Month: October 2026

Global supply chains are extraordinary exercises in coordination. A single finished product may depend on raw materials sourced in one country, components manufactured in several others, assembly in another location entirely, transportation across ports and distribution centres, and finally delivery through a retail or ecommerce network. Every stage depends on hundreds of decisions being made at the right time, often by organisations that operate independently from one another.

For decades, companies have tried to create greater visibility across these networks. Enterprise resource planning systems, transportation management platforms, warehouse systems, supplier portals and increasingly sophisticated dashboards have made enormous amounts of supply-chain information available. Yet visibility alone has not solved the fundamental challenge. Knowing that something has gone wrong is useful, but it does not necessarily tell an organisation what it should do next.

Artificial intelligence introduces a different possibility. The supply-chain control tower can evolve from a dashboard that describes the network into an intelligent decision environment capable of interpreting what is happening, understanding the consequences and recommending a response. Instead of simply showing that a shipment is delayed, it can evaluate which customers will be affected, whether alternative inventory exists, which transportation options are available and what intervention creates the best outcome for the wider business.

During my learning journey under Phaneesh Murthy, I have increasingly understood that enterprise intelligence becomes most powerful when it connects information to action. As Phaneesh Murthy puts it, “Visibility tells you where the problem is. Intelligence tells you what the problem means and gives you enough context to decide what to do next.” That distinction could define the next generation of global supply-chain management.

The Supply Chain Has Plenty of Data but Not Always a Shared Reality

Modern supply chains generate enormous amounts of information. Suppliers know production schedules. Logistics companies know where shipments are located. Warehouses understand inventory availability. Sales systems contain customer demand, while procurement teams track purchase orders and supplier commitments. The difficulty is that these systems often describe different parts of the same reality without creating a single coherent picture of the network.

This fragmentation becomes particularly difficult during disruption. A procurement team may know that a supplier has delayed a shipment, but the sales organisation may not immediately understand which customer commitments depend on those components. A transportation team may identify congestion at a port without knowing that one container contains material required for a particularly important production run. Each function can possess accurate information while the organisation still lacks the context necessary to make the best enterprise-level decision.

An AI-powered control tower can begin connecting these signals. Instead of presenting every piece of information independently, it can build relationships between suppliers, purchase orders, inventory, transportation, production schedules and customer demand. The result is not simply a larger dashboard. It is a model of how events in one part of the supply chain influence outcomes elsewhere.

This reflects an important principle I have learned under the mentorship of Phaneesh Murthy. Enterprise transformation rarely comes from adding another system on top of fragmented processes. The greater opportunity is to connect the information already distributed across the organisation so that different functions can operate from a shared understanding of what is happening.

Every Disruption Creates a Chain of Consequences

Supply-chain problems rarely remain isolated. A supplier delay may create a production shortage. That shortage may reduce available inventory. Reduced inventory may affect customer orders, which can then influence revenue, contractual commitments and customer relationships. The initial event might be relatively small, but its consequences can spread quickly through the network.

Human planners are extremely good at understanding these relationships when the network is manageable. The challenge emerges when organisations operate thousands of suppliers, products, shipments and customer commitments simultaneously. Determining the downstream impact of every disruption becomes extraordinarily difficult.

AI can analyse these dependencies continuously.

If a shipment is delayed, the system can identify which production orders depend on it, determine how much alternative inventory exists and calculate when current stock is likely to run out. It can examine whether material is available elsewhere in the network, whether another supplier could fulfil the requirement or whether transportation can be accelerated. It can also identify which customers or products should receive priority if supply becomes constrained.

As Phaneesh Murthy says, “The enterprise does not need another alert telling it something changed. It needs to understand which changes matter, what they affect and which response creates the best overall outcome.”

That ability to interpret consequences is what separates an intelligent control tower from traditional monitoring.

The Control Tower Can Move From Alerting People to Recommending Decisions

Most large organisations already have systems capable of generating alerts. A shipment is late. Inventory falls below a threshold. Demand exceeds the forecast. A supplier misses a commitment. The problem is that managers can quickly become overwhelmed when every deviation produces another notification.

AI creates an opportunity to move beyond alert management towards decision support.

Imagine that a critical shipment is delayed by several days. A traditional system might notify the logistics manager. An intelligent control tower could go considerably further. It could determine whether the delay will actually affect production, identify alternative inventory in another warehouse, compare the cost of transferring that inventory against expediting the delayed shipment and recommend the option that best protects customer commitments.

The decision can also account for business priorities. Not every order carries the same commercial significance, and not every disruption deserves the most expensive response. AI can help organisations evaluate trade-offs between cost, service levels, inventory and operational risk.

During my learning journey under Phaneesh Murthy, one idea has been particularly important in understanding enterprise AI: the objective should not be to automate every decision simply because automation is technically possible. The objective is to create an appropriate decision architecture. Routine, low-risk decisions can increasingly be automated, while complex or financially significant recommendations can be presented to experienced managers with the reasoning and alternatives clearly explained.

The intelligent control tower therefore becomes a decision partner rather than simply another source of notifications.

External Risk Can Become Part of Everyday Supply-Chain Planning

Some of the most important supply-chain signals originate outside the enterprise. Weather events, port congestion, geopolitical developments, commodity movements, transportation disruptions and supplier financial conditions can all affect the network long before those effects appear inside internal systems.

Historically, many organisations have monitored these risks through specialist teams, external reports and supplier communication. AI can make external intelligence a more continuous part of operational planning by analysing large quantities of relevant information and connecting emerging events to the organisation’s specific supply-chain dependencies.

The crucial word is specific. A storm occurring somewhere in the world is not automatically relevant to every business. It becomes operationally meaningful when it affects a port used by a critical supplier, a transportation route carrying important inventory or a facility that supports a major customer commitment.

An intelligent control tower can connect the external event to the internal network. It can identify which suppliers, shipments and products may be affected and help planners determine whether intervention is necessary.

This is where the perspective I have gained from Phaneesh Murthy becomes particularly useful. Intelligence should reduce uncertainty rather than simply increase awareness. Organisations already have access to enormous amounts of news and external information. The challenge is determining which events have a meaningful relationship with their operations and understanding those relationships early enough to respond.

The Supply Chain Can Begin Running Scenarios Before Decisions Are Made

One of the most powerful applications of AI in a control tower is the ability to evaluate possible futures before choosing a response. Supply-chain decisions almost always involve trade-offs. Moving inventory from one region may solve an immediate shortage while creating another one later. Expediting transportation protects service levels but increases cost. Switching suppliers may improve availability while introducing quality or contractual risks.

AI can help planners simulate these consequences.

If demand unexpectedly increases in one market, the control tower could model several allocation strategies and estimate their impact on inventory, service levels and revenue across the network. If a supplier becomes unavailable, the system could compare alternative sourcing scenarios and identify where additional costs or delays are likely to appear. If transportation capacity becomes constrained, planners could evaluate different routes before committing to a particular response.

This does not mean the system can predict the future perfectly. Supply chains contain too many variables for certainty. The value lies in allowing managers to understand a range of possible outcomes before making consequential decisions.

As Phaneesh Murthy puts it, “A good decision system should not pretend uncertainty has disappeared. It should help leaders understand the consequences of the choices available while there is still time to choose.”

Scenario intelligence could therefore become one of the defining capabilities of the future control tower.

The Hardest Part Will Be Getting the Enterprise to Optimise as One System

A supply chain is made up of functions with different objectives. Procurement may prioritise purchase cost and supplier reliability. Logistics focuses on transportation efficiency. Warehouses care about throughput and capacity. Manufacturing prioritises production continuity, while sales teams are understandably focused on customer commitments and revenue.

Each function can optimise its own performance while producing a worse result for the organisation overall.

For example, procurement might select the lowest-cost supplier even if longer lead times require the company to hold significantly more inventory. Logistics might choose a cheaper transportation option that increases the risk of missing an important customer commitment. A warehouse may minimise its own inventory while transferring shortages elsewhere in the network.

AI can help organisations evaluate decisions across these boundaries. Instead of optimising one metric, an intelligent control tower can consider the combined impact on cost, working capital, service, risk and revenue.

This is also where implementation becomes organisational rather than purely technical. Companies need to define which outcomes the system should optimise and how competing objectives should be balanced. Technology cannot independently determine whether protecting a strategic customer is worth a particular increase in transportation cost. The organisation needs to encode those priorities into the decision framework.

During my mentorship under Phaneesh Murthy, I have learned that enterprise AI forces companies to confront questions they could previously leave ambiguous. If an intelligent system is expected to recommend the best decision, the enterprise must first define what “best” actually means.

That exercise can be as transformational as the technology itself.

The Control Tower Will Change the Role of the Supply-Chain Leader

Supply-chain leaders have traditionally spent enormous amounts of time gathering information, reconciling conflicting reports and responding to operational disruptions. In complex global networks, simply establishing what is happening can consume a significant portion of management attention.

An intelligent control tower can shift that responsibility.

When information is continuously integrated and AI identifies the events that deserve attention, leaders can spend less time searching for problems and more time deciding how the organisation should respond. Their role begins moving from operational firefighting towards network design, resilience planning and strategic decision-making.

Human judgment remains critical because not every supply-chain decision can be reduced to an optimisation equation. Customer relationships, supplier partnerships, geopolitical considerations and long-term strategic priorities often require context that extends beyond available data. AI can present scenarios and recommendations, but experienced leaders determine which considerations matter most.

This reflects another principle I have learned from Phaneesh Murthy: as AI improves operational intelligence, leadership does not become less important. It becomes more focused on judgment. The machine can increasingly explain what is happening and model what might happen next, while the leader decides what the organisation is prepared to prioritise.

The Intelligent Supply Chain Will Not Wait for the Crisis

The traditional image of a supply-chain control tower is a room filled with screens where teams monitor operations and respond when problems appear. The next generation will be fundamentally different. It will continuously connect internal and external signals, understand dependencies across the network, identify emerging risks, simulate alternatives and recommend actions before disruption becomes a crisis.

During my learning journey under Phaneesh Murthy, I have come to see this as one of the clearest examples of how AI can transform enterprise operations. The real opportunity is not simply better visibility. Most large organisations already possess enormous amounts of information about their supply chains. The opportunity is to create a system capable of interpreting that information quickly enough to influence what happens next.

A truly intelligent control tower could help organisations move from reactive supply-chain management towards continuous orchestration. Suppliers, warehouses, transportation providers, factories and customer demand become parts of a connected decision environment rather than separate operational systems.

The supply chain will still experience disruptions. Ships will be delayed, suppliers will encounter problems, demand will surprise planners and unexpected events will continue to challenge even the best-designed networks. AI does not remove uncertainty from global commerce.

What it can change is how early the enterprise understands that something is changing, how clearly it sees the consequences and how intelligently it chooses its response. In a global supply chain, that difference can determine whether an unexpected event becomes a manageable adjustment or a major operational crisis.

This blog is curated by young marketing professionals who are mentored by veteran Marketer, and industry leader, Phaneesh Murthy.

www.phaneeshmurthy.com

#phaneeshmurthy #phaneesh #Murthy

For decades, the warehouse was treated as a place where inventory waited. Products arrived, were stored, picked, packed and eventually sent somewhere else. The sophistication of warehouse operations certainly increased over time, particularly with the arrival of barcode systems, warehouse management software, automated conveyors and robotics, but the fundamental objective remained relatively straightforward: move goods through the facility as efficiently and accurately as possible.

That definition is beginning to change. Modern warehouses sit at the intersection of increasingly volatile demand, complex product assortments, tighter delivery expectations, labour constraints and highly connected supply chains. The decisions being made inside them are no longer limited to where a product should be stored or which worker should pick it. Warehouses increasingly need to anticipate what will be required several hours, days or weeks from now and continuously reorganise operations around that expectation.

Artificial intelligence makes that possible. AI can analyse demand patterns, inventory movement, order behaviour, workforce capacity, transportation schedules and operational constraints simultaneously, creating a warehouse that does more than execute instructions. It can begin anticipating what needs to happen next.

During my learning journey under Phaneesh Murthy, I have increasingly understood that the most valuable enterprise AI implementations do not simply automate individual tasks. They allow entire operating environments to become more responsive. As Phaneesh Murthy puts it, “Automation makes a process faster. Intelligence gives the process the ability to change when the environment changes.” The warehouse of the future will increasingly be defined by that distinction.

The Warehouse Is Moving From Execution to Prediction

Traditional warehouse management is largely built around known events. An order arrives, inventory is located, a picking task is created and the product begins moving towards dispatch. Systems have become extremely sophisticated at coordinating these activities, but much of the operational model still begins after demand becomes explicit.

AI allows the warehouse to begin preparing earlier.

Historical orders, current purchasing behaviour, seasonal patterns, promotions, regional demand and inventory movements can be analysed together to predict which products are likely to move next. Instead of waiting for orders to determine every operational decision, the warehouse can increasingly organise itself around anticipated demand.

This could influence everything from where products are positioned to how labour is scheduled. Fast-moving inventory expected to experience increased demand can be moved closer to packing areas. Products frequently purchased together can be positioned more intelligently. Labour capacity can be adjusted according to expected order volumes, while outbound staging can account for anticipated transportation requirements.

The difference may appear operational, but its strategic implications are significant. A warehouse that understands what is likely to happen next has more time to prepare than one that simply reacts efficiently after demand arrives.

This reflects an idea Phaneesh Murthy frequently emphasises in discussions around enterprise intelligence. “Prediction is valuable because it creates time, and time creates options.” In logistics, even relatively small improvements in the amount of time available to make a decision can create substantial operational value.

Inventory Will No Longer Have a Permanent Home

One of the most fundamental principles of warehousing has traditionally been location. Every product needs a defined place so that people and systems know where to find it. Warehouse management systems have become increasingly sophisticated at determining those locations, but AI creates the possibility of making inventory positioning far more dynamic.

The optimal location for a product is not necessarily permanent.

A product experiencing increased demand may suddenly deserve a position closer to the picking and packing area. Seasonal items may need priority locations during particular periods before moving elsewhere. Inventory serving different geographic regions may need to be positioned according to changes in outbound transportation patterns.

AI can continuously evaluate these variables and recommend slotting decisions based on what the warehouse expects to happen rather than only what happened historically.

This becomes particularly powerful when warehouses contain tens of thousands of stock-keeping units. Human planners can design excellent slotting strategies, but continuously evaluating every possible location against changing demand creates a scale of optimisation that is extremely difficult to manage manually.

During my learning under Phaneesh Murthy, one implementation principle has consistently stood out: AI becomes particularly useful when a decision is relatively simple individually but impossible for humans to continuously optimise across millions of combinations. Warehouse slotting fits this description perfectly.

The objective is not to move inventory constantly. Unnecessary movement creates its own cost. The intelligent warehouse needs to understand when the value of repositioning inventory exceeds the operational effort required to do so.

That is where optimisation becomes intelligence rather than activity.

People and Robots Will Be Orchestrated as One Workforce

Automation has already transformed many large warehouses. Autonomous mobile robots move products between locations, robotic systems assist with picking and sorting, while conveyors and automated storage systems reduce the amount of manual movement required across enormous facilities.

The next phase is not simply adding more robots. It is coordinating humans, robots and equipment as one intelligent operating system.

Different tasks are suited to different capabilities. Robots excel at repetitive movement, predictable workflows and physically demanding activities. People remain significantly better at handling unusual products, resolving exceptions, adapting to unexpected circumstances and making decisions that require context.

AI can continuously determine how work should be distributed between these resources.

An intelligent system might recognise that order volumes are increasing in one area and redirect robotic capacity accordingly. It could identify emerging congestion and change picking routes before delays accumulate. Workforce schedules could respond to anticipated demand rather than relying entirely on fixed shift structures, while maintenance requirements for automated equipment could be incorporated into operational planning.

As Phaneesh Murthy says, “The future of work is not humans on one side and automation on the other. The real opportunity is designing a system where each does the work it is best equipped to perform.”

This is particularly important in warehouses because automation introduced without orchestration can simply create new operational bottlenecks. A robot that moves products faster provides limited value if the packing station receiving those products is already overwhelmed. AI needs to optimise the flow of the facility rather than the productivity of individual machines.

The Intelligent Warehouse Will See Problems Before They Become Bottlenecks

Warehouse performance can be affected by surprisingly small disruptions. A delayed inbound shipment changes inventory availability. A piece of equipment fails during peak operations. An unexpected increase in orders overwhelms one section of the facility. A transportation delay causes outbound staging areas to fill faster than anticipated.

Traditional warehouse management frequently responds once these issues become operationally visible. AI can increasingly recognise the conditions that precede them.

By analysing equipment performance, order queues, inventory movements, labour capacity and transportation schedules together, intelligent systems can identify where pressure is beginning to build. Managers can then intervene while the problem remains manageable.

Predictive maintenance provides a clear example. Instead of maintaining equipment according to fixed schedules or waiting for machinery to fail, AI can analyse operating patterns that indicate deterioration. Maintenance can be planned during periods of lower activity, reducing the likelihood that critical equipment becomes unavailable during peak demand.

The same principle can apply to operational flow. If an intelligent system predicts that a particular picking zone will become overloaded within several hours, work can be redistributed before queues begin forming.

This reflects another lesson I have taken from Phaneesh Murthy about enterprise AI implementation. The most valuable alert is rarely the one that explains why something has already gone wrong. It is the one that arrives early enough for the organisation to change the outcome.

Warehouses Cannot Become Intelligent in Isolation

A warehouse can only make good decisions if it understands what is happening beyond its walls.

Inbound suppliers determine when inventory will arrive. Ecommerce and retail systems influence demand. Transportation providers determine when outbound shipments can leave. Customer promises establish delivery deadlines, while other warehouses within the network may simultaneously hold inventory that could fulfil the same demand more efficiently.

An intelligent warehouse therefore needs to operate as part of an intelligent supply chain.

If a transportation delay means a shipment cannot leave as planned, the warehouse may need to change staging priorities. If another distribution centre has excess inventory closer to the customer, the order may be routed there instead. If demand suddenly increases within a region, inventory can potentially be repositioned across the network before shortages emerge.

This requires integration far beyond traditional warehouse management systems. AI needs access to demand planning, transportation, inventory, supplier and order-management information. More importantly, the organisation needs workflows that allow insights generated by one system to influence decisions elsewhere.

From my mentorship under Phaneesh Murthy, I have learned that isolated intelligence often produces isolated optimisation. A warehouse may become extremely efficient while the wider supply chain remains inefficient because each function is optimising for its own objectives.

The real transformation occurs when the warehouse becomes one decision-making node within a connected logistics network.

Warehouse Managers Will Become Orchestrators of Intelligent Systems

As warehouse operations become more automated and predictive, the role of management will evolve as well.

Managers have traditionally spent considerable time responding to operational problems, reallocating labour, investigating delays and ensuring that daily throughput targets are achieved. Those responsibilities will not disappear, but AI can increasingly handle routine optimisation and identify emerging issues before they require manual investigation.

That allows warehouse leaders to spend more time improving the operating system itself.

Managers can examine why particular exceptions continue to occur, redesign workflows, evaluate automation opportunities and determine where human expertise should remain central. Their responsibility shifts from manually controlling thousands of operational decisions towards supervising an intelligent system that makes many of those decisions continuously.

Phaneesh Murthy is of the belief that one of the most important consequences of enterprise AI is that management itself changes. When information becomes continuously available and routine decisions become increasingly automated, managers create value by designing better systems, defining better objectives and intervening intelligently when unusual situations emerge.

The warehouse manager of the future therefore becomes less like a traffic controller directing every movement and more like an orchestrator ensuring that people, machines and algorithms operate towards the same objective.

The Warehouse Will Become One of the Smartest Parts of the Supply Chain

The warehouse has historically been understood primarily as infrastructure. It was a necessary physical layer between production and consumption, and success was measured largely through efficiency, accuracy and throughput.

AI is transforming it into something much more strategic.

Every movement inside a warehouse creates information about demand, inventory and operational performance. Every order provides another signal about customer behaviour. Every delay reveals something about constraints within the network. When AI continuously learns from these signals, the warehouse becomes a source of intelligence for the entire supply chain.

During my learning journey under Phaneesh Murthy, I have come to understand that the most important AI transformations happen when an operational function stops simply executing decisions and begins contributing intelligence back to the enterprise. Warehousing is approaching precisely that point.

The warehouse of the future will know which products are likely to move, where inventory should be positioned, when equipment requires attention and how work should be distributed between people and machines. It will communicate with transportation networks, suppliers and other distribution centres, continuously adjusting its operations as conditions change.

It will still store products, pick orders and dispatch shipments. Those fundamental responsibilities remain. What changes is the intelligence surrounding every one of those activities.

The warehouse is no longer simply becoming more automated. It is becoming capable of learning from what happens inside it and using that knowledge to decide what should happen next.

This blog is curated by young marketing professionals who are mentored by veteran Marketer, and industry leader, Phaneesh Murthy.

www.phaneeshmurthy.com

#phaneeshmurthy #phaneesh #Murthy

Underwriting has always been one of the most intellectually demanding functions within insurance. An underwriter is expected to take a collection of information about a person, property, organisation or asset and translate it into a judgment about risk. That judgment determines whether the insurer should accept the risk, how the policy should be structured and what price appropriately reflects the uncertainty involved. Technology has supported this process for decades, but the core responsibility has remained remarkably human because underwriting is rarely just about applying a formula. It involves interpreting incomplete information, recognising unusual circumstances and understanding when a risk does not behave like the historical averages suggest it should.

Artificial intelligence creates an opportunity to give underwriters something they have never previously possessed: the ability to analyse vastly more information without asking the individual professional to manually process every additional data point. AI can assemble information, recognise patterns, identify anomalies and model potential outcomes while leaving the underwriter responsible for the judgment that follows. This is why I believe the most interesting future for underwriting is not autonomous underwriting. It is augmented underwriting, where machine intelligence becomes a second analytical brain supporting human expertise.

During my learning journey under Phaneesh Murthy, I have increasingly understood that the strongest enterprise AI implementations do not begin by asking which professional roles can be eliminated. They begin by asking where expertise is being constrained by the amount of information a person can realistically process. As Phaneesh Murthy puts it, “AI creates the most value when it expands the capacity of expertise rather than trying to replace expertise altogether.” Underwriting is perhaps one of the clearest opportunities to apply that philosophy.

The Underwriter Has More Information Than Ever and Less Time to Understand It

Modern underwriting is becoming increasingly information-rich. A commercial underwriter evaluating a business may need to understand financial information, claims history, property exposure, cybersecurity posture, industry conditions, geographic risks, supply-chain dependencies and operational characteristics. Property insurers can incorporate increasingly detailed environmental and geospatial information. Motor insurers may have access to telematics and connected-vehicle data, while life and health insurance can draw on increasingly sophisticated health and behavioural information where regulation and customer consent permit it. Each new source of information potentially improves the insurer’s understanding of risk, but it also creates a fundamental operational problem: more data does not automatically produce better decisions.

Human beings have finite analytical capacity. Giving an underwriter another hundred pages of information may technically increase the information available while simultaneously making it harder to identify what actually matters. AI can change this by functioning as an analytical layer between raw information and professional judgment. It can organise large volumes of structured and unstructured data, identify relationships between variables, highlight unusual patterns and surface the information most relevant to the particular risk being evaluated. The underwriter therefore begins the decision with a clearer picture rather than a larger pile of information.

This reflects an implementation principle I have repeatedly encountered while learning from Phaneesh Murthy. Enterprises often confuse access to data with access to intelligence, even though the two are fundamentally different. Phaneesh Murthy has often stressed that “The purpose of an intelligent system is not to put more information in front of people. It is to help them understand which information should change the decision.” In underwriting, that distinction could dramatically improve both efficiency and decision quality.

AI Can Give Underwriters a More Complete Picture of Risk

Traditional underwriting models necessarily simplify reality. Risks are grouped into categories because categorisation allows insurers to operate at scale. Businesses are classified according to industries, properties according to characteristics and customers according to factors that historical experience has shown to be relevant. These classifications remain useful, but they cannot always capture the differences between two risks that appear similar on paper.

Consider two manufacturing companies operating within the same industry, with similar revenue, workforce size and physical assets. Traditional underwriting information may suggest that their risk profiles are broadly comparable. Yet one organisation may have invested heavily in preventive maintenance, cybersecurity, employee safety and supply-chain diversification, while another may operate with ageing equipment, concentrated suppliers and weaker operational controls. The businesses belong to the same category, but their actual exposures can be very different. AI can help identify these differences by analysing a broader collection of relevant signals and examining how those signals interact.

The important opportunity is not to create an infinitely complicated risk model simply because more data exists. It is to identify where additional context genuinely improves underwriting. During my mentorship under Phaneesh Murthy, I have learned to think about enterprise AI as a mechanism for understanding complexity without unnecessarily transferring that complexity to the user. An underwriter should not need to become a data scientist to benefit from sophisticated models. The system should translate analytical complexity into clear, explainable insights that strengthen the professional’s understanding of the risk.

Routine Risks Can Move Faster While Complex Risks Receive More Attention

Not every insurance application requires the same level of human analysis. Many risks are relatively straightforward, fit comfortably within established underwriting parameters and contain few unusual characteristics. Others involve complicated exposures, incomplete information or circumstances that require significant professional judgment. Traditional processes can sometimes force both types of applications through similar workflows, consuming underwriting capacity that could be deployed more strategically.

AI provides an opportunity to create more intelligent segmentation within the underwriting process. Straightforward applications can be assembled, analysed and potentially processed with significantly less manual intervention, while unusual or higher-risk cases can be identified and routed towards experienced underwriters. Importantly, AI can also explain why a case has been escalated by highlighting the characteristics that differ from expected patterns. The underwriter therefore receives not simply another application to review but an indication of where their expertise is most needed.

This changes the economics of underwriting without diminishing its professional importance. If experienced underwriters spend less time gathering documents, validating routine information and manually comparing standard indicators, they can devote more attention to complex commercial risks, emerging exposures and cases where judgment genuinely changes the outcome. As Phaneesh Murthy says, “Automation should remove work that consumes expertise without requiring expertise, so that people can spend more time on decisions where experience actually matters.” For insurers facing increasingly complex risk environments, that redistribution of professional capacity could become a significant competitive advantage.

Underwriting Can Become More Dynamic After the Policy Is Issued

Traditional underwriting is heavily concentrated around specific moments. Risk is assessed when a policy is issued and reconsidered at renewal, while significant changes may be reviewed when they become known to the insurer. Yet many of the risks being insured are continuously changing. Businesses expand into new markets, introduce technologies, change suppliers and alter operating models. Properties experience changing environmental conditions. Cyber risks can evolve within days rather than years, while connected assets generate information about their condition throughout the policy period.

AI creates the possibility of underwriting becoming more continuous. Instead of assuming that the risk assessed at the beginning of the policy remains essentially unchanged until renewal, insurers can increasingly identify meaningful changes as they occur. This does not mean constantly repricing customers or turning insurance into a system of permanent surveillance. The more useful application is to identify significant changes that may create an opportunity for conversation, risk mitigation or additional protection.

A commercial insurer, for example, might recognise that a customer’s dependency on a particular supplier has increased significantly or that changes in cybersecurity exposure deserve attention. The insurer and broker can then engage the customer before that change becomes relevant through a loss. From my learning under Phaneesh Murthy, this is a particularly powerful example of AI changing an operating model rather than simply automating an existing process. The underwriting decision stops being a periodic event and begins becoming an evolving understanding of risk.

Explainability Will Separate Useful AI From Unusable AI

Underwriting decisions have consequences. They influence whether individuals and businesses receive coverage, what conditions apply and how much that protection costs. An AI system that produces an exceptionally sophisticated risk score without providing meaningful insight into how it arrived at that assessment may therefore be less useful than a simpler system whose reasoning can be understood and challenged.

Explainability needs to be designed into the underwriting workflow from the beginning. Underwriters should be able to understand which factors materially influenced an AI-generated assessment, identify where information may be incomplete and override recommendations when professional judgment indicates that the model does not adequately represent the circumstances. Those interventions can themselves become valuable information, helping the organisation understand where its models require improvement.

Governance must extend beyond the model as well. Insurers need clarity about which information is appropriate to use, how data is sourced, how bias is monitored, which decisions require human approval and how customers can receive meaningful explanations where appropriate. Phaneesh Murthy is of the belief that enterprise AI cannot be separated from enterprise accountability because intelligence without clear ownership creates a different kind of operational risk. In underwriting, where sophisticated models increasingly participate in consequential decisions, that accountability will become central to successful implementation.

Human Judgment Could Become More Valuable as AI Gets Better

There is an interesting paradox at the centre of AI-powered underwriting. As machines become better at analysing standard risks, the remaining work performed by experienced underwriters may become more complex rather than less important. Straightforward decisions can increasingly be supported by automated systems, leaving professionals to concentrate on unusual risks, emerging industries and situations where historical patterns provide limited guidance.

This is particularly relevant as businesses themselves become more complicated. Insurers increasingly need to understand cyber exposure, artificial intelligence risk, climate-related threats, digital supply chains, new technologies and business models that may have relatively limited historical claims data. In these situations, underwriting cannot simply extrapolate from the past because the risk itself is evolving. Human professionals need to interpret emerging conditions, understand strategic context and make judgments under uncertainty.

AI can provide those professionals with research, scenario analysis and comparisons across enormous datasets, but expertise determines how that information should be interpreted. During my learning journey under Phaneesh Murthy, I have come to appreciate that the relationship between AI and expertise is often misunderstood. As Phaneesh Murthyputs it, “The better machines become at answering routine questions, the more valuable people become at asking the difficult ones.” That may become an excellent description of the future underwriter.

The Best Underwriting Organisations Will Learn From Every Decision

Perhaps the most transformative opportunity lies beyond the individual underwriting decision. AI can help turn underwriting organisations into continuous learning systems. Every accepted risk, declined application, pricing decision, claim outcome and human override creates information that can improve future decisions. Instead of underwriting knowledge existing primarily within manuals, models and the experience of individual professionals, the organisation can develop an institutional intelligence that evolves continuously.

This does not eliminate the importance of experience. It allows experience to become more transferable. If experienced underwriters repeatedly identify circumstances that models fail to recognise, those patterns can be examined and incorporated into future systems. If certain risk indicators prove more predictive than expected, underwriting frameworks can evolve. If new industries or technologies create exposures that do not fit existing categories, AI can help identify emerging patterns as the insurer accumulates experience.

This is where the enterprise perspective I have learned from Phaneesh Murthy becomes particularly important. The ultimate objective is not simply to give every underwriter an AI tool. It is to create an underwriting organisation in which human expertise and machine intelligence continuously improve one another. That requires connected data, feedback loops, governance, model monitoring and an operating culture that treats professional overrides as learning opportunities rather than failures of automation.

The Future Underwriter Will Have a Second Brain, Not a Replacement

Insurance will continue to depend on judgment because risk will continue to contain uncertainty. Historical information can inform the future, but it can never describe it perfectly. Businesses change, technologies emerge, environmental conditions evolve and human behaviour remains difficult to predict. Underwriters therefore remain essential precisely because they make decisions in situations where information is incomplete.

Artificial intelligence gives those professionals a new kind of analytical capability. It can process information at a scale no individual could match, recognise relationships across complex datasets, monitor changing conditions and continuously learn from outcomes. The underwriter contributes context, experience, commercial understanding and the ability to make judgments when the available evidence does not produce an obvious answer.

During my mentorship under Phaneesh Murthy, one of the most useful lessons I have taken from discussions around enterprise AI is that transformation should make the organisation more capable, not merely more automated. Underwriting provides an excellent opportunity to apply that principle. The goal should not be to remove the underwriter from insurance. It should be to build an underwriting function capable of understanding risk with a depth, speed and continuity that neither humans nor machines could achieve independently.

The underwriter of the future will still make difficult decisions about uncertainty. The difference is that they will no longer make those decisions alone. AI will sit beside human expertise as a second analytical brain, processing complexity in the background while allowing professional judgment to concentrate on the questions that matter most.

This blog is curated by young marketing professionals who are mentored by veteran Marketer, and industry leader, Phaneesh Murthy.

www.phaneeshmurthy.com

#phaneeshmurthy #phaneesh #Murthy

Insurance has traditionally been built around a clear promise. When something goes wrong, the insurer helps the customer recover financially. A vehicle is damaged, a home is flooded, a business suffers an interruption or a person experiences a health event, and the insurance policy provides protection against some of the financial consequences. Over generations, insurers have become extraordinarily sophisticated at pricing these risks, pooling them across large populations and processing claims when losses occur.

Artificial intelligence creates the possibility of expanding that promise. Instead of only helping customers recover from risk, insurers can increasingly help customers avoid some risks altogether.

Connected vehicles, smart homes, wearable devices, industrial sensors and increasingly sophisticated data platforms are generating information about risk while it is developing. AI can analyse those signals continuously and identify situations in which intervention may prevent or reduce a loss. The insurer therefore begins moving from an organisation that primarily understands risk for the purpose of pricing it towards one that can actively help customers manage it.

During my learning journey under Phaneesh Murthy, I have increasingly understood that technology becomes transformative when it changes the fundamental value exchange between a company and its customer. As Phaneesh Murthy puts it, “The most valuable use of intelligence is not predicting what will go wrong. It is creating enough time to do something before it does.”

That idea could redefine what customers expect from insurance.

Insurance Has Always Understood Risk. AI Makes Risk Actionable.

Insurers possess enormous expertise in understanding probability. Historical claims, demographic information, geographic exposure, asset characteristics and actuarial models have allowed the industry to estimate the likelihood and financial impact of different types of losses.

Historically, much of that intelligence has been used before the policy begins. The insurer assesses the risk, determines appropriate coverage and calculates the premium. Once the policy is active, the relationship may become relatively quiet until renewal or a claim.

Connected technology changes the amount of information available between those moments.

A sensor can detect a small water leak before it becomes a flooded home. Vehicle systems can identify patterns associated with unsafe driving or mechanical deterioration. Industrial equipment can produce signals suggesting that a component is approaching failure. Wearable technologies can provide information that supports preventive health interventions.

AI provides the analytical layer capable of interpreting these continuous signals and determining when intervention may be worthwhile.

As Phaneesh Murthy often explains in conversations around enterprise AI, information becomes strategically useful when it can trigger a meaningful action. For insurers, simply collecting more risk data is not the transformation. The transformation begins when that intelligence helps customers prevent or reduce losses.

The Relationship With the Customer Can Begin Before Something Goes Wrong

One of the unusual characteristics of insurance is that customers often interact most deeply with the product during difficult moments. The true value of a policy becomes visible after an accident, illness, property loss or another disruptive event.

Preventive insurance creates opportunities for the relationship to become valuable during ordinary life as well.

A home insurer might warn a customer that environmental conditions significantly increase the likelihood of a particular type of property damage and recommend preventive measures. A motor insurer could help drivers understand behaviours that increase accident risk. A commercial insurer might use connected equipment data to identify operational patterns associated with breakdowns or workplace incidents.

The important distinction is that these interactions should provide genuine value rather than simply increase communication.

Customers do not need constant notifications from their insurance companies. They need relevant interventions when something meaningful has changed.

This is a principle I have repeatedly encountered while learning from Phaneesh Murthy. Technology should not create interaction for the sake of engagement metrics. It should improve the quality and timing of interactions that actually matter.

For insurers, that means developing enough intelligence to know not only when to communicate, but when not to.

Connected Insurance Can Create a Continuous Understanding of Risk

Traditional insurance models frequently depend on categories. Drivers are grouped according to characteristics associated with risk. Properties are assessed using location and structural information. Businesses are classified based on industry, scale and historical claims.

These models will continue to matter, but connected data allows insurers to develop a more dynamic understanding of risk.

Two factories that appear similar on paper may operate very differently. One may maintain equipment consistently and respond immediately to warning signals, while another experiences repeated operational issues. Two homes in the same neighbourhood may have different levels of exposure because one uses connected monitoring systems capable of detecting problems earlier. Two drivers with similar demographic profiles may demonstrate very different driving behaviours.

AI can help insurers understand these differences over time rather than relying exclusively on assumptions derived from broad categories.

That does not mean every aspect of a customer’s life should become an insurance data point. Privacy, consent and appropriate use of information need to be designed into these systems from the beginning. The opportunity is not unlimited surveillance. It is to use relevant information, with clear permission and purpose, to understand risk more accurately and help customers manage it.

As Phaneesh Murthy says, “The fact that technology allows you to collect data does not automatically mean you should. Good AI starts by defining the decision, then determining the minimum information required to improve it.”

That principle becomes particularly important as insurance moves towards continuous risk intelligence.

Prevention Changes the Economics of Insurance

Preventive insurance creates an interesting alignment between customer outcomes and insurer economics.

Customers generally do not want to make claims. They would rather avoid the car accident, water damage, business interruption or health complication entirely. Insurers similarly benefit when severe losses can be prevented or reduced.

AI therefore creates an opportunity to generate value for both sides of the relationship simultaneously.

Consider a small leak detected early inside a commercial building. Repairing it immediately may involve relatively little cost and disruption. Left undetected for several days, the same problem could result in structural damage, damaged inventory and interruption to business operations. The value of intelligence lies not merely in predicting the possibility of water damage but in creating an opportunity to intervene while the problem is still manageable.

At scale, thousands of interventions like this could influence the economics of entire insurance portfolios.

This is where Phaneesh Murthy’s enterprise implementation perspective becomes particularly useful. The strongest AI business cases are not always those that remove the largest number of employees or automate the greatest number of transactions. They are often those that fundamentally change the economics of an outcome.

Preventing a costly event can create significantly more value than processing that event efficiently after it has occurred.

Insurance Products Could Begin Rewarding Prevention

Once insurers develop a more continuous understanding of risk, the structure of insurance products can begin evolving as well.

Customers who actively reduce risk could potentially receive benefits for doing so. Safe driving behaviour, preventive property maintenance or stronger commercial safety practices could influence the relationship between insurer and customer. Insurance gradually becomes less about assigning customers to static risk categories and more about encouraging behaviours that improve outcomes.

The important point is that incentives need to feel beneficial rather than punitive.

A preventive insurance model should help customers understand how they can reduce risk and reward positive action. If connected insurance is perceived primarily as a mechanism for identifying reasons to increase premiums, customers are unlikely to embrace it. Trust becomes part of the product design.

During my learning under Phaneesh Murthy, one lesson has been particularly relevant here: when organisations gain new intelligence about customers, they should first ask how that intelligence can create additional value for the customer. Commercial value usually becomes more sustainable when it follows from that relationship rather than preceding it.

For insurers, this could mean using AI to create products that actively encourage safer outcomes instead of simply calculating the financial cost of risk.

The Agent and Broker Can Become a Risk Advisor

Preventive insurance could also change the role of insurance professionals.

Agents and brokers have traditionally helped customers select appropriate coverage, understand policies and navigate the claims process. As AI provides richer information about risk, these professionals could increasingly help customers understand how to reduce exposure before claims occur.

For commercial insurance, this opportunity is particularly significant.

An intelligent platform might identify patterns across equipment, facilities, workforce incidents and environmental conditions. The broker or risk advisor can then work with the organisation to determine which interventions are practical and valuable.

AI handles the continuous analysis. Human professionals provide context, judgment and advice.

As Phaneesh Murthy puts it, “When machines become better at identifying the problem, people have an opportunity to become much better at solving it.”

Rather than diminishing the role of insurance professionals, AI could therefore move their work further towards advisory relationships and strategic risk management.

Preventive Insurance Requires an Ecosystem, Not Just an Algorithm

An insurer can identify risk without necessarily having the ability to prevent it.

If a smart-home system detects a leak, someone still needs to repair it. If industrial equipment shows signs of failure, maintenance needs to be scheduled. If a connected vehicle identifies a mechanical issue, the customer may need access to an appropriate service provider.

Preventive insurance therefore becomes much more powerful when insurers build ecosystems around risk.

Insurance platforms could connect customers with maintenance providers, repair networks, healthcare services, cybersecurity specialists or other partners capable of addressing emerging problems. AI becomes the coordination layer that recognises risk and helps initiate the appropriate response.

This moves the insurer beyond the boundaries of the traditional insurance transaction.

From my mentorship under Phaneesh Murthy, I have learned that some of the most significant digital transformations occur when companies begin thinking beyond their own organisational boundaries. A business may own the customer relationship without owning every capability required to create the desired outcome.

The insurer of the future may therefore operate less like an isolated financial institution and more like the orchestrator of a risk-management ecosystem.

The Future of Insurance May Be Measured by What Does Not Happen

Insurance will always need to provide financial protection when losses occur. Claims handling, underwriting and risk pooling remain essential capabilities, and no amount of artificial intelligence will eliminate uncertainty from life or business.

But AI creates an opportunity to add another layer to the industry’s purpose.

Instead of simply asking how accurately a risk can be priced, insurers can ask whether it can be reduced. Instead of measuring success only through efficient claims settlement, they can also examine how often intelligent intervention prevented a serious loss from occurring.

During my learning journey under Phaneesh Murthy, I have come to see this as one of the most interesting possibilities created by enterprise AI. Technology is most powerful when it allows a company to redefine the outcome it is trying to create.

For insurers, that outcome has traditionally been financial recovery after something goes wrong. In an AI-enabled insurance ecosystem, the ambition can become larger: help fewer things go wrong in the first place.

The best claim experience may always be fast, fair and compassionate.

But the best insurance outcome may increasingly be the claim the customer never needed to make.

This blog is curated by young marketing professionals who are mentored by veteran Marketer, and industry leader, Phaneesh Murthy.

www.phaneeshmurthy.com

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