IT professionals and infrastructure technologists have all been living the “AI Dream” for more than 5 years, but what I find through discussions with lots of customers and analysts is that many of the Enterprises’ understanding about the HOW and WHY of using AI is inchoate (bonus points if you already knew what that word meant, hint: it combines the ideas of “under developed” and “unorganized”). In fact, I was on a Dell’Oro webinar this week and one of the real-time survey questions asked the audience about the stage of in-house AI deployment and the results showed two-thirds of all attendees were NOT building out ANY AI infrastructures themselves – yet. These Enterprises were dabbling in the use of AI, trying to find a problem to solve, piloting new exotic ideas in labs, and in a few cases buying AI from a service provider for limited and bounded projects.

So, I did some further research (using several AI engines of course, LOL) and converged on a “Pedestrian Friendly” list of the Top-5 common business use cases for AI that CAN and SHOULD be on every CIO’s adoption agenda NOW. These Top-5 uses are not fabricated or concocted ways to use the shiny new “AI” toy, but real business applications that can and should be turbo-charged using AI to boost revenues, valuations, customer retention, and customer satisfaction. (And in fact a handful of F100 companies are doing so already.)

It’s clear from the list below that AI can help Enterprise businesses who WANT to re-tool their very core operational approaches leveraging AI will be able to demonstrate and maintain leadership in their respective industries. (And those that do NOT leverage AI will find themselves at a competitive disadvantage).

These Top-5 business applications are: 1) customer service, 2) fraud detection, 3) supply chain optimization, 4) predictive maintenance, and 5) personalized promotion and marketing. (It should be noted that today, these requirements are already being addressed through a combination of manual, rule-based systems and early, less adaptive experiential models which I will discuss below)

1. Customer Service

Immediate AI inferencing benefits:
AI inferencing enables conversational AI and chatbots to provide instantaneous, personalized, and context-aware customer support. By analyzing and understanding customer sentiment in real time, AI can automatically route complex issues to human agents while resolving simpler requests autonomously. This reduces wait times and frees human staff to focus on more complex, high-value tasks. The key here is the AI models are part of the data fabric that makes up an enterprise. Customer detail, case context, product and solution details, shipping and logistics and other very specific details are part of the knowledge the AI refers to. So, when a customer interacts with a AI based Customer Service process, it brings more value than a human could ever bring since the AI instantly has access to everything needed to answer a question or create a solution.

How Customer Service needs are addressed without AI:

1. Rule-based chatbots: Most basic chatbots today use pre-defined scripts and decision trees, which can only handle predictable requests and fail on more complex or nuanced questions. This is a fairly generic experience and is treated as “the dreaded bot hell” experience such by customers.
2. Manual routing: Many companies rely on human agents to manually assess and triage incoming customer inquiries, which can lead to delays and inefficiency. In most cases, many human agents need to be involved since information is siloed. In most cases these human agents are still script based so their ability to zig and zag suffers, and customers become frustrated regularly.
3. Limited personalization: Support may be personalized based on a customer’s basic profile (e.g., name and purchase history), but it lacks the real-time, behavioral context that an AI can provide. While a human agent may know what was purchased, they still fail to understand the context of why it was purchased, and the nuances of who the customer is. Again, lipstick on a pig kind of thing.

2. Fraud Detection

Immediate AI inferencing benefits:
AI inferencing enables real-time transaction monitoring and anomaly detection to identify fraudulent activity with far greater accuracy than traditional methods. Using machine learning, it can analyze vast datasets to spot subtle behavioral patterns and flag emerging fraud tactics that rule-based systems would miss. This helps prevent financial loss and protects customer security. And the key here is the anomaly analysis can occur continuously and in near-real time based upon a growing base of what ‘normal’ looks like. The AI model automatically learns what normal is as it is used, and then flags further attention when deviations are detected.

How Fraud Detection needs are addressed without AI:

• Rule-based systems: Current systems often rely on hard-coded rules (e.g., flagging transactions over a certain dollar amount or from a new location). While effective against known threats, these systems are easily bypassed by new, sophisticated fraud schemes. Fraud that occurs at a single time, or one that mimic normal behavior is easily missed when hard-coded systems are in place, but AI based detection has a much larger and longer-term foundation of normal to draw from.
• Post-facto analysis: Much of today’s fraud investigation is reactive. Human analysts review flagged transactions after they have occurred, which is too late to prevent financial loss. And the manual process burden based upon using human investigators is significant in time, cost and scale. AI investigation can comb through mountains of case detail, to determine what makes sense and reduce this human-intervention by a factor of 10 or more.
• High false-positive rates: Legacy rule-based systems often generate a high number of false positives, which can inconvenience legitimate customers and create unnecessary work for human review teams. Again, AI reduce the false positive rates, and increases productivity and customer retention, while decreasing costs and risk.

3. Supply Chain optimization

Immediate AI inferencing benefits:
For large manufacturing based organizations, Supply Chain is everything. Every component or finished good has a lifecycle. AI inferencing provides highly accurate, real-time demand forecasting and logistics optimization for the entire lifecycle by analyzing market buying and demand trends, manufacturing production patterns, expected shelf time, and real-time transportation data of materials in and out. This enables companies to predict potential supply disruptions, manage production schedules inventory more efficiently, and optimize delivery routes, etc.

How needs are addressed without AI:

• Historical forecasting: Businesses typically rely on historical sales data and seasonal trends to forecast demand, which is slow and can’t adapt to sudden market shifts or component issues. AI is essentially real-time, using historical data as well as macro customer and supplier trends to determine what is needed and when, creating a supply chain plan to meet these needs.
• Manual route planning: Logistics managers often plan materials manually or with outdated siloed software that cannot incorporate real-time variables like changing customer requirements, global/macro economics, shortages, etc
• Reactive inventory management: Inventory management is frequently transactional, reactive, leading to either stockouts or overstock situations that increase costs, reduce customer retention, lost revenues, etc

4. Predictive Maintenance

Immediate AI inferencing benefits:
By analyzing real-time sensor data from industrial equipment, AI inferencing can accurately predict mechanical failures до they happen. This allows for preemptive repairs, minimizing expensive and disruptive unplanned downtime and extending the lifespan of critical assets. As the data grows, AI becomes more accurate at suggesting actions that should be taken proactively.

How needs are addressed without AI:

• Preventative maintenance: Maintenance is often scheduled on a fixed calendar, whether equipment needs it or not, leading to unnecessary inspections and costs.
• Rules-based anomaly detection: Legacy systems use rules based on standard performance thresholds. AI goes beyond this by identifying subtle deviations from normal operation, which are often the earliest indicators of an issue.
• Reactive repairs: Many organizations still practice a “run-to-failure” model, where equipment is repaired only after it breaks down. This leads to severe, costly, and disruptive downtime. AI literally predicts failure and suggests the actions to be taken in advance. And with Agentic AI, those maintenance tasks can be dispatched automatically.

5. Personalized Promotion and Product recommendations

Immediate AI inferencing benefits:
AI inferencing analyzes customer and prospect data, browsing patterns, external related social conversations, and all other digital sources in real-time behavior to deliver hyper-personalized product recommendations, content, and dynamic or promotional pricing if needed. In fact, demographically valid prospects that show high buying intent via their own trackable actions can be treated entirely different than those that are unengaged. This significantly increases customer engagement and conversion rates by presenting the right offer to the right customer at the right time.

How needs are addressed without AI:

• Broad segmentation: Marketing teams often segment customers into large groups based on basic demographics, a process that is far less precise than individual personalization.
• Recommendation engines: While modern recommendation engines already use machine learning, AI inferencing can take this further by analyzing a wider range of data points—including sentiment and real-time context—to create more relevant, dynamic recommendations.
• Manual campaign management: Marketers traditionally create and manage static marketing campaigns, which are not adapted in real-time based on customer interactions.

So, the long and short of it… AI is here now, it’s here to stay, it will alsay get better over time but is already production quality, and it can easily be consumed doing real business functions better than you are doing today. Period. And yes, while AI technology seems to have come from nowhere just half a dozen years ago, it is maturing at break-neck speed and is already delivering huge results for those Enterprise adopters that WANT to leverage it. Like many of the historical macro tech trends which seemed like science fiction initially (e.g. Linux, WiFi, Internet), there is no need to wait for AI to be ‘done’. AI can be deployed easily, and huge value can be realized, today! The old adage applies perfectly, “Those that Wait, Want”

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