You can't be everywhere at once, and your training and processes can only be so good. Adjusters, especially junior adjusters, can miss things in claims files that make a big impact on losses. These might be big misses, like with deadlines, or might be smaller misses that have a big impact, like whether contributory negligence was present. Both these kinds of errors can have a significant impact on your bottom line.

Claim leakage can be avoided with better tools, and AI is the ideal solution. AI can handle most aspects of file review with accuracy and consistency. However, you need a trained AI model to yield true value.

If you already use a product like OpenAI's GPT, you know that it can run into issues with deep or complex issues. Even many of the newest models that help with deed research still run into problems with lengthy and detailed output at speed. However, you should not compare your experience with the publicly available models on the internet against the quality that AI-specific insurtech companies can provide. AI is amazingly accurate when properly directed and trained.

Well-trained AI can handle virtually all aspects of a file review. After a file is closed, AI can also supplement the audit process to ensure your carrier's best practices were followed. AI has the ability to review tens of thousands of pages and compare any checklist against the claim's ultimate outcome and payout. AI can be incredibly proficient at this kind of outcome.

Using AI does not mean you (or your team) abdicate control over claim files, or the review of files. You should still validate the data. However, because AI has the ability to cite to specific pages relevant to its analysis, this process can be significantly sped up. This is especially true with mountains of medical files that are not relevant to the claim, or significant witness statements or communication logs, where only small bits of information are helpful or relevant.

This kind of review catches mistakes quickly and can be a terrific learning tool for your team. AI can speed up training time for new adjusters, who can immediately see areas of files that they may have not considered. Even when adjusters are manually trained, AI can be implemented to validate results and facilitate faster understanding of the key job functions and key performance indicators.

AI is not perfect, but neither are humans. The good thing about programming AI is that it follows your instructions every time the same way. Even if some of its output needs adjusting here and there, AI can be given a checklist of 10, 20, or even 100 things to review for every single file. It can effectively replicate your best and brightest over and over again.

Replicating your "best" is a key point as you consider software options. You want an AI that will replicate the best practices in your organization that are followed by the top 1-5% of all adjusters (or whichever team you are considering the use of AI with). Good AI software will start with your output in mind and work backward to determine what data, and ultimately what type of AI focusing, is necessary to produce that best output.

This process is different from a technology company that wants reams of data to "find" and tell you about your company's best practices. This generally produces average results (at best) and requires substantial internal training and focusing time to get you something very useful. The process takes a very long time, does not succeed, and costs substantial amounts of money. The reason why this can fail is in the inherent nature of how AI learns.

If you give any AI system 100,000 documents and you ask it to provide you with key concepts from all those documents, it will do a good job at summarizing them. It may even produce parts of a usable document as a template. This is because AI works by looking for correlations in the content of the documents. It is going to look for the things that most commonly appear. If you think about all the sections within the 100,000 documents as appearing on a bell curve, AI is going to go for the meatiest middle part of that bell curve. It will give you the results that closely match the middle because it is looking for correlation among the documents.

The issue with AI giving you the meatiest middle part of the bell curve is that the middle is the average. Nobody wants the average. Mitigating risk and reducing losses isn't about catching the average issues within a file - it's about catching the absolute largest number of issues no matter who is reviewing the claim. Average seems helpful in theory but is a failure in practice.

You do not want AI to produce average results, so you do not want it to evaluate the middle section of the bell curve. You want it to give you the very best, which means you want the results from the right-most area of that bell curve that represents the top of the top results. Conversely, you want AI to stay away from the very worst examples that reside at the left-most area of the bell curve - the place where the majority of leakage resides.

To get the best from AI, you must instruct it on your best practices. "Best practices" can mean either the best process/checklist you use, or the best example of a report that contains all the data you expect to see from your best people. Once you instruct AI on the best practices, then you can move backward into the reams of data to fill in the content. With the right application layer that directs AI, the results can be truly remarkable. This does not require creating a large language model just for your company's use, but rather harnessing smart applications built on top of the existing models.

Remarkable results can help reduce risk through better and more consistent file analysis, whether by an adjuster, outside counsel, or as part of a file audit. It can also reduce staff time by removing much of the labor-intense review of files that can take hours or days. Because AI doesn't get hungry, stressed, or tired, the time savings also means higher quality.

AI can offer greater benefits beyond time and file management. For example, AI can identify red flags in files, like excessive treatment, pre-existing conditions, or missing documents. It can provide an adjuster with a clearer understanding of property damage or bodily injury to better assess the claimant's demand. Using AI can even reduce the likelihood of a claimant getting counsel because an offer can be made within days versus weeks of the first notice of loss. The faster an offer is made, the less likely the claimant is to hire a lawyer.

Addressing demand letters is a new and powerful use of AI that smart carriers are implementing immediately. The plaintiffs' bar is already using AI to produce those demand letters, and the companies creating them brag about how much more money their AI-generated demands yield. One demand-generating company that recently raised funds on a billion-dollar valuation advertises that its users are 69% more likely to max out policy limits.

AI can effectively be used to counter these demands by recognizing holes in the file and presenting those to claimant's counsel. This includes identifying holes in coverage, such as endorsements or intentional conduct that might reduce or eliminate exposure. AI can do an initial review of liability by comparing police reports and witness statements to determine causation, and even flag contributory negligence and the lack of mitigation of damages.

As part of a file review, AI can also analyze damages and whether those appear excessive in light of the injury or economic information in the file. These kinds of robust demand responses point out all the ways a claim's value is not as high as the other side believes. This can yield higher leverage and lower payouts through appropriate risk analysis. This kind of analysis and response would be ideal for every file, but it takes a lot of time to do manually. AI offers the ideal solution, with the ability to produce a comprehensive response in less than five minutes.

AI can also reduce employee and customer churn. Using AI can lead to greater job satisfaction for adjusters and for customers. Your employees all of a sudden get to focus most of their time on the things that bring purpose and meaning to their jobs. They get to think more about strategy, talking to stakeholders, and analyzing files versus simply sifting through piles of documents that AI can do faster and more accurately anyway.

Customers are less likely to churn as claims are resolved faster and at fairer, more consistent valuations. When AI follows the same standards in every file, then variation in claim payouts stabilize, leading customers to appreciate the transparency and speed in which their claim is resolved.

The benefits of using AI are many, but it is not perfect. However, when AI reduces the time and energy it takes to review one file from 20 hours to two, that still equals a savings of 18 hours. And that is just one file. As you consider using AI for your organization, focus first on the best results that your best people produce. Then work backward. Also remember that perfect should not be the enemy of the good.

Early forms of artificial intelligence (AI) have played a role in shaping our technological landscape since the mid-20th century, from Grace Hopper's early programming breakthroughs during World War II to the codebreaking efforts involving the Enigma machine. Innovations like ELIZA—an early natural language processing program in the late 1960s designed to simulate human conversation—paved the way for today's AI-powered tools. Over the decades, AI has been quietly integrated into everyday life, from generating entertainment content and powering virtual assistant chatbots in banking apps, to recommending shows based on our streaming habits. That quiet presence changed dramatically in 2023, when generative AI tools, like OpenAI's ChatGPT, disrupted the market and brought AI to the mainstream.

Alongside these advances comes a troubling counterpart: deepfakes, which are capable of creating hyper-realistic videos, audio, and images that can be weaponized to impersonate executives, manipulate markets, and erode public trust.

This article explores the cybersecurity and reputational risks posed by AI—particularly deepfakes—and considers whether existing insurance products are equipped to handle them. How will the response to generative AI incidents differ from those traditional cyber-related incidents? As generative AI technologies continue to advance and become more sophisticated—and adopted on a wide scale—insurance providers face the challenge of determining how AI risk should be treated within the scope of existing insurance products or if they warrant their own distinct insurance product.

The Threat of Deepfakes to Businesses

Deepfake threats can take many forms. While the types of threats discussed in this article are demonstrative, they are just a small sample of the possibilities AI opens to cybercriminals. Like "traditional" cybersecurity security threats, AI threats evolve hand-in-hand with the underlying technology.

Blackmail & Extortion: Threat actors could use deepfake videos to manipulate or blackmail a company. By creating fake footage of executives or key employees in compromising situations, cybercriminals can pressure organizations to comply with demands or face reputational damage.

Social Engineering: Imagine a deepfake impersonating a C-suite executive, authorizing fraudulent wire transfers, or gaining access to sensitive information. This scenario is no longer hypothetical. A notable case saw a finance worker at a multinational company tricked into paying out $25 million to fraudsters who used deepfake technology to pose as the company's CFO. The ability of deepfakes to mimic the voices, likeness, and even the mannerisms of company leaders make them a powerful tool for cybercriminals.

Market Manipulation: Competitors or even nation-states could deploy deepfakes to damage a company's reputation, manipulate stock prices, or disrupt public trust. Fake announcements, altered earnings reports, or fabricated speeches from top executives could quickly erode investor confidence, causing significant financial losses. And once information is out, even if false, it is hard to contain. For example, on April 7, 2025, a misleading tweet on X regarding President Donald Trump's tariff policy caused turmoil in the U.S. stock market.

Reputational Damage & Liability Exposure: While reputational harm was once a major concern in the early days of cybersecurity, evolving public perception has made such risks feel more commonplace—though that may change as sophisticated AI-driven deepfake attacks push the boundaries of what's believable and trustworthy. Deepfake attacks can cause significant reputational harm —especially for high-profile leaders of publicly traded organizations. A CEO's image and trustworthiness are critical for stock performance and investor confidence. Deepfake technology has the potential to erode that trust almost instantly. Even if the content is later proven to be fake, the damage to a company's public image can linger, and the financial impact can be substantial.

Beyond public image, these incidents may lead to allegations that company directors and officers violated fiduciary duties, such as inadequate financial reporting, or failure to implement prudent AI policies or safeguards. Professional liability exposure may arise if AI adversely affects the rendering or performance of professional services.

The creation of fake content—such as a deepfake video of an executive making damaging statements—could also lead to immediate loss of consumer trust, stock price volatility, and lasting damage to the brand. This kind of damage is not only hard to quantify but also harder to recover from in a traditional sense, as rebuilding reputation takes much longer than addressing technical fixes or financial losses.

How Should AI Risk Be Covered by Insurers?

AI-driven incidents present unique challenges that may not be fully addressed or appreciated by traditional insurance policies.

From a policy language perspective, defining what constitutes an "AI incident" could be difficult. While deepfakes are a clear example, AI is also being used in various other ways, such as in decision-making processes, automation, and data analysis. Will all AI-driven incidents fall under this coverage, or will they need to be explicitly defined?

Furthermore, the complexity of claims associated with AI incidents, such as fraud or misinformation, may require new expertise and claims handling processes. For example, it could be difficult to identify liability in a deepfake scenario—will the board of a publicly traded company be found at fault for failure to implement adequate AI safeguards if a deepfake impersonates a CEO and causes stock price drops thus negatively impacting investors?

These challenges have created a debate over whether AI-driven incidents are sufficiently covered under existing insurance products or whether an AI-specific insurance product should be created to address these risks.

There are two schools of thought on how to approach coverage:

1. Traditional Coverage Perspective: Some argue that AI risk does not inherently change the covered risk, but rather changes the magnitude of the risk. For instance, traditional cyber insurance generally covers the financial losses incurred by an insured arising out of a cybersecurity incident; be it business interruption, crisis management costs, reputational harm, or damages arising out of third-party liability claims or regulatory investigations. If a threat actor group uses AI to infiltrate an insured's system, and then deploys a ransomware attack, the use of AI does not change the covered risk (loss due to a network intrusion), but rather makes it easier for the network intrusion to take place. The same can be said about other lines of insurance whose insureds interact with AI. Therefore, AI risk should not be covered under a standalone insurance product, as it is sufficiently covered under existing products. Notwithstanding, carriers should actively consider AI risk in the underwriting process and amend pricing and modeling operations accordingly.

2. Standalone AI Coverage Perspective: Given the unique nature of AI-driven incidents, some argue that this risk should warrant its own stand-alone product. Traditional insurance products were not designed with AI in mind. This could lead to gaps in coverage for losses involving AI. There is also a rising trend of specific AI exclusions in existing products. Without a dedicated product, businesses may find themselves unprotected from AI risks.

While this is far from a settled matter, it will be interesting to see how the industry reacts and adapts to AI risk in the near future.

Final Reflections

The rise of AI-driven risks poses a significant challenge for businesses and insurers alike. Whether AI-driven risks are adequately covered under existing insurance products or whether they should have their own distinct coverage category is a nuanced debate that requires careful consideration of the risks involved.

On one hand, AI-specific coverage could offer more tailored protection for financial, reputational, and operational risks. On the other hand, integrating AI-related incidents into traditional coverages might offer businesses more streamlined protection.

Ultimately, insurers must stay ahead of the curve by adapting their policies, training claims teams, and rethinking risk modeling. Businesses, too, must reevaluate their coverage and internal controls to ensure they are not caught off guard by AI-driven incidents.

Employee Retirement Income Security Act (ERISA) lawsuits have experienced a fever pitch, with 136 new cases coming to light in 2024, a shocking 183% increase from the previous year. This trend seems likely to continue into 2025, as new ERISA-related lawsuits filed against Southwest Airlines and Charter Communications were brought forward. While these legal actions are becoming prominent, the ERISA legislation experienced a milestone, recently celebrating its 50th anniversary, further indicating the endurance of this law and its strong framework in protecting employee benefits and emphasizing the need for clear guidelines of fiduciary duties for those managing retirement plans.

2025 and beyond will be high stakes for employers and companies that are maintaining retirement plans for employees, otherwise known as plan sponsors. Risk mitigation and contingency planning for protection of individuals and companies are essential.

A new consideration for plan sponsors?

ERISA, major federal legislation that took effect Jan. 1, 1975, governs employee benefit plans of almost all types, and holds fiduciaries – as broadly defined – personally liable for plan administration and management. It has evolved over the years to ensure it is up to date with market changes and retirement planning requisites to better support employees. Inherently, it is a long and complex legislation with numerous nuances that contribute to plan sponsors' challenges in maintaining compliance. Recent class action litigation indicates that there are standout fiduciary areas where plan sponsors are struggling – including 401(k) plan forfeitures in defined benefit plans, pension risk transfers (PRTs) and health plans, as well as who bears administrative costs for these plans.

Most current lawsuits challenge how forfeiture in 401(k) plans is handled, and the outcome could have significant repercussions for the sponsor community. Typically, forfeiture funds are those contributions associated with employees leaving their jobs before fully vested in their employer's contributions to their 401(k) plans. Plan sponsors can use these forfeited funds to offset their contributions. However, many lawsuits argue that ERISA requires these funds to be used solely for plan expenses or redistributed to plan participants. While the verdict is still out on the court ruling, one thing is for sure – should the plaintiffs come out victorious, there could be a massive shift in forfeiture policies.

In the same vein, PRT-related cases are under the spotlight. PRTs have long been leveraged as a favorable strategy by employers to eliminate their pension obligations and associated risks. Plan sponsors have typically conducted transfers to an insurance company through an annuity purchase or a lump-sum buyout. Yet recent court cases indicate that the tides may be turning as plaintiffs have filed a handful of cases alleging that these annuities are too risky and thus fail to meet ERISA fiduciary requirements. This is another area that plan sponsors would be wise to watch, as the outcome could result in higher standards for PRTs.

Health plan litigation is another area of concern for plan sponsors. In 2024, class actions against health plans were all over the spectrum, from actions on health-based wellness programs to how plans choose to provide pharmacy benefits to their employees, particularly their choice of pharmacy benefit managers. Plan sponsors should be keen on keeping up with the effects as they can broadly affect their programs and require significant adjustments.

The impact of an ERISA-based lawsuit

Legal issues are never on the agenda for businesses, as they bring forward an onslaught of consequences, but ERISA-related lawsuits can play a particularly malignant role in an organization's continued growth and success.

Firstly, the short- and long-term financial strain can be debilitating. ERISA lawsuits incur mountains of legal costs – from attorney fees, settlements, and more – potentially reaching well into the seven-figure range. Additionally, under ERISA, plan sponsors may face personal liability for confirmed fiduciary breaches, potentially leading to civil penalties, removal of fiduciary status, or criminal prosecution.

Companies should also be wary of the reputational damage an ERISA lawsuit can cause. Stakeholder trust can be eroded following a lawsuit, making it challenging to hold onto and attract new investors. Similarly, talent attraction and retention are heavily affected. Employee benefits and retirement planning support are now expected by employees. If marked by an ERISA-related lawsuit, top talent may look for other organizations that meet their long-term financial wellness needs. By losing top talent, businesses will struggle to maintain and grow their business performance.

These examples of potential impacts underscore the importance of companies and plan sponsors effectively managing ERISA compliance and fiduciary responsibilities. The best way to mitigate these issues and their impact is to avoid falling victim to alleged breaches and staying alert about legal rulings. However, given the complexities and nuances of ERISA, it can be challenging to keep pace. Realistically, plan sponsors and businesses must be prepared to address potential issues from all angles.

The need for fiduciary liability insurance

Plan sponsors may be aware that bonds are required by ERISA; however, their protection is limited to fraud and dishonesty. For comprehensive risk management and to better navigate the growing trend of litigation, fiduciary liability insurance should be at the top of the list for fiduciaries and their organizations. Typically sold in increments of $1 million, this insurance offers valuable protection against allegations of improper judgment related to employee benefit plans, including, most importantly, covering legal defense and even settlements.

While it is understandable that concerns about costs exist, neither the mandatory ERISA bonding nor the optional fiduciary liability insurance should be seen as expensive. Considering the backdrop of regulatory fines and penalties from the Department of Labor for non-compliance and the increasing cost associated with defending against litigation, the cost of insurance is quite reasonable.

The protection from this coverage extends to the sponsoring organization, officers and directors, and plan fiduciaries. As ERISA holds individuals with discretionary authority over retirement plans personally liable for decisions that harm employee beneficiaries, fiduciary liability insurance provides essential protection.

Additionally, firms should enhance their compliance processes by leaning on technology-driven solutions to stay current with new ERISA provisions and automate wherever possible. Leveraging tools and platforms that can support tracking vesting schedules and contributions reduces human error and oversight, often the drivers of fiduciary breaches. Furthermore, digital-first solutions can support generating audit-ready reports if needed to demonstrate ERISA fiduciary duties are being met.

Navigating the future of fiduciary risk

The long-term success of ERISA demonstrates the effectiveness of its framework in protecting American workers' retirement plans. As new retirement trends emerge, market volatility increases, and regulations evolve, there will be a continued emphasis on risk mitigation and compliance. It is crucial for plan sponsors to stay updated and not fall behind in these areas. They must remain vigilant in managing funds in accordance with ERISA, especially as legal scrutiny intensifies.

However, due to the complexity of ERISA, it is not uncommon for gaps to arise. A significant aspect of risk mitigation involves preparing for the worst-case scenario, particularly in facing potential allegations of fiduciary breach. Robust defense plans should include solid fiduciary liability insurance, monitoring evolving regulatory frameworks, and updating/automating compliance practices. Only then can organizations and plan sponsors have the peace of mind to run excellent plans for the sole interest of participants and beneficiaries, which in turn benefits themselves and the organization.

In the complex world of insurance, underwriters and brokers play crucial roles — but they don't always see eye to eye. Brokers act as trusted advisors, helping clients find the right policies, while underwriters evaluate risks to keep coverage financially sound. Even though brokers and underwriters share an end goal, miscommunication and disconnected data often create friction between them.

What's the fix? Better data sharing and governance. 

Here's why the two factors are key to improving collaboration, building trust, and driving efficiency across the insurance industry.

The Role of Data Sharing

Underwriters and brokers can benefit from seamless, secure data sharing with enhanced risk assessment. For example, brokers can send underwriters detailed client data about applicable risks, such as operational metrics or a history of claims, enabling the underwriter to evaluate the risk with much greater precision. Underwriters can also share dynamic insights with brokers, so they can create custom policies that might better suit client needs. In general, better data sharing could reduce redundant communications and ad hoc, manual entry, streamlining the process of issuing policies and processing claims.

Data Governance

While secure data sharing is essential, the data itself must also be governed, not only to enable compliance and security, but also to improve the confidence of both parties in the integrity and authority of the data to be shared. In terms of compliance, mandates such as the California Consumer Privacy Act (CCPA) in the U.S. and the General Data Protection Regulation (GDPR) and Solvency II in Europe require especially strong data governance capabilities to align data with these regulatory requirements. Additionally, data governance can establish rules about which users can access which data, to protect sensitive client and business information from breaches and misuse. Finally, data governance maintains accuracy across shared platforms, reducing errors and improving decision-making. 

The Limitations of Current Technology

One would think that in this age of powerful data lake houses and other cloud platforms, data access, governance, and sharing would not be an issue. Although this is largely true, the gray areas of data lake houses — that is, the situations in which data lake houses alone cannot enable seamless collaboration — are becoming larger and larger.

Although data lake houses are considered capable of storing all data to support every need, they cannot store all data, and they will never be able to do so. During mergers and acquisitions (M&A), for example, the data resources of entire companies will be temporarily unavailable. And in the case of multi-cloud infrastructures, in which companies leverage the capabilities of different cloud providers, certain datasets or workloads will never be stored in the main, central lake house. Data-export restrictions, to comply with data privacy and other laws, are yet another reason why some data will always remain distributed.

From a collaboration perspective, when data is distributed, it is simply not immediately accessible, and therefore not governable, especially if it changes rapidly.

Even if a company did manage to keep all of its data in a data lake house, data lake houses have a few limitations with regard to collaboration. They lack universal-semantic-layer functionality, which means that some data within the lake house will not be immediately usable. Universal semantic layers automatically transform data from myriad applications and departmental silos into the form required by the end user. Similarly, data lake houses do not provide extensive search and discovery features with comprehensive access controls, presenting another obstacle to seamless underwriter-broker collaboration.

Logical Data Management: The Enabler

It is evident that underwriters and brokers need a solution — one that works either standalone or alongside data lake houses and other cloud platforms — and one that can connect disparate data sources and create a semantic layer above all of them, to enable seamless, secure, and governed data sharing, in real time.

One such solution is logical data management. This is a data management approach that operates differently from traditional, physically oriented data management approaches that rely on extract, transform, and load (ETL) processes. In contrast, logical data management platforms enable data management, including real-time access and governance, without first having to physically replicate data into a central repository. Organizations with data lake houses can easily implement logical data management platforms to include other cloud and on-premises data sources, even though the data may be geographically separated or otherwise in a functional silo.

Logical data management platforms enable insurers to create a unified view of all related data for brokers and underwriters. Leveraging APIs and open insurance standards, brokers and underwriters can use logical data management platforms to engage in seamless collaboration. AI-powered analytics can further enhance the potential of underwriter-broker collaboration, helping them to gain predictive insights in the realms of risk assessment and policy personalization.

The Missing Link

The entire insurance ecosystem benefits when underwriters and brokers can collaborate effectively and securely through the seamless sharing of well-governed data. For starters, policy issuance becomes faster, leading to shorter turnaround times, which helps improve client satisfaction and gives companies a competitive edge. With more accurate data, risk pricing becomes more precise, ensuring better profitability and fewer disputes. Plus, this collaboration enables the creation of client-focused solutions, offering policies tailored to specific needs and strengthening the relationship between brokers and their clients. On top of that, clear communication fosters trust and transparency and paves the way for long-term partnerships built on mutual respect.

As the industry continues to evolve, the need for seamless collaboration between brokers and underwriters is only going to grow. Embracing advanced data-sharing practices and strong governance helps bridge gaps and sets the groundwork for innovation, agility, and resilience in what's becoming an increasingly complex market.

Navigating the complex considerations affecting organizations and their third parties presents challenges for insurance professionals advising businesses on compliance matters. As external experts, insurance professionals can often provide key guidance on verification requirements that protect organizations, but this takes time. What begins as a simple call or email can often cascade into a series of lengthy exchanges — turning a straightforward inquiry into a time-consuming back-and-forth.

AI solutions are emerging as supportive resources that strengthen insurance knowledge, expertise and efficiency. What makes AI a game-changer isn't automation alone, nor is it replacing human expertise — it's how it equips those with insurance expertise, and those without, with intelligent insights to better understand what items are needed to achieve insurance compliance.

According to a recent survey, 90% of small business owners are unsure about the adequacy of their coverage. AI serves as an intelligent assistant, quickly surfacing important information and providing context when needed. This allows both insurance experts and non-insurance professionals alike to understand what's needed and why it matters, fostering alignment and transparency.

The impact includes faster verification, fewer coverage and requirement gaps left unaddressed, and faster time to compliance. As Gartner predicts a doubling in risk and compliance technology spending by 2027, companies recognize that AI solutions that enhance collaboration deliver the greatest returns.

In insurance compliance, AI provides benefits in three ways: quickly identifying emerging risks, providing deeper insights and analysis, and enabling informed decisions — all while reducing manual effort and enhancing accuracy.

AI provides intelligent risk insights

Insurance verification has long operated as a black-and-white checkbox: compliant or non-compliant. This binary approach frequently disrupts insurance professionals, who must answer repeated basic questions from clients and their third parties, taking time away from key advisory work.

AI can enhance the process by offering intelligent, real-time insights within existing workflows. The technology automatically screens uploaded certificates, instantly identifying non-compliant documentation and generating precise, tailored communications to insurance professionals in their preferred language and professional context.

By acting as both an intelligent flagging system and a nuanced translator, AI eliminates the time-consuming back-and-forth that typically delays compliance processes. Insurance professionals can now focus on strategic risk assessment, while the AI handles routine verification, communication, and alignment across different stakeholders.

For instance, the system can identify specific documentation needs. Instead of a simple status notification, an AI-powered platform can share what's needed in clear language, why it matters, and how to obtain it.

This clarity fosters an environment where insurance knowledge is seamlessly integrated into the process, creating alignment across all parties involved. With everyone operating from the same information, AI tools can streamline communication and reduce confusion.

The result is a more collaborative, transparent, and simplified process in which AI can handle routine inquiries. This allows professionals to trust that compliance is properly managed without the administrative headaches.

Meanwhile, third parties and their agents benefit from improved transparency, easy communications and automated notifications that demonstrate their compliance status, establish them as reliable vendors, and facilitate timely payments — all of which strengthen their business relationships.

AI centralizes compliance and enhances visibility

Compliance verification often involves multiple parties with different priorities and levels of insurance knowledge, which can create communication challenges and process inefficiencies.

For insurance professionals, AI transforms client advisory services through three key capabilities: providing real-time visibility into compliance status, identifying and clearly communicating specific documentation needs, and enabling automated, precise notifications to address emerging compliance gaps.

The transparency provided by these systems allows third parties to see precisely where they stand on compliance at any moment, enabling them and their insurance agents to take steps toward resolution. Organizations gain comprehensive visibility into compliance trends across their network, identifying patterns and opportunities for process improvement that might otherwise remain hidden in dispersed data.

AI's ability to analyze large volumes of compliance data also provides risk insights tailored to the appropriate industry context, flagging potential gaps in compliance that can be addressed by humans before they escalate into business disruptions. While AI can't yet fully interpret complex or conflicting information, these automated alerts help identify areas needing expert attention.

AI systems can provide instant responses to routine questions, highlight complex insurance industry terminology, and offer contextual guidance as end users navigate through the system, thereby enabling insurance professionals to dedicate their expertise to more nuanced and strategic case analyses. This creates efficiency while equipping end users with insurance knowledge, ensuring specialized expertise is applied where it adds the most value and creating a more streamlined experience for everyone involved. Requirements remain firmly in place, but the path to meeting them becomes clearer and more transparent.

AI delivers business impact

For insurance professionals, the business case for AI extends beyond helping clients achieve processing efficiency — it can enhance their own service delivery and advisory capabilities. AI creates value through three strategic dimensions: efficiency gains, speed to compliance, and relationship enhancement.

Time savings represent one of the most immediate benefits, as AI automates routine verification tasks and provides instant feedback. This acceleration removes bottlenecks that delay project starts, contract finalizations, and service initiations.

Coverage verification quality also improves. AI doesn't get distracted, tired, or rushed during busy renewal periods. Organizations typically see significant improvements in compliance rates when implementing AI-powered solutions. It's always helpful, ready, and insightful. It also flags renewals well in advance, giving everyone ample time to meet deadlines — eliminating those last-minute rush requests that disrupt workflows. This improvement represents real risk reduction through faster time to compliance and potential cost avoidance from unexpected claims that could arise from non-compliance.

Perhaps most valuable for insurance professionals is how AI can transform their client communications by automating timely, precise notifications across all parties. These systems ensure instantaneous, compliant updates that eliminate missed deadlines, reduce administrative stress, and keep insurance agents, businesses, and third parties seamlessly aligned — transforming potential communication chaos into a streamlined, proactive workflow.

Companies that adopt AI compliance tools can gain competitive advantages through faster onboarding, stronger protection, and more collaborative relationships. These advantages translate directly to bottom-line results through reduced administrative costs, lower risk exposure, and improved operational efficiency.

The future of insurance compliance

AI is transforming how risk insights are distributed and leveraged across the insurance compliance ecosystem. By providing relevant information exactly when it's needed, AI helps organizations, their third parties, and insurance professionals work together more effectively.

As these technologies continue to evolve, increasingly sophisticated applications will empower everyone involved in the compliance process — whether they have years of insurance expertise or are new to these requirements. These systems will enhance visibility across the compliance ecosystem, automate review and renewal workflows, and facilitate more transparent communication channels between all stakeholders.

With AI-powered compliance tools, insurance compliance can become even more efficient, accurate, and collaborative. Insurance compliance is evolving from a necessary process into a strategic advantage that strengthens business relationships while enhancing protection. The future belongs to companies that recognize compliance is about creating value, not just checking boxes.

Recent improvements in the dexterity of humanoid robots and of the AIs that control them have led to a surge of publicity about their prospects, not just for industrial uses but as possible helpers in the home. Elon Musk, never one to shy away from a bold prediction, says there will be more humanoid robots than people on Earth by 2040. 

So my ears perked up when I heard that a half-marathon in Beijing over the weekend would include a classification for two-legged robots, as a way of showing off all the progress Chinese scientists have made. The robots didn't do so well.

Of the 21 robots that were entered, one veered into a barrier right at the start and shattered, while throwing its human handler to the ground. Another's head fell off. Still another had smoke pour from its head, while one ran in the wrong direction at times, then sat down and declined to get up. 

All the robots took large amounts of human attention: changing batteries, spraying water on the robots to reduce overheating, etc. Many had to be tethered to controls held by a human, who ran (or, more often, walked) behind the robots. 

Only four of the robots finished in the allotted time of less than four hours, and the fastest took more than 2 1/2 times as long as the human winner, who clocked in at an hour and two minutes. 

None of that is to say that humanoid robots have no future. Enthusiasts liken the race to the Grand Challenge for autonomous vehicles held in 2001 that also produced embarrassing results but, 24 years later, has Waymo providing 200,000 fully driverless, paid rides each week in its robotaxis. 

But the race does suggest the need for a sober look at the hype about robots, to set expectations for the insurance industry over the next five to 10 years.

If you want to see for yourself what the race looked like, here is a video summary. (The broadcast cuts away after 50 seconds.)

For me, the upshot of the race, in keeping with other progress reports on AI and robotics, is that, no, humanoid robots won't outnumber humans in 15 years. Not even close. 

They will be especially scarce in homes, where they will accomplish little while costing as much as a car. (Musk says his Optimus robots will cost $20,000 to $30,000 when they become available next year — and he has a long history of overpromising.) I dislike doing dishes and laundry, vacuuming and dusting as much as the next person, but I'm not going to pay tens of thousands of dollars to avoid minor chores, especially when my Oura ring keeps telling me to get up and stretch my legs. And you want me to maintain the thing? The extent of my trouble-shooting consists of turning a device off and then turning it on again. 

Robots have much better prospects in manufacturing, where they are already a force and are helping workers' compensation carriers and employers keep reducing injuries and, thus, premiums. The robots don't look at all human, but they have automated an awful lot of the assembly in electronics factories and others. Amazon and others use robots to handle much of the grinding work in warehouses. 

Progress in manufacturing will continue, likely rapidly, because robots can benefit from improvements in AI while operating in a controlled environment, not having to worry about maneuvering in a small kitchen full of  little kids and a puppy.

Even in manufacturing, though, there are limitations. The Wall Street Journal reports, for instance, on how hard it's been for shoemakers like Nike to move work to automated factories in the U.S. and out of Vietnam and China. It turns out that the soft materials in the upper parts of shoes change consistency based on heat and humidity. Skilled human workers can adapt, but robots have trouble. Robots also struggle with the fact that no sole of a shoe is quite the same as any other. They have trouble, too, with the constant changes in shoe design; robots function best when they can finetune their handling of a task and then do it over and over and over and over. 

Those of us of a certain age long for Rosie, the maid in "The Jetsons." My daughters tell me the updated version is "Smart House," a Disney movie in which a boy tries to keep his father from dating by programming a house to be a surrogate mother. Or there's "Cassandra," a recent series about a family that moves into a decades-old smart home and reactivates its dormant AI assistant, who was once a human and who was transferred into an AI system. 

Whatever your hopes are for robotics in the long term, as you think about the prospects for the next five or 10 years, especially in the home, it's worth keeping in mind this image of the robot that crashed, shattered and threw its handler to the ground only a few feet past the starting line of the Beijing half-marathon:

Cheers,

Paul

When alleged errors or breaches of duties give rise to professional or management liability claims, renewal premium increases are likely to follow. Policyholders often push their brokers to remarket the account in pursuit of more competitive pricing. The question is: Should insurance programs be remarketed to avoid any post-loss premium increase? 

The answer is often "no" (as long as the carrier is acting in fairly good faith and the increase is reasonable). Doing so is often penny wise, pound foolish. 

Here's why:

If the carrier has tendered the claim, they are demonstrating good faith by doing so (particularly if it's a claim that falls in a gray area). The fact that they are willing to offer renewal terms is additional testament to that good faith. It's uncertain whether another carrier would have taken the same coverage stance or been more aggressive in disclaiming coverage. Brokers and policyholders are better off working with insurers that have demonstrated their willingness to stand by them. Additionally, if the client has built a long history with this particular insurer and coverage is replaced, the client is effectively beginning a new relationship.

Even if the carrier has only shown partial good faith, covering only a portion of the claim (while disputing coverage for a portion of what should be covered damages) it may still make sense to renew coverage. In such cases, brokers (and the insured's counsel) may wish to challenge the coverage decision. When making such challenges, policyholders are likely to encounter less abrasion when coverage is still with the insurer in question – those who elected to replace coverage immediately following a claim may encounter greater resistance.

It's important to maintain a good relationship with the insurers during the claims process. It's not that replacing coverage will necessarily change the insurer's coverage determination, but it could make the claims process and any coverage determinations for future related claims more contentious.

Replacing coverage also leaves open the possibility for errors. Strong directors and officers (D&O) programs are often built over time, and rounds of policy term negotiations. Any enhancements obtained will need to be carried over to a new carrier. Errors such as incorrectly applied retroactive dates, advanced prior and pending litigation dates, overly broad related claims clauses or specific matter exclusions, and unaccounted for subsidiaries, are just a few examples of very basic general errors that can occur when replacing coverage, all of which can have a crippling effect.

As a practical matter,  replacement can also have unintended coverage consequences. Take the following example: An insured maintains a D&O policy, in which the 2024-2025 term is with carrier "A". A claim is noticed to the D&O carrier during that term, and the carrier has agreed to tender coverage. Shortly afterward, the carrier provides a renewal with a 35% increase, which prompts the insured to replace coverage for the 2025-2026 term, issuing a new policy with carrier "B". Months into the new term, the organization receives a new, separate demand, which is tendered to the new carrier. However, the new carrier has determined the allegations are similar enough to the prior litigation, and per the policy's terms (which will very likely include a specific matter exclusion), the new carrier disclaims coverage because it is "related" to the initial claim the year before. Carrier "A," however, has determined that the two claims are not related, and also disclaims coverage. Such a situation sets the grounds for an obvious battle.

These are just some of the many considerations brokers and their insureds should consider prior to making premium-based decisions, which may be more harmful than beneficial. That being said, there are situations in which it may be prudent to consider another carrier, namely: if the carrier is perceived as being overly contentious with what should be a covered claim, if the renewal terms being offered are more restrictive, or if the renewal premium is unreasonable.

Enterprises are engaging with agentic AI in five distinct ways:

1. Agent-Open: Developers are building AI agents on open-source Agent Development Kits (ADKs) such as LangChain, LLamaIndex (Meta), Haystack (Deepset), and Transformers Agents (HuggingFace).
2. Agent-Closed: Developers are building AI agents on "big-software" ADKs from the likes of Microsoft, IBM, Salesforce, and SAP.
3. Data-Small: Data engineers are building data pipelines on which to train and inference proprietary AI agents using ADK-like tools from mainly Databricks and Snowflake. I call this "small" because only a small amount of enterprise data is typically fit for consumption by AI.
4. Data-Big: This approach makes major investments in ontologizing and unifying the full corpus of enterprise data to be consumable by AI at scale at some point. Some enterprises are attempting this work themselves; others are paying Palantir to do it with their Foundry platform. These are big, hairy engagements; think: SAP enterprise resource planning (ERP) of AI.
5. Expert Agents: The first four approaches are for building agents to streamline work and workflows for productivity and operational efficiency. An expert agent is, for example, the clinician in a clinical workflow, i.e., the cardiologist or nephrologist. (Yes, it's coming.) These expert agents are by their nature GPU-chip intensive, andm as NVIDIA makes the GPU chips powering 90% of AI, their CUDA, NeMo, and Clara tools are by far the most cost-effective option for building expert agents.

Enterprise leaders seem to be asking two questions, the first of which is, "Can we connect an agent--however it's built--to our core systems?"

Google has built--and open-sourced--over 600 connectors against the likes of Microsoft Office, Adobe Acrobat, Salesforce, Workday, and ServiceNow, enabling agents of any origin to understand the data models of these core enterprise systems. These connector models are trained to understand different data elements, so in a Salesforce customer relationship management (CRM) dataset, for example, the connector understands "What's an account?", "What's an opportunity?", "What's a product?" It also knows when to access data, maintaining permissions "seeing" only what it's authorized to see.

The takeaway: You don't need to use Big Software ADKs to build agents interacting with Big Software datasets.

The second question is, "Can we have agents of different origin on the same team? Will teams built on one ADK work with teams built on another?"

Anthropic's open-source Model Context Protocol (MCP) has rapidly become the industry standard for agent-to-tool, and agent-to-data integrations. For agent-to-agent communications, the standard is Agent Protocol (AP).

Recently, Google, in league with 50 technology and consulting partners, announced the new Agent-to-Agent (A2A) protocol. A2A offers significant upgrades over AP including enterprise-grade security by default, support for long-running and asynchronous tasks, modality-agnostic communication (AP is text only, A2A adds images, audio, video), and the biggie: vendor-neutral and framework-agnostic design. This reduces vendor lock-in and allows organizations to compose best-of-breed agent networks easily.

It looks like A2A, open-source and available by the end of the year, will become the industry standard for agent-to-agent communications working seamlessly with MCP on agent-to-tool, and agent-to-data interactions.

The takeaway: McKinsey pegs the current market for AI products and services at $85 billion, forecasting growth to a low expectation of $1.6 trillion and a high expectation of $4.7 trillion by 2040. That sets the over/under line at $3.2 trillion: Bet the over.

In the last half of 2024, there were over 300 announced M&A transactions in the insurance space, valued at more than $20 billion. While the number of deals was down due to economic uncertainty, persistent high interest rates, and regulatory scrutiny, deal value was higher than normal.

What's driving a substantial amount of insurance M&A activity?

The third-party administrator (TPA) market.

TPA investment and acquisitions are nothing new. But several factors are creating a hotter market, encouraging acquisitions now and over the next few years:

• Private Equity Demand: Private equity interest is driven by a desire to deploy capital and achieve greater returns through growth potential and operational efficiency.
• Top Line Growth: TPAs are embracing a growth-through-acquisition model – improve top-line growth through acquisition, then implement cost takeout initiatives to improve business unit margin.
• Market Consolidation: TPAs have to achieve critical size to effectively provide services across multiple lines of business and remain relevant in the market – consolidation is how larger TPAs remain relevant.

The result is clear – acquire or be acquired. As a TPA, your acquisition strategy is either to add services to provide multiple lines of business at scale or become focused on a particular niche and establish market dominance through expertise.

Where Do TPAs Go From Here?

In the midst of this acquisition rush, TPAs (and other interested parties) are always seeking the next big opportunity for growth. But not all growth opportunities are the same. TPAs need to be forward-thinking. Over the next decade, the best TPAs will:

1. Leverage automation, technology, and AI to significantly reduce both labor and non-labor costs to aggressively improve margin
2. Develop a customer-centric selling model through upsell and cross-sell opportunities, using multiple lines of business to meet evolving client needs and gain greater client penetration
3. Provide clients with expertise to solve increasingly complex challenges through services and claims administration
4. Use a target operating model that encourages integration of new acquisitions to achieve synergies and improve overall enterprise performance through shared services and enterprise corporate functions

No single acquisition will fit every TPA – a variety of factors will influence which particular acquisition makes sense to a given firm.

Instead, TPAs that are seeking to grow across multiple lines of business should focus on markets that are primed for expansion. While there are several opportunities, there are four growth markets that TPAs should strategically evaluate their desire and capabilities to serve:

1. Healthcare Claims and Administration:

The combination of increasing costs around healthcare administration, complexity of health claims, and shifting demographics suggest a growing, lucrative market for TPAs. But investors have more than just top-line revenue growth to focus on. AI and automation opportunities can significantly reduce medical errors, accelerate claims processes, and reduce costs by reducing manual effort and standardizing key processes. TPAs have an additional reason to enter this space – the growth of the market is not just driven by processing insurance claims. Employers seeking to self-insure and expanding healthcare networks (e.g., hospital networks) provides another customer base. There are a variety of services that can be provided – bill review, long-term care assistance, or mental health care. Future-minded TPAs will assess opportunities to leverage data and analytics insights to provide opportunities to reduce healthcare costs. PE firms may seek to purchase or develop a TPA to administer claims associated with wholly owned long-term facilities.

2. International Claims

The insurance market in emerging economies is expected to experience significant growth over the next five to 10 years, far outpacing growth in established Western markets. Consider that life insurance premiums are expected to grow by approximately 6% in countries such as China, India, and Latin America, compared with the standard 1-3% annual growth seen in the U.S. Property and casualty (P&C) is expected to follow a similar trend. Protection gaps need to be addressed, and a strengthening middle class will have disposable income to address them. Insurance providers may have an interest in entering those markets but will likely partner with claims administrators to support global markets. Growth in the market and the opportunity to leverage global shared services models to significantly reduce cost position TPAs to be critical as carriers expand. Strategically, global TPAs will need to consider regional strategies to navigate geopolitical risk (e.g., supply chain/tariff challenges and international sanctions against countries).

3. Cyber-Related Claims

Increasing frequency and severity of cyberattacks, such as ransomware, data breaches, and phishing, are driving demand for cyber insurance, particularly for businesses. Small and medium-sized businesses are becoming more aware of their vulnerabilities. TPAs have at least two types of services to focus on through acquisition. One opportunity is in the B2B space, where TPAs can provide claims adjudication and processing in support of businesses facing a variety of cyber-related issues. In that space, TPAs may focus on fraud detection services, particularly combating AI-enhanced threats. The second opportunity is for TPAs to focus on cyber insurance sold through an embedded insurance model. One example that will become increasingly common: Individuals who purchase software or AI tools will have the opportunity to buy basic cyber insurance at the point of sale, with the opportunity to enhance coverage for specific AI protection gaps (e.g., protection against intellectual property (IP) infringement tied to AI-driven operations).

4. Legal Claims Administration

Over the next decade, legal claims administration will present another frontier for TPAs seeking to grow. Specifically, class action lawsuits and mass torts will provide opportunities for TPAs to administer legal claims. Class action lawsuits will arise, particularly as data breaches and greater data connectivity will take center stage for businesses across all industries. And mass torts will continue to be more relevant – evolving legal theories are increasing mass tort possibilities, both through expansion of harmed parties and creative theories on liability. For example, public nuisance claims were used in opioid litigation and are being considered for climate change and data privacy litigation. The challenge for law firms is that they do not do well in handling settlement, tracking down claimants, and managing documents. TPAs that can leverage technology to simplify and support law firms will position themselves well in the market. For example, smart contracts could automate the distribution of settlement funds to class members. Once eligibility criteria are verified, the smart contract could release payments directly to claimants, reducing administrative costs and delays.

The Price of Admission

While each of these areas presents a significant growth opportunity for TPAs, there are barriers to entry.

• Regulatory and Compliance Challenges: Each of these services has stringent regulatory considerations – for example, international claims administration requires understanding jurisdiction-specific laws and regulations. TPAs need to have a plan for how they will satisfy the compliance obligations associated with any new business unit or line of business.
• Technology Integration: Most TPAs rely on proprietary systems for claims processing and data management. This forces TPAs to take one of two paths – allow newly acquired businesses to continue to run as-is, without integration, or attempt data and platform migrations. Organizations need a technology integration strategy as a part of their M&A.
• Market Competition and Valuation: In the current economic environment, high-interest rates and economic uncertainty make only the best deals viable. Increased competition drives up valuation, making deals less financially viable to many of the firms. Specialized TPAs, such as healthcare of cyber-focused TPAs, face valuation inflation risk.

TPAs attempting to grow will overcome these challenges through comprehensive strategy and a commitment to providing services to carry them into an evolving market. TPAs should use strong due diligence, explore partnerships, and evaluate lessons learned from competitors' acquisitions to give themselves the best chance for success.

Agentic AI represents a paradigm shift. It perceives information; understands the context and intent; and autonomously creates, modifies, and orchestrates workflows through contextual reasoning and continual learning. These AI agents enable enterprises to be perpetually adaptable to the dynamic needs of customer and market conditions. 

The true revolution emerges in human-AI collaboration and its ability to drive business transformation across industries.

What is Agentic AI?

An Agentic AI framework has the following key components:

• Model - To reason over goals, planning and generating responses
• Tools - To retrieve data and perform actions by invoking an application programming interface (API) or services
• Orchestration - To maintain the memory, state, tools, data acquired/retrieved etc.

Why the buzz?

Agents have been in existence in various forms such as robotic process automation (RPA), workflows etc., but their applications were limited to non-complex and rule-based tasks that lacked adaptability to dynamic needs and often require human intervention. This is where Agentic AI strikes a perfect symphony for knowledge and complex tasks. With autonomy at its core, Agentic AI moves from an assist role to business transformation.

In today's volatile, competitive and complex business environment, enterprises and businesses are looking to continually adapt. The recent advancements in AI, IoT, robotics, etc. together with the need to drive efficiency and agility, make Agentic AI suitable for various applications across industries. They range from horizontal services such as knowledge management, quality assurance, HR, finance, etc. to industry-vertical services such as underwriting/risk assessment, loan processing, market research, claim processing, fraud detection, clinical management, cyber security, customer servicing, supply chain management, self-driving cars etc.

Agentic AI is enabling a paradigm shift, and new business models are emerging. Enterprises that were focused on the software-as-a-service model are pivoting to service-as-a-software. Also, there is a rise in the number of Agentic AI frameworks such as AutoGen, LangChain, LangGraph, CrewAI, Agentspace etc. to realize this vision.

Four potential applications in industry

Below is a list of a few applications in the healthcare, insurance, manufacturing and technology services industries.

1. Drug discovery – It is a complex problem in the scientific community, which involves years of research, analysis, experimentation and collaboration to arrive at possible solutions such as drug discovery for COVID-19. The challenge is that solutions must adapt to dynamic needs, and new information may become available (such as new variants of COVID-19).

This complex biological problem requires an approach where it can be decomposed into manageable sub-tasks with specialized tools for targeted problem areas (specialized agents, digital twin, research databases, etc.). The process involves brainstorming of ideas (e.g.: brainstorming agent), extracting and synthesizing information from research databases (e.g. search agent), experimental tools such as genome sequencing, analyzing the results (e.g. analysis agent), reasoning the various outcomes simulated using digital twin via techniques such as chain-of-thought, graph-of-thought or tree-of-thought along with feedback loops for continuous learning.

2. Claims Management – It is the core of customer servicing in healthcare and insurance and involves complex process and workflows to determine eligibility, process large datasets such as electronic health records (EHR), X-rays, treatment procedures, diagnosis, recoveries, medical bills, etc. and payouts. For instance, in group benefits (such as disability), this time-consuming problem requires a human-in-the-loop approach to reduce the financial burden and accelerate recovery to participants.

The claim intake agent involves sensors, spatial data about the accident environment, visual language models to analyze the injury details; validation and fraud detection agent to process the claim -- spatial and image analysis, knowledge graph and digital twin to test the hypothesis space. Once a hypothesis is validated, a decision-making agent must weigh in on the job-specific impact, claimant's ability and timelines to recovery and accelerated payout via blockchain. The agent can further actuate its role as a recovery and support agent to continuously monitor the progress, adjust the payout based on progress and optimize recovery to improve overall experience with explainability.

3. Manufacturing - From controlling the flow of production lines to customizing products to making suggestions for improved product design, Agentic AI is likely to have multiple applications in smart manufacturing.

Data from sensors attached to machines, components, and other physical assets in factories and transportation can be analyzed by Agentic AI systems to predict wear-and-tear and production outages, avoiding unscheduled downtime and associated costs to manufacturers. German AI start-up Juna.ai deploys AI agents to run virtual factories, with the aim of maximizing productivity and quality while reducing energy consumption and carbon emissions. It even offers tailored specific goals, such as production agents and quality agents.

4. Technology Services – Enterprises need to be perpetually adaptable, which hinges on speed, quality and cost. Agentic AI will play a prominent role by emulating capabilities of:

• "Requirement analysis agent" such as creating user stories based on standards and template (LLM + RAG)
• "Design agent" to interpret the requirements and create a blueprint based on approved technology, architecture patterns, data flows/source to target mapping (e.g.: for data migration efforts) etc.
• "Data engineering agent" for automated data discovery, build ingestion pipelines leveraging appropriate connectors
• "Data quality agent" for AI/ML driven anomaly detection, de-duplication, self-healing/auto-correction (e.g.: using GIS data wrt location/address anomalies) in conjunction with various tools
• "Synthetic data generator" for test data generation
• Digital twin to create and test hypothesis via "test and learn" simulations, thereby improving productivity and efficiency of data and tech. services/roles.

The way forward

As with any technology advancements, fundamental principles must be applied, such as guardrails for ethics, values, empathy and to address potential bias; explainability and auditability to enable transparency; human-in-the-loop for oversight and decision-making; and accountability on critical areas such as healthcare, financial services etc., 

Human-AI collaboration are to be evaluated closely as this frontier of AI expands its horizons.