide advantage.

The connective tissue between your operations, your customers, and your competitive edge is data. The choice now is yours: will you move forward by assessing your data maturity and aligning modernization with underwriting and claims — or risk being left behind?

Mergers and acquisitions (M&A) are high-stakes transformations that can redefine a company's structure, strategy, and vendor relationships. Amid the rush to integrate systems and scale operations, insurance brokers often find themselves under scrutiny—and at risk of being replaced.

A recent survey found that nearly one-third (31%) of companies switch insurance brokers post-M&A—a striking figure that underscores the volatility of broker relationships during transitions.

Why Brokers Lose Ground After M&A—and How to Avoid It

Post-deal broker changes aren't just about starting fresh. They often reflect a deeper misalignment between the evolving needs of a newly merged company and the capabilities of their existing broker. Many legacy brokers were a good fit for smaller, regional clients with straightforward coverage needs and personal service models. But after a merger, especially under private equity ownership, companies quickly outgrow that model. They now require enterprise-level support, digital integration, and broader risk expertise. Brokers who can't scale or adapt are often left behind, regardless of how well they served the legacy business.

Several changes brought on by M&A activity commonly trigger a reassessment of broker relationships:

Geographic Expansion Challenges: As the organization grows to operate nationally or globally, new risk requirements emerge, from jurisdiction-specific compliance issues to cross-border liability, international D&O policies, and export-related coverage. Brokers lacking carrier relationships across regions may be quickly replaced by firms with global footprints. The inability to navigate this expanded geography becomes a key disqualifier for regional brokers.

Workforce Growth Strains Coverage Confidence: An increase in workforce size triggers heightened scrutiny of employment-related exposures, such as EPLI, workers' compensation, and health benefits risk. If a broker can't facilitate seamless transitions across multiple employee populations or identify coverage gaps created by rapid growth, HR and finance leaders may lose confidence in their ability to protect the organization through transition

Increased Operational Complexity Demands Expertise: The transition to a more operationally complex enterprise introduces nuanced liabilities that require specialized underwriting knowledge. They need a broker that can handle risks like technology errors and omissions, professional liability, or recall exposures—especially when those risks are tied to revenue-generating units.

Private Equity Expectations Raise the Bar 

Recently merged private equity-backed companies have added intense pressure to deliver efficiencies and returns quickly. Investors seek scalable partners who bring financial rigor, cost containment strategies, and data-driven reporting to the table.

When companies consolidate, they often enter entirely new business landscapes. Expanding geographically, scaling their workforce, and diversifying operations all introduce more complex risk profiles. At the same time, leadership is under intense pressure to realize deal synergies quickly, prompting a reassessment of every vendor relationship.

In this high-stakes environment, brokers who offer only transactional services like renewals or claims processing risk being replaced. Decision-makers now expect strategic partners who can anticipate evolving exposures, identify coverage gaps proactively, and design scalable, cost-efficient programs that support long-term growth.

From Vendor to Strategic Partner: Solving the Benefits Billing Puzzle

In the post-M&A environment, brokers who rely solely on transactional services risk being sidelined in favor of firms that deliver integrated strategy and value. To remain relevant and indispensable, brokers must reframe their role: not just as insurance intermediaries, but as risk and operational optimization partners. One powerful way to do this is by addressing a pain point often overlooked during integration: benefits billing.

Benefits billing is a complex, administrative function that becomes exponentially harder after a merger. Companies may suddenly be managing multiple payroll systems, varied carrier relationships, legacy employee enrollment systems, and disjointed plan structures across locations or subsidiaries. The result? A minefield of billing errors, overpayments, and reconciliation blind spots – issues that quietly erode budgets and create compliance risk.

Brokers who bring outsourced benefits billing solutions to the table position themselves as proactive problem-solvers. Rather than leaving clients to untangle multi-carrier billing chaos on their own, these brokers offer a scalable, centralized solution that consolidates invoices, flags discrepancies, and reports all expensive errors so they can be corrected in the next billing cycle. This shift from transactional to strategic support is not just a value-add—it's a survival strategy in a post-M&A landscape where every vendor must justify their place.

How Outsourced Billing Reinforces Broker Value in M&A Scenarios

Scalability Without Increased Administrative Load: M&A often comes with the expectation of leaner, more efficient operations. Brokers that integrate benefits billing solutions into their service offering empower clients to scale quickly without adding headcount or burdening internal HR teams. By automating reconciliation across carriers and divisions, brokers prove they understand the new demands of growth and are equipped to support them.

Immediate Financial Impact: Outsourced billing solutions often uncover 12–15% in invoice inaccuracies—real money that can be redirected toward growth initiatives or integration costs.

Audit-Ready Accuracy During a Scrutinized Period: Post-M&A periods are often marked by private equity oversight, board reporting, or internal audits. A benefits billing partner ensures clean, centralized records, reducing risk exposure and enabling finance teams to answer questions about benefits spend with confidence. Brokers who provide this visibility elevate themselves from tactical vendors to operational stewards.

Technology-Enabled, Not Just Commission-Driven: In today's market, clients expect more than just access to carriers, they expect access to tools. Brokers who align themselves with benefits billing automation platforms demonstrate a tech-forward mindset, positioning them to retain modernizing clients by sending a clear signal: this broker is built for what's next.

Liberating HR to Focus on Integration Management: HR teams already overwhelmed with culture integration, workforce alignment, and new onboarding processes don't have the bandwidth to chase down invoice discrepancies. Brokers who solve this problem help clients increase HR capacity for post-deal needs: another signal that they understand the broader business context of the deal.

Earning Client Trust = Earning A Seat Post-Deal

In the wake of a merger or acquisition, broker relationships are no longer guaranteed. Those who step up with scalable, tech-enabled, and cost-saving solutions earn more than retention—they earn trust, influence, and a seat at the strategic table.

As the 2025 MIT State of AI in Business report finds, despite $30–40 billion in enterprise investment in generative AI, a staggering 95% of projects have failed to deliver any measurable business value. The authors dub this stark disparity the "GenAI Divide" – a small 5% of AI initiatives are generating millions in value while the vast majority remain stuck with zero return on investment. In short, high adoption has not translated into high transformation. Tools like ChatGPT are widely piloted, yet most enterprise-grade GenAI solutions never get past experimentation. According to the MIT study, these efforts fail not due to model quality or regulations, but due to approach – with common pitfalls including brittle workflows, lack of contextual learning, and misalignment with day-to-day operations.

How can organizations avoid falling on the wrong side of this GenAI Divide? This article offers a practical playbook. We outline five key implementation strategies and five guidelines for sustainable adoption to help enterprises turn promising AI pilots into production-scale successes. The focus is on disciplined execution and organizational alignment – moving beyond one-off demos to deeply integrated, value-generating AI solutions. The goal is to provide senior leaders a concise, HBR-style road map to crossing the GenAI Divide and realizing the business impact that so far has eluded 95% of adopters.

Five Implementation Strategies

To successfully implement generative AI at enterprise scale, leaders should apply the following five strategies. Each principle addresses a common failure point identified in the MIT report and steers projects toward long-term, production-level value rather than superficial wins:

1. Start Narrow, Scale Later – Rather than chasing broad, grandiose AI projects, begin with a focused use-case where AI can solve a defined problem and demonstrate clear value. The organizations on the right side of the GenAI Divide focus on narrow but high-value use cases, integrate deeply into workflows, and scale through continuous learning rather than broad feature sets. Starting small allows teams to learn, adapt, and earn quick wins. Once the AI solution proves itself in one domain, it can then be expanded to adjacent processes or scaled across the enterprise. This controlled approach prevents overreach and tackles the integration complexity that often stalls broader deployments. As the MIT study notes, successful innovators often "land small, visible wins in narrow workflows, then expand" – in contrast to less successful efforts that try to "boil the ocean" and end up overwhelmed by complexity.

2. Data Foundations First – Enterprise AI will only be as effective as the data and context you feed it. Before layering fancy models, ensure robust data foundations: consolidated, clean, and relevant data sources that the AI can learn from. Many GenAI pilots falter because the model lacks domain context or access to up-to-date internal knowledge. Top-performing firms in the MIT research "demanded deep customization aligned to internal processes and data", underscoring that AI must be grounded in the organization's own information and workflows. Investing early in data integration (connecting the AI to your databases, documents, and transaction flows) and data quality (governance, deduplication, lineage) will pay off later. A strong data foundation means the GenAI system isn't operating in a vacuum – it's embedded in your business reality, making its outputs far more relevant and reliable at scale.

3. Human-in-the-Loop by Design – Build human feedback and oversight into the AI workflow from day one. Generative AI shouldn't operate as an autonomous black box in enterprise settings – it works best as a collaborative tool that continuously learns from its users. The MIT report emphasizes that the core barrier to scaling AI is a learning gap: most GenAI systems "do not retain feedback, adapt to context, or improve over time". By contrast, projects that succeed treat AI deployment as an iterative, human-supported process. Establish formal loops for employees to review AI outputs, correct errors, and provide domain input. Design dashboards to capture these interactions and retrain models on this feedback. This human-in-the-loop approach improves accuracy, builds user trust, and ensures the AI evolves in line with real-world needs. It also assigns clear human accountability – critical in regulated and high-stakes environments – without forfeiting the efficiency gains of automation.

4. Governance and Risk Controls – Don't bolt on risk management at the end; bake it into the implementation plan. Enterprise AI adoption must be guided by strong governance: policies and guardrails for ethical use, regulatory compliance, and operational risk. Upfront, define what decisions or content the AI is not allowed to handle, establish approval workflows for sensitive outputs, and set up an oversight committee to monitor AI activities. This proactive stance prevents the common scenario of promising pilots being killed by compliance or security fears. Indeed, teams are far more willing to embrace AI if guardrails are in place during deployment. Effective AI governance includes transparency (knowing why the model produced a result), robust testing for bias or errors, and contingency plans when the AI gets something wrong. By instituting risk controls by design, leaders create the conditions for AI to flourish safely. Governance is ultimately an enabler: it builds the confidence among stakeholders – from frontline employees to regulators – that the new AI can be trusted in production.

5. Productization Discipline – Treat AI initiatives as products, not one-off projects or experiments. This means applying the same rigor to AI pilots that you would to bringing a new product to market: clear milestones, user testing, performance monitoring, and continuous improvement cycles. Many organizations stumble by considering an AI pilot "successful" after a demo, without planning for scaling, maintenance, and integration – the result is a pilot that never translates into operational impact. Instead, instil a product mindset. Develop an MVP (minimum viable product) version of the AI solution, deploy it to real users, gather feedback, and iterate. Incorporate MLOps practices for version control, monitoring, and model retraining. Successful adopters often "partnered through early-stage failures, treating deployment as co-evolution" – recognizing that the first attempt won't be perfect and committing to refining it over time. By expecting and managing iterative improvement, you turn a short-term pilot into a long-term, scalable product with a dedicated team and budget for continuing enhancement. Discipline in productization bridges the gap between prototype and production, ensuring the AI solution delivers sustained business value.

Five Guidelines for Sustainable Adoption

Implementation strategy alone isn't enough – the surrounding organizational environment determines whether AI truly takes root. The following five guidelines are leadership principles to ensure that generative AI adoption is sustainable, cost-effective, and deeply integrated into how the business operates. These guidelines emphasize change management, accountability, and the often-neglected factors that separate a flashy pilot from lasting enterprise transformation:

1. Align AI With Recurring Workflows – Focus on use cases that naturally plug into the regular rhythm of the business. AI solutions should attach to routine, frequent workflows – the monthly report preparation, the daily customer inquiry triage, the weekly financial reconciliation – where they can continuously assist and improve productivity. Aligning AI with recurring processes ensures two things: first, the AI system has a steady stream of real-world practice (and feedback) to learn from, and second, employees incorporate the AI into their normal work rather than viewing it as a novelty. Projects fail when they are mismatched to how work actually gets done. In fact, the MIT report found that many enterprise AI tools were "quietly rejected" because of "misalignment with day-to-day operations" . Leaders should therefore choose GenAI initiatives that map to pain points in existing workflows and design the integration such that using the AI is as natural as using email. When AI augments work that people already do frequently, it stands a far better chance of sticking and scaling.

2. Communicate in Business KPIs, Not Model Metrics – Drive the AI program with business-focused objectives, not just technical benchmarks. Executives and front-line workers alike care about outcomes such as revenue growth, cost reduction, customer satisfaction, and efficiency gains – not model precision scores or the latest algorithm. It's critical to translate AI performance into the language of business value. For example, instead of reporting that a model achieved 92% accuracy, communicate that it helped reduce customer churn by 5% or processed 1,000 more claims per week. This principle was evident among successful adopters in the MIT study, where organizations "benchmarked tools on operational outcomes, not model benchmarks". By linking AI initiatives to key performance indicators (KPIs) that business leaders recognize, you ensure continuing executive sponsorship and cross-functional buy-in. Importantly, framing results in terms of ROI and business metrics forces AI teams to stay focused on use cases that truly matter to the organization's bottom line, closing the gap between technical potential and realized value.

3. Build Cost and Performance Observability In – Once an AI system moves out of the lab, leaders need clear visibility into its usage, effectiveness, and costs. Too often, enterprises deploy generative AI without robust monitoring, only to be surprised later by escalating API bills, latency issues, or drifts in quality. Avoid these surprises by baking observability into the solution. This includes tracking metrics like inference cost per transaction, runtime performance, error rates, and the business metrics influenced (e.g. time saved per task). Set up dashboards that allow both the technical team and business owners to see how the AI is performing in real time. Observability is not just about tech metrics – it ties back to business KPIs. For instance, if an AI customer support bot's handle time creeps up or its customer satisfaction score drops, that should trigger an alert and investigation. Likewise, if monthly usage costs exceed expectations, it should prompt optimization or re-calibration of scope. Building this level of transparency creates accountability and enables data-driven decision-making about the AI's future. It ensures that scaling an AI solution doesn't lead to uncontrolled spending or unnoticed degradation in value. In short, treat your AI system as a living part of the business that needs continuous monitoring, just like any critical infrastructure.

4. Prioritize Security & Privacy – Any enterprise AI adoption must take security, privacy, and data protection as non-negotiable requirements. This goes beyond basic compliance checkboxes – it means designing the AI's data flows and integrations such that sensitive information is safeguarded at every step. Many companies remain understandably wary of generative AI tools because of confidentiality risks (e.g. an employee prompt inadvertently leaking client data to an external model). Address this upfront by implementing measures like data anonymization, encryption, on-premise or private cloud deployment of models, and strict access controls. That sentiment echoes across industries: if stakeholders don't trust that an AI system will keep data secure and decisions auditable, they will simply not allow it into production. Leaders should institute an AI privacy policy, involve the cybersecurity team early, and educate employees on safe AI usage practices. Additionally, consider model-specific risks – for example, generative models sometimes hallucinate (produce false information) or exhibit bias; robust governance and validation can mitigate these. By prioritizing security and privacy from day one, you not only reduce the risk of incidents, you also remove a major barrier to adoption – giving regulators, customers, and your own legal team confidence that the AI initiative is enterprise-ready.

5. Don't Forget the Last Mile: UX and Change Management – The difference between a pilot that impresses in the lab and a solution that succeeds in the field often comes down to the "last mile." This refers to bridging the gap between the technology and the people who use it. A great AI solution must fit seamlessly into users' workflows and be accompanied by effective change management. On the user experience (UX) side, integrate AI into the tools and interfaces employees already use, rather than forcing them to learn a new platform from scratch. Notably, business leaders in the MIT study stressed that if a new AI tool doesn't plug into established systems, nobody will use it – "If it doesn't plug into Salesforce or our internal systems, no one's going to use it." . This underlines the importance of meeting users where they are. On the change management side, involve end-users early, provide training, and appoint AI champions in teams. Many successful deployments began with enthusiastic front-line "prosumers" who tried out AI tools on their own and became internal evangelists. Leverage these power users to help others overcome initial scepticism. Leadership must also set realistic expectations – clarifying what the AI will and won't do – to prevent disappointment or fear. Finally, gather continuous feedback from users' post-launch and refine the solution and workflows accordingly. By investing in user experience design and organizational change management, you ensure that the AI initiative is not just technically sound but widely embraced by the people it's meant to help. This is what transforms a pilot into a scalable solution embedded in the fabric of the business.

The race to capture generative AI's benefits is on, but few have crossed the finish line. As the MIT report warns, the window to cross the GenAI Divide is rapidly narrowing. Enterprises are already locking in AI tools that learn and adapt, creating high switching costs and competitive advantage for the frontrunners. The urgency is clear: organizations that linger in perpetual "pilot purgatory" risk being left behind by more disciplined adopters. Bridging this divide requires more than enthusiasm – it requires executional rigor, deep integration, and a long-term commitment. The strategies and guidelines outlined above all point to a common ethos: treat generative AI as a transformational capability to be woven into the business, not a one-off experiment. Success with enterprise AI is ultimately less about the brilliance of any single model and more about the management practices around it – focusing on narrow value, strong data and governance foundations, alignment with people and process, and relentless iteration towards improvement. With a disciplined approach, cross-functional ownership, and an eye on sustainable value, companies can turn generative AI from hype into lasting competitive advantage. The opportunity is immense for those willing to invest in doing it right – and the cost of failure, in an era of rapidly advancing AI, is an ever-widening gap that no organization can afford.