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Satellite, SD-WAN, and 5G are increasingly forming the backbone of unified, on-demand telecom services — especially in areas where coverage, resilience, and reach are non-negotiable. But orchestrating these services across different domains, networks, and providers raises big challenges: how to ensure seamless activation, SLA tracking, and service assurance in real time. 

That’s the broader focus of the TM Forum Catalyst SATCOM with an Edge – Phase III: enabling hybrid service delivery and cross-domain orchestration using TM Forum Open APIs and SD-WAN routing intelligence. 

Within that architecture, DigitalRoute plays a focused role: ensuring that the usage data generated by these services — including Call Detail Records (CDRs) and SLA-relevant metadata — is complete, contextualized, and ready to support monetization, audit, and service accountability.

The operational shift: From domain-centric to service-centric thinking 

Traditional OSS architectures evolved in vertically aligned environments. A service was provisioned, monitored, and billed within a single network stack. But now? 

• A drone command session may start over terrestrial 5G and fail over to satellite uplink. 
• A field technician may upload sensor data via SD-WAN backhaul with variable link quality. 
• A critical application may span multiple slices or operators, depending on location and load. 

In these cases, usage data doesn’t just move across systems — it crosses ownership boundaries, link types, and SLAs. And it often does so without centralized oversight in the moment it happens. 

Without a usage data mediation layer built to handle this, OSS/BSS processes lose sight of what’s being used, by whom, and under what conditions — with major implications for service assurance, charging, and partner settlement.

What breaks — and why it matters 

When service logic crosses domains, three common failure points appear: 

1. Loss of context 

Events collected from different sources often arrive without shared metadata. Who initiated the session? What QoS tier applied? What policy rules were triggered? 

2. Fragmented records 

Usage records from satellite and terrestrial domains may use different formats, timestamps, or sequencing — making it hard to assemble a clean session timeline for billing or assurance. 

3. Delayed or incomplete delivery 

When links drop or rebuffer, usage data may arrive out of order or not at all — creating invoicing or reporting gaps in billing systems or SLA monitoring tools. 

These are not theoretical risks. They are daily realities for operators delivering hybrid services. And they directly impact revenue assurance, compliance, and customer trust.

DigitalRoute’s role in the catalyst: Processing CDRs for monetization and SLA trust 

In SATCOM with an Edge – Phase III, DigitalRoute provides the usage data mediation layer that processes CDRs  generated by the 5G Core and related infrastructure. These records form the foundation for downstream monetization and SLA evaluation. 

Importantly, the CDRs originate from the terrestrial domain — specifically from the 5G Core provided by Bell — but they include metadata reflecting the broader hybrid service context. This includes information such as user identity, service class, and backhaul type, enabling usage data to support charging and assurance across both terrestrial and satellite environments. 

DigitalRoute ensures that this data is not only collected, but transformed into a revenue-grade asset: 

• Ingesting CDRs and usage-related metadata, including user ID, service class, and backhaul context 
• Normalizing and enriching those records, aligning with TMF APIs and applying relevant SLA or partner context 
• Delivering structured usage data to billing, assurance, and inventory systems — with ordering, completeness, and traceability guarantees 

This enables downstream systems — like those from Enghouse Networks, Oracle, and others — to treat hybrid service usage as a single, auditable flow, even when service paths are diverse.

What we mean by ‘revenue-grade’

Revenue-grade usage data is accurate, with no duplicates or missing fields. It is contextual, carrying service identifiers, location information, and policy metadata. And it is ordered, meaning it’s structured and sequenced for downstream use in charging, SLA assurance, or partner settlement.

That’s the kind of data required to reliably monetize services across domains.

Why this matters now 

For OSS and BSS leaders, the question is no longer “Can we connect the services?” It’s “Can we operate and monetize them in real time, across domains, with commercial integrity?” 

This catalyst helps answer that question — by showing how usage data mediation fits into a broader ecosystem of: 

• Service orchestration and SLA enforcement across hybrid networks 
• Policy-based routing and domain abstraction 
• Cross-domain usage data that enables billing and assurance 

In multi-domain service environments, where multiple operators collaborate to deliver a unified SLA-backed service, usage data becomes a shared source of truth. Usage data mediation ensures that each partner — terrestrial or satellite — can account for its role in the delivery chain, supporting transparent monetization, SLA tracking, and partner settlement. 

It shows that with the right usage data mediation layer in place; usage data becomes a trusted input to delivering commercially viable hybrid services.

Learn More  

If you’re working on usage-aware hybrid services or modernizing your OSS/BSS for distributed delivery, let’s talk. We’re happy to share insights from this catalyst and explore how usage data mediation can de-risk your cross-domain strategy. 

Schedule a meeting at DTW25 Ignite or Online

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This isn’t about KPIs or counters. We’re talking about call- and session-level trace data generated by the network itself — based on 3GPP trace functionality — providing real-time visibility into user-specific control and user plane behavior across 3G, 4G, and 5G networks.

Subscriber awareness has always existed — but what’s changing now is not the availability of this data, but the number of systems that depend on it — and the pressure on OSS teams to deliver it reliably, contextually, and in real time. Historically, subscriber and UE (user equipment) trace data, as defined in 3GPP, remained trapped in domain-specific tools with limited extensibility, weak integration capabilities, and high operational overhead. Today, with the rise of automation, service orchestration, and AI/ML, this data is no longer just diagnostic. It’s foundational.

This blog breaks down why subscriber and UE trace data is central to OSS evolution, what’s driving its growing importance, and how new approaches like the DigitalRoute Network Subscriber Trace Analysis solution make it operationally viable.

The New Reality: Subscriber Data Demand Is Surging

Why this matters:
In traditional OSS architectures, subscriber-level trace data was primarily used reactively — post-incident triage, customer care escalations, or KPI reporting. The tools that accessed this data were often probe-based, domain-specific, and disconnected from broader OSS and analytics environments.

Today, that landscape has changed. New, distributed systems — both within OSS and beyond — increasingly demand subscriber and UE centric data in real time, and for much more than troubleshooting.

What’s driving this trend:

• Service Assurance platforms need real-time subscriber traces to correlate network behaviors with end-user experience.
• AI/ML and anomaly detection engines require large volumes of contextualized data to train models that support predictive maintenance, self-healing, and dynamic service quality optimization.
• Policy and orchestration systems use live session-level data to adjust resources based on application usage, congestion, or SLA violations.
• Data lakes and observability stacks rely on granular, enriched data for historical analytics, trend forecasting, and reporting for both technical and business audiences.

How this affects OSS strategy:
Architects must now consider subscriber and UE trace data not as a specialized niche requirement, but as a shared utility — a data service that feeds multiple consumers. This introduces challenges around data architecture, integration, enrichment, and lifecycle management that legacy systems weren’t designed to solve.

Why Subscriber Data Is So Valuable

Why subscriber- and UE level granularity is critical:
Networks may be built from interfaces and nodes, but services are delivered to individual users. Subscriber and UE trace provides granular visibility into how these services behave at the device and session level.

This context is essential to:

• Detecting and resolving issues that don’t trigger alarms but degrade user experience
• Automating service assurance responses (e.g., rerouting, throttling, prioritization)
• Enabling cross-domain correlation, such as understanding how a core signaling event affects RAN congestion or user throughput
• Meeting SLA requirements in enterprise and wholesale scenarios by providing visibility at the session or flow level

What makes it unique:
Subscriber and UE trace data is not raw packet payload or aggregated KPI data — it provides enriched, session-level visibility into signaling flows, user-plane characteristics, QoS context, and, critically, into the RAN domain — which is often a blind spot for traditional probe-based solutions.

This level of visibility also bridges the gap between network operations and customer-facing teams — making trace data a strategic asset for aligning service delivery with experience expectations.

How it supports broader OSS evolution:
Modern OSS architectures are shifting toward intent-based operations, closed-loop automation, and AI-driven assurance. These models require structured, normalized, and enriched input data streams — which subscriber and UE trace data can provide when made available correctly.

The Problem: Traditional Monitoring Tools Weren’t Built for This

Why legacy tools are insufficient:
Most traditional monitoring tools — especially probe-based systems — were designed with a narrow scope: passive monitoring for fault detection and performance management. While some can deliver data in real time, they lack the ability to provide full subscriber and UE trace visibility, especially in the RAN, where interfaces cannot be physically tapped.

What the limitations look like in practice:

• Data silos: Each system consumes and stores data differently, preventing reuse across domains (e.g., data collected in the core can’t be easily used by RAN or customer care teams).
• Probe dependencies: Data collection is often hardware-tied and interface-specific, making scaling difficult and expensive.
• Rigid architectures: Data pipelines are static, and routing new data streams to emerging consumers requires manual integration and testing.
• Operational complexity: Maintaining multiple overlapping monitoring systems creates duplication and hinders centralized observability.

How this slows down innovation:
When data can’t flow freely to where it’s needed, efforts like service assurance automation, SLA reporting, and real-time experience tracking are delayed or derailed. Architecture teams spend more time on integration and troubleshooting than on enabling new capabilities.

What about next-gen monitoring tools?
It’s true that some newer, software-based probe solutions offer more flexibility than their hardware-bound predecessors. These tools often support virtualization and cloud-native environments. But while they improve how data is collected, they still lack RAN visibility and typically operate as domain-bound monitors — limiting their ability to feed subscriber-level insights into multiple OSS, BSS, and analytics systems in real time. The real challenge isn’t just visibility or delivery — it’s building a data layer that transforms fragmented, domain-specific data into enriched, actionable subscriber insights across the OSS.

Enabling Subscriber Observability at Scale

DigitalRoute’s Network & Subscriber Trace Analysis (NSTA) is a modular observability solution designed specifically for modern OSS needs. Built on our proven telecom data mediation solution, NSTA makes it possible to collect, enrich, and route subscriber and UE trace data in real time—across 3G to 5G, RAN and Core, and into any number of OSS, BSS, or analytics systems.

By decoupling data access from legacy heavyweight infrastructure, NSTA helps telecom operators reduce cost, simplify OSS architectures, and deliver the data agility needed for automation, AI/ML, and real-time service assurance — all while integrating seamlessly with your existing environment. Learn how NSTA works and what it enables in real-world OSS deployments → Explore the NSTA solution.

Conclusion: A Strategic Shift, Not Just a Tooling Update

Why architecture teams should care now:
Subscriber and UE trace data is no longer just a troubleshooting asset — it’s becoming a foundational enabler for OSS agility, automation, and intelligence. As demand for this data grows across multiple layers of the OSS stack, so too does the need for a solution that makes it accessible, meaningful, and operational at scale.

What the opportunity is:
By implementing a capability like NSTA, OSS architects can create a data foundation that supports service-centric operations without disrupting existing monitoring systems. It’s not about replacing probes — it’s about elevating subscriber and UE trace data to a first-class citizen in your OSS design.

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They reach for easy models first — premium license fees, fixed subscriptions, bundled packages, or simply raising prices.

But AI is rewriting the script. According to our latest State of AI Monetization research, 68% of CFOs say AI is disrupting their current pricing and revenue models — and what worked yesterday won’t work tomorrow.

Monetizing AI requires something most companies lack: the ability to track usage moments in real time, meter units of value accurately, and entitle users based on their individual plans.

AI Has No Set Price Tag — It’s a Living Value Experience with Living Costs

AI isn’t a one-time product or a simple subscription. It delivers value in moments of usage — when solving problems, executing tasks, or driving better performance and outcomes. That’s when value (and revenue) is created. Miss those moments, and you miss the revenue.

In B2B software, the shift from perpetual licenses to SaaS was more than a pricing pivot. It was a revolution in how businesses earned — and grew. Monetizing AI follows a similar path, but with a critical twist: it comes with living, dynamically fluctuating revenues and costs.

Managing that requires a new level of dependency on high-quality, real-time usage data. In a traditional subscription model, it was enough to know who your active users were at the end of the month. You could charge accordingly. Simple.

But with AI, the reality is more complex. You can’t monetize what you can’t track. Tracking usage and outcomes, metering units of value (whatever they are), and enabling dynamic pricing must happen in real time — at scale, intelligently and automatically.

Yet today, only 18% of companies monetizing AI say they can meaningfully track usage and 29% say they AI monetization models do not work. The rest are flying blind — and gambling with their margins.

Usage-Based Monetization Is Nothing New

There’s nothing novel about monetizing based on usage, outputs, or outcomes. Entire industries have done it for years, pricing according to the actual value they deliver. AI monetization should learn from them and follow their lead.

• Streaming media companies don’t just count subscribers — they track engagement by show, device, and time of day, and monetize accordingly.
• Telecom and utilities moved from billing by minutes to billing by megabytes. Now they meter every device, every interaction, in real time.
• Logistics companies price not just by miles, but by real-time tracking, delivery attempts, and predicted delays.
• Cloud services and manufacturers price based on consumption, performance, and outcomes — not static inputs.

AI is heading in the same direction — fast.

If you’re still pricing AI like a static SKU or cost-plus product, you’re forcing yesterday’s logic onto tomorrow’s reality.

Usage Optimization Platforms Are Setting New Standards

At DigitalRoute, we’ve spent 25 years helping the world’s most complex businesses — in telecom, SaaS, media, logistics, and more — to realize usage-based monetization innovations and models.

To us, AI products, features, and offerings are just the latest (and fastest-growing) source of usage data — data that, when processed correctly, can drive financial, commercial, and operational value and optimize that value generation constantly.

That’s why we built a new category: Usage Optimization Platforms.

We didn’t build our own usage optimization platform, Usage Cloud, because of AI hype. But AI has made it impossible to ignore now.

Usage data holds enormous, untapped value — and most companies underestimate it. To unlock that value, they’re still relying on data integration platforms, ETL pipelines, iPaaS tools, or homegrown systems that were never designed for this purpose.

Modern Usage Optimization Platforms must be natively connected to real-time usage moments. They must be automated, intelligent, and AI-driven — capable of powering a complete Usage-to-Value process. That means ensuring every AI usage moment is captured, formatted, and delivered to the right system — for monetization, product insights, or operational efficiencies.

But again, this is not new. Many industries are already doing it:

• Streaming companies use it to optimize personalization.
• Media companies to manage partner settlements and royalties.
• Logistics leaders to optimize deliveries.
• Telecom giants to automate operations.
• Advanced manufacturers to sell outcomes, not just machines.
• SaaS and software vendors to optimize offerings.

We might say: the next age of digital business runs on usage data. And AI monetization demands it.

Why You Can’t Wait

The pressure is real:

• 72% of companies say outdated monetization models are the biggest barrier to monetizing AI features.
• 67% of CFOs want better real-time visibility into product usage.
• 61% fear they’re underpricing AI-powered features.

And 64% agree: the hype is over. Their boards and executives no longer want AI adoption and innovation. They want results. They want monetization.

The companies that win won’t be the ones with the flashiest AI demos — they’ll be the ones that treat usage data as a core business asset, not just a reporting afterthought.

The Bottom Line

AI isn’t a static product you price once like a traditional SKU and move on. It’s a dynamic, evolving experience — constantly learning, adapting, and creating value with every moment of usage. And every one of those moments is an opportunity for revenue.

If you can’t see it, optimize it, and monetize it — someone else will.

At its core, every AI monetization challenge is a data challenge. Directly or indirectly, that’s what it comes down to. You don’t have to take our word for it — we’ve seen it firsthand, time and again, when we’re called in to turn around struggling projects.

At DigitalRoute, we help companies shift their perspective from “what we sold” to “what customers actually use and gain.” Because it’s only fair to charge where the value is — based on what users actually use or get, not on what they don’t.

Download the full State of AI Monetization report

Book your 45min free Executive Briefing session

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AI Trends Impacting O2C in 2025 

A new year brings a fresh wave of innovation, and 2025 is no exception. Artificial intelligence (AI) remains at the forefront of this change, reshaping how businesses operate. “To stay relevant, every company needs to embed AI into their offerings in some way,” says Andreas Zartmann, CEO of DigitalRoute.

But what does this mean for Order-to-Cash (O2C) processes? How will AI drive revenue and streamline daily operations? I spoke with DigitalRoute experts across Product, Marketing, and beyond to explore the key trends to watch.

Monetization and Adoption of AI Features in Modern Business Models 

Gaurav Dixit, Head of Data and AI Products, says that predictive AI and Generative AI are helping businesses improve their O2C processes with data-driven insights.

“AI also enables the democratization of business and pricing model experimentation and rollout.”

These advancements help businesses explore new monetization and pricing strategies, moving away from strict systems to create solutions that better fit their customers. By using AI, companies can build flexible, scalable processes that improve efficiency and support future growth.

Usage-Based Billing and Business Models in Revenue Generation 

AI is driving a shift from traditional fixed pricing to dynamic, usage-based models that put customers in focus. Companies are increasingly leveraging usage data to optimize these models. Stephen Hateley, Head of Product and Partner Marketing, shared:

“The expected growth of AI/Gen-AI adoption by tech companies will have them prioritize and expedite the introduction of usage-based business models.”

This shift is changing how businesses generate revenue, aligning pricing directly with usage. The outcome? More flexibility for customers, better personalization, and scalable growth for companies ready to embrace these models. 

Emphasis on Cybersecurity 

The rise of AI highlights the urgent need for strong cybersecurity. According to Liubov Koreva, SRE Manager:

“Companies must protect customer data, ensuring it is both accurate and secure. Inaccurate data can result in poor AI predictions, flawed decisions, or legal risks. Data accuracy and strong protection are critical for long-term success.”

When looking ahead, Liubov expects a cybersecurity-first strategy featuring robust frameworks like Zero Trust models and leveraging threat intelligence at the intersection of AI and cybersecurity.  

Jonas Wallenius, Strategic Product Manager, also emphasized the need for robust cybersecurity:

“Data privacy and isolation in multi-tenant environments with GenAI will become a big deal, discussion point, and regulatory hassle.”

As AI adoption grows, protecting sensitive data will be crucial for maintaining trust and staying compliant with regulations. 

How AI, Data Monetization, and Usage Data Shape Business Strategies and Revenue Generation

Let’s shift focus to revenue generation—how are AI, data monetization, and usage data influencing business strategies and driving revenue in the near future? I asked the team for their thoughts. 

Mediation: The Backbone of AI-Driven Business Models 

Demed L’Her, Chief Technology Officer, emphasized the importance of mediation in this process. 

“It might be counterintuitive, and you could argue it’s “old” technology since it’s been used in telecoms for ages. But consumption and hybrid models are becoming an imperative in 2025 and everyone seems to have forgotten that there is no consumption-based billing possible without strong mediation. It’s a problem that has to be solved first.” 

In the context of AI, data mediation is essential for transforming raw data into a standardized format. This process takes usage data, product details, service quantities, and timing, and structures it in a way that billing systems can recognize and use. According to Andreas,

“AI-powered services must be usage-based because costs arise as soon as customers start using the service. Usage data management (UDM) is the missing link in most Order-to-Cash processes, and for companies to scale their monetization, it becomes a strategic necessity.” 

Predictive Analytics: Driving Revenue Growth with AI 

Predictive analytics will be key to AI-powered revenue generation. AI-driven analytics will help businesses spot trends, improve operations, and find new revenue opportunities. Liubov explains:

“AI predictions and anomaly detection will become a must-have in the near future.”

Diksha, Product Owner at DigitalRoute, adds:

“Companies know that they need to change strategy and business models but there is hesitance because it may impact the revenue. AI will help them respond faster, using predictions to adjust strategies and drive revenue and growth.” 

How AI is Transforming Daily Work at DigitalRoute 

Andreas, CEO: “Tangible productivity improvements. This differs from everyday applications, ie we will see large shift in areas where we adopt AI.” 

Demed, CTO: “There are a lot of non-value added administration tasks in our daily lives that take a lot of our time and can easily be solved with AI. Meetings scheduling. Notes taking. Action items and summaries. The tools to solve these menial tasks exist and will become mainstream in 2025. And then there are the tools to help us in product design and development (prototyping, dev co-pilot, etc.) – that’s where the main value lies.” 

Liubov, SRE Manager: “AI Automation, but only when the consequences of mistakes are less costly than the expense of human effort. Changes in AI regulations will impact the company’s governance strategies.” 

Gaurav, Head of Data and AI Products: “Productivity and new skills. By the latter, I mean that now I can do the other functional tasks which I was 100% reliant on others to do, to a decent stage myself.” 

Jonas, Strategic Product Manager: “Planning, risk analysis, prototyping/product experiments will speed up by 10x or more, but there will also be 100s of gold-digging tool vendors that don’t add that much value. Must verify value in each company’s context.” 

Diksha, Product Owner: “Repetitive tasks will be automated. There will be tools in market to do work quickly, like creating UX mocks with simple instructions. Competitor analysis will be much faster and accurate.” 

Stephen, Head of Product and Partner Marketing: “Gen AI has undoubtedly boosted production of marketing assets and massively assisted in strategic subject research in the absence of expensive research papers and contracts.” 

Looking Ahead: The Role of Agentic AI in O2C

While predictive and generative AI are already transforming O2C processes, agentic AI is poised to take things even further. These autonomous agents have the potential to greatly streamline and automate operations. As Demed explains,

“Agentic AI is the ultimate goal in the O2C space: agents that can generate quotes, contracts, invoices, etc. Technology is ready—or close to being. However, the stringent regulations in the space probably mean that the rollout of autonomous agents will take a bit longer.”

Although widespread adoption may be delayed due to regulatory challenges, agentic AI will ultimately reshape O2C operations, offering businesses even greater efficiency and scalability.

Embracing AI for a Smarter Future 

From predictive analytics to usage-based billing and enhanced O2C processes, AI is fundamentally reshaping how businesses operate and generate revenue. At DigitalRoute, we see AI as more than a tool—it’s a transformative force that’s enabling smarter strategies, better customer experiences, and more scalable growth.

As we navigate this evolving landscape, one thing is clear: the businesses that succeed will be those that embrace AI not just as a technology, but as an integral part of their strategy.  

Enjoyed this blog post?

Check out our podcast on AI-Powered Usage Data Management, featuring Gaurav Dixit, Head of Data and AI Products.

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Businesses are generating more data than ever before. Yet, many struggle to extract meaningful insights. To stay competitive, companies are increasingly turning to artificial intelligence (AI) to unlock the hidden potential within their data. 

One area where AI is making a significant impact is usage data management—the process of collecting, interpreting, and utilizing data about how customers interact with products or services. 

In the latest Data for Subscriptions podcast episode, we spoke with Gaurav Dixit, Head of Data and AI Products at DigitalRoute. Gaurav shared valuable insights into how AI is revolutionizing the way businesses manage usage data, particularly in optimizing processes like the order-to-cash (O2C) cycle. 

In this blog, we’ll explore the key takeaways from that conversation, including: 

The importance of structured, high-quality data 

How AI delivers granular insights and enhances forecasting

Two powerful AI use cases in usage data management: anomaly detection and usage forecasting

Understanding Usage Data 

Usage data refers to any data about how a product or service is used, spanning industries like software, cloud infrastructure, telecommunications, and more. This data includes everything from: 

• Computing resources (like data and connectivity) 

• Physical telemetry (like distance travelled) 

• Product consumption (like materials, ingredients and chemicals) 
• Service utilization (like licenses, software features and support) 

Usage data captures detailed information about how customers engage with products or services, such as: 

• Consumption metrics for SaaS and subscription models (e.g., number of active users, data storage) 

• In-app analytics and engagement tracking (e.g., feature usage, session length) 

• Itemized phone bills showing call durations and data consumption 

• Electric vehicle (EV) charging metrics (e.g., charging times, energy usage) 

• Consumption data from metered utilities like electricity or water 

AI takes this data to the next level by transforming raw numbers into actionable insights. While usage data is valuable for improving customer experiences and operational efficiency, AI’s ability to uncover hidden patterns makes it a game-changer. 

Why AI is a Game-Changer in Usage Data Management 

AI isn’t just another buzzword—it’s a tool that helps to proactively solve problems before they escalate, enabling smarter, data-driven decisions.  

As Gaurav explains, “the key advantage of incorporating AI into usage data management lies in its proactive nature. AI predicts and helps resolve issues before they turn into costly problems.” 

Here’s how AI transforms traditional usage data management: 

1. Data quality: Ensures data cleanliness and reliability for accurate insights

2. Efficiency gains: Reduces manual intervention and operational costs

3. Predictive power: Identifies and addresses issues before they escalate

By adopting AI-driven solutions, businesses unlock efficiencies, enhance customer satisfaction, and improve decision-making. 

Building the Foundation: AI-Ready Data

The starting point for any AI initiative is high-quality data. Gaurav stresses this: 

“AI-ready data is hygiene—it’s essential to get started.” 

Without clean, organized data, AI cannot deliver reliable insights. Think of it like building a house: without a solid foundation, everything else crumbles. 

To achieve AI-ready data: 

• Regularly clean and validate data to eliminate errors

• Standardize formats and structures for consistency across systems 

• Leverage automated tools for data preprocessing to save time 

When businesses prioritize data quality and avoid dirty data, they establish a foundation for AI to deliver accurate predictions and valuable insights.

The Power of Granular Data 

Granular data—detailed, unaggregated information—is a treasure trove for businesses. As Gaurav notes: 

“Usage data is a very rich and important source of signals.” 

Accessing granular data in real time allows businesses to: 

• Detect trends and patterns early 

• Make more precise decisions
• Gain deeper insights into customer behavior

Gaurav uses Legos as an analogy.

“Granular data is like smaller Lego pieces. When combined, they can form something complex that bigger pieces could not.” This level of detail empowers businesses to understand their operations and customers at a much deeper level, enabling smarter, more informed actions. 

Unlocking Hidden Insights with AI 

Once businesses have clean, structured data, AI can extract hidden insights from granular usage data.

AI can reveal things such as:

• Sudden spikes or drops in usage
• Early indicators of customer churn
• Unresolved support issues

By identifying these patterns, businesses can improve customer engagement, address problems early, and uncover new opportunities for growth. 

Key Applications of AI in Usage Data Management 

1. Anomaly Detection 

Anomaly detection is the process of identifying unusual patterns or deviations in data that may indicate issues like fraud, equipment failures, or customer dissatisfaction. 

“If you can detect anomalies in real time and fix them early, that’s the best thing that can happen,” said Gaurav.  

Gaurav brought the power of AI to life with a real-world example: electric vehicle (EV) charging stations. 

“Imagine an EV charging network spread across hundreds of locations. Each station generates streams of data—from the amount of energy consumed to the duration of each charge. But what happens when something goes wrong?” 

This is where AI steps in. By monitoring charging data in real time, Gaurav explained that AI can detect unexpected patterns, like sudden surges in energy usage or irregular charging times. If anomalies occur, the system immediately flags them as potential issues, such as malfunctioning equipment or even fraudulent activity. 

The benefits of using AI for anomaly detection are clear: 

• Address operational failures quickly: AI helps businesses identify and resolve equipment issues before they escalate, minimizing costly downtime. 

• Prevent fraud: Detecting unusual usage patterns early protects financial resources and ensures fair billing. 
• Ensure smoother operations: With fewer disruptions, companies deliver a better experience for customers. 

“By spotting these anomalies as they happen, businesses can stay one step ahead—fixing issues before customers even notice them. It’s a game changer for operations and customer trust.” 

With clean, reliable data feeding these AI systems, companies can unlock their full potential—turning raw information into actionable insights that drive real-world results. 

2. Usage Forecasting 

Another powerful use case Gaurav mentioned was usage forecasting.  Usage forecasting uses historical data to predict future consumption patterns. This allows businesses to better plan resources, pricing, and operations. 

“Usage forecasting allows companies to optimize inventory, adjust pricing models, or scale operations based on predicted demand levels,” Gaurav explained. 

Take, for example, utilities. By forecasting usage, they can prepare for high-demand periods, ensuring they don’t run into shortages or overproduce. Similarly, subscription services can use forecasting to adjust their pricing models based on predicted customer behavior, ensuring they stay competitive and aligned with customer needs. 

Usage forecasting can help businesses:

• Optimize Inventory: Plan for demand fluctuations without overstocking or understocking 

• Adjust Pricing: Fine-tune pricing strategies based on expected customer behavior

• Scale Operations: Adjust resources to meet predicted demand levels 

By predicting trends in advance, businesses can reduce waste, improve efficiency, and align their strategies with what’s coming—leading to more informed decision-making and smoother operations. 

Case Study: AI to Optimize Data Management for Utility Companies

To highlight the true value of AI, Gaurav shares a real-world case study example from a utility company.  

The Challenge 

A utility company managing hot water and electricity consumption faced significant hurdles in leveraging its vast usage data effectively. Despite having advanced tools in place, the company struggled with predicting issues early and relied heavily on manual interventions to address problems. These inefficiencies led to revenue leakage and bottlenecks in their Order-to-Cash (O2C) process, including: 

• Delayed Invoices: Inefficient workflows caused invoicing delays, affecting cash flow

• Billing Errors: Inaccurate bills slowed collections and frustrated customers
• Inaccurate Data: Poor data quality hindered timely decision-making and reporting

The Solution

DigitalRoute partnered with the utility company to develop a customized solution, transforming their O2C process. By leveraging historical data and advanced techniques like predictive modeling and anomaly detection, he team drove significant improvements. 

As Gaurav explained: “We could see anomalies on the 15th of the month rather than waiting until the 31st, rather than spending five days with five people working 12-hour days. This manual effort caused an average delay of nine days per year in issuing invoices, leading to higher Days Sales Outstanding and revenue leakage.” 

The solution helped the company: 

• Identify issues earlier: Anomalies were detected by mid-month, reducing the need for manual fixes

• Ensure timely and accurate invoicing: Proactive measures minimized delays and prevented revenue leakage 

• Streamline operations: Automated workflows reduced manual effort, saving significant time and resources 

The Results

As a result, the utility company improved cash flow, decreased DSO, and gained confidence in their data-driven decision-making, leading to substantial savings. As Gaurav noted,

“For a typical $100 million revenue U.S. company, they saw net saving in the range of 1.5 to 2 million a year just by doing this. So, it kind of pays for itself even before it gets started.” 

Key Features that Made the Difference: 

1. Data-Driven Insights: Advanced machine learning techniques, like anomaly detection models, identified billing issues early, saving time and resources. 
2. Iterative Data Testing: Starting with three months of data, the team refined models to account for seasonal trends, ensuring predictions grew increasingly accurate. 
3. Business Value Validation: Frequent testing and validation demonstrated how proactive issue detection could save millions annually. 

The Human Factor

The primary challenge wasn’t the technology but integrating it into the customer’s existing workflow. Effective change management was crucial. As Gaurav noted, “The bigger part is how do we integrate it into the business flow? Technology is useless if it’s not used.” 

Conclusion

This use case highlights the power of co-innovation in solving real-world challenges. By working closely with the customer and utilizing data-driven insights, we provided a solution that reduced manual effort, saved costs, and ultimately drove measurable business value.  

Wrapping Up: The Transformative Power of AI

AI is revolutionizing usage data management, offering businesses the ability to: 

• Detect anomalies in real time 

• Accurately forecast future usage 

• Make data-driven decisions that drive growth and improve efficiency 

As Gaurav wrapped up, he emphasized the importance of proper data preparation: 

“Invest in AI-ready data, and you’ll realize the transformative potential of this technology.” 

By prioritizing AI-ready data, companies can unlock transformative potential and stay ahead of the curve. The real question isn’t whether to adopt AI—it’s how quickly you can integrate it into your strategy.

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