Bridging the Visibility Gap in Modern CPG Supply Chains

India’s CPG market is growing faster than most supply chains can handle. Valued at approximately USD 245 billion in 2024, it is projected to reach around USD 1.1 trillion by 2033. This growth exposes operational cracks quickly.

A 2018 KPMG India study highlights a key issue. More than half of Indian organizations still lack end-to-end supply chain visibility. This leaves planners, sales, and logistics teams with partial or outdated data, causing drifting forecasts, delayed inventory decisions, and reactive operations.

Indian retail structure complicates matters: Kirana stores coexist with modern trade chains and online marketplaces, each with different behaviours and data reporting. Unifying this into real-time insights is essential to reduce demand volatility and excess stock.

Today, supply chain visibility in India is no longer an operational nice-to-have. It determines how quickly a business can respond to change, protect margins and stay relevant. The logic is simple and familiar to anyone who has run operations. If you cannot see what is happening, you cannot plan what to do next.

Key Impacts of Visibility Gaps:

• Forecasts based on incomplete data lead to 15-20% overstock in volatile regions.
• Stockouts or excess inventory contribute to 10-15% expiry losses in categories like beauty and dairy.
• Logistics delays and reactive fixes cost CPG firms millions annually.
• Reduced trust in data erodes decision-making confidence.

Where Visibility Breaks Down

In most CPG organizations, the issue is nota lack of data. It is that the data does not connect.

ERP systems (handling purchasing and inventory) often are isolated from DMS (tracking distributor sales and stock), varying by region and partner. This forces days of manual reconciliation.

This remains one of the most persistent challenges in supply chain visibility for CPG companies.

Demand variability makes matters worse. Seasonal and regional swings are part of daily reality. Hair oil is a good example. Demand rises in North India during winter but softens in humid southern regions. When visibility is weak, forecasting teams tend to smooth these patterns into averages. The outcome is predictable. Too much stock in some locations and stockouts in others.

Expiry and liquidation losses follow. Beauty and personal care brands such as Dabur and Emami lose significant value each year due to expired or unsold products. The root cause is usually the same. Batch-level inventory is not visible across the full distribution network, so risk is spotted only when it is too late to act.

Manual work compounds the problem. Many mid-sized FMCG companies still use Excel to manage returns, claims, and credit notes. These spreadsheets slow down finance teams and hide the true cost of serving each channel.

Tier-2/3 markets add opacity: Offline sub-stockists deliver late or incomplete sell-through data, forcing assumption-based planning.

Why Reporting Is Not Visibility

To cope, many organizations add reporting layers on top of existing systems. It helps with tracking, but it does not solve the core problem.

Excel-based trackers and basic BI dashboards cannot support CPG supply chain visibility at scale. They depend on delayed uploads and manual consolidation, which makes them unsuitable for fast-moving environments.

Inconsistent data definitions make things worse. When SKUs, locations, and distributor hierarchies are defined differently across systems, central reporting becomes a clean-up exercise. Overtime, confidence in supply chain data analytics erodes because teams no longer trust what they see.

Timing is another issue. Weekly or monthly updates force reactive behaviour. By the time a stock issue or excess inventory appears in a report, the financial impact has already occurred.

Summary: Reporting vs. True Visibility

Traditional reporting tells you what happened (e.g., a stockout occurred). True visibility reveals why it happened (e.g., delayed replenishment due to distributor delays or demand spikes) unlocking proactive, AI-era decisions that prevent issues before they impact margins.

Making Data Useful, Not Just Visible

The next stage of FMCG supply chain visibility starts with a simple principle. Data must be aligned before it can be acted on.

A single data layer that connects product, location, and time across the organization is now essential. Without it, teams continue to debate numbers instead of making decisions.

Modern solution services build on this foundation by applying intelligence. AI models analyze multiple signals together, including sales trends, inventory levels, expiry timelines, and broader economic indicators. This helps planners understand not just what is happening, but why it is happening.

Agentic AI goes one step further. Instead of waiting for manual intervention, the system initiates actions on its own. Replenishment quantities adjust when demand shifts. Expiry risks trigger early liquidation. Reconciliation issues are flagged automatically rather than discovered weeks later.

ADA’s CPG Supply Chain Intelligence solution embodies this approach, seamlessly connecting ERP systems (SAP,Oracle, Microsoft Dynamics 365), DMS platforms (Botree, Field Assist, Bizom),and ecommerce channels (Amazon, Flipkart, Blinkit).

ADA Capabilities Breakdown:

• Unifies disparate systems for real-time, end-to-end data integration across distributors and retailers.
• Enables regional Generative AI forecasting at distribution centers and distributor levels.
• Automates batch-wise expiry tracking, it is critical for dairy and beauty categories and to minimize losses.
• Streamlines reconciliation, reducing manual credit note validation.
• Optimizes inventory with replenishment recommendations for slow-moving or regional SKUs.

This enables Gen AI forecasting at both the regional distribution centre and distributor levels. It supports batch-wise expiry tracking, which is critical for dairy and beauty categories. Automated reconciliation reduces manual credit note validation. Inventory optimization logic provides real-time data integration for distributors and retailers, including replenishment recommendations for slow-moving or regional SKUs.

Together, these capabilities demonstrate how to improve supply chain visibility in FMCG environments that operate at scale.

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Conclusion

For CPG brands managing large general trade networks and thousands of SKUs, supply chain visibility is not about producing more reports. It is about control. When real-time supply chain data is unified and available across the network, teams stop reacting to problems after the fact and start managing the business with intent. Inventory decisions improve, expiry losses fall, and service levels become more predictable.

India’s digital ecosystem is accelerating this shift. UPI-scale infrastructure, ONDC integration, and broader ERP and DMS adoption are driving supply chain digitization, making organizations increasingly data-rich but still insight-poor. The companies that close this gap will be the ones that turn supply chain data analytics into timely, operational decisions rather than retrospective analysis.

Over the next few years, AI-led, self-correcting systems will move from pilots to standard practice. Stockouts, expiry risks, and delivery delays will increasingly be identified early and resolved through automated actions. This evolution will redefine FMCG supply chain visibility inIndia, but it still starts with a solid foundation of integrated data.

Key Takeaways for 2026:

●     Unify data for proactive decisions.

●     Leverage AI to cut risks by 20-30%.

●     Embrace digitization amid ONDC and UPI growth.

ADA supports CPG and FMCG organizations asa long-term solution partner in building that foundation. By enabling real-time data integration for distributors and retailers, ADA helps teams establish true end-to-end CPG supply chain visibility and act on it with confidence.

For organizations looking to improve forecasting accuracy, reduce inventory risk, and regain control across complex distribution networks, the next step is clear. Make the supply chain visible with a partner like ADA.

The New Demand Forecasting Standard India’s FMCG Giants Are Quietly Adopting In Their Supply Chains

India’s retail market is racing toward USD 1.4 trillion by 2026, but over 80% of FMCG volume still flows through general trade channels with almost zero digital visibility.

The result? Tens of thousands of  crores annually in stock-outs and excess inventory alone. (Nielsen-IAMAI 2024).

At the same time, AI adoption in supply chain digitisation in India is accelerating. The use of AI in supply chain management is growing by more than 30% each year, yet only a small share of consumer brands apply it effectively to their forecasting processes. Many continue to rely on fragmented datasets and static spreadsheets.

As the market matures, demand forecasting for FMCG will no longer operate as a quiet back-office function. 2026 will separate the category leaders from the laggards. The winners will treat demand forecasting as strategic intelligence, not a back-office ritual.

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Limitations of Current Forecasting Approaches

Traditional forecasting methods struggle to keep pace with the complexity of the modern CPG supply chain in India. Legacy systems were not designed for today’s speed, variety, and volatility. They often overlook important demand drivers such as inflation, heat waves, local events, search trends, and competitor pricing.

1. The static forecasting model fails to adapt

Many forecasting systems run in monthly or quarterly batches. These static models do not react quickly to rapid changes such as unexpected heat, festive pre-loading, flash e-commerce events, or supply disruptions. When demand moves faster than the planning cycle, the forecast becomes outdated before it is even applied.

1. Manual planning creates errors and version mismatch

A large share of planning teams still operate in spreadsheets. They run parallel versions of forecasts, apply judgment-based overrides, and circulate files through email. This causes version mismatches, delays, and manual errors. More importantly, it prevents organisations from building a truly data-first supply chain strategy.

2. Demand and supply planning remain disconnected

In many FMCG organisations, demand planning and supply planning continue to function as separate processes. Forecasts are created without visibility into real-time capacity constraints, production bottlenecks, or stock availability at different nodes. As a result, the forecast signal rarely aligns with operational realities. Supply teams then produce based on lagged shipment data rather than real demand. This increases stock-outs in some regions while building excess inventory in others.

These structural limitations create an environment where traditional practices cannot support the level of precision required today. They set the stage for the business challenges that follow.

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The New Standard for Forecasting

A new approach to forecasting has emerged, powered by AI in supply chain management and deeper data integration. Instead of fragmented, channel-specific datasets, modern forecasting relies on unified visibility from regional distribution centres through distributors, retailers, and finally consumers.

1. Multi-tier forecasting for end-to-end clarity

A multi-tier forecasting model creates a connected view across general trade, modern trade, and e-commerce. It aligns demand signals from the warehouse, distributor, retailer, and consumer levels. This provides a clearer and more accurate representation of market movement and reduces reliance on shipment-based approximations.

2. Gen AI enhances accuracy and responsiveness

Gen AI systems incorporate external datasets such as weather patterns, inflation indicators, macroeconomic reports, and competitor pricing trends. They learn from past patterns, including returns, out-of-stock periods, and promotional behaviour. The forecast adjusts dynamically, enabling more responsive planning even in volatile environments.

3. Data harmonisation as the foundation

A strong data foundation supports every modern forecasting approach. This includes harmonising data across ERP, DMS, CRM, and marketplace systems. A unified taxonomy allows consistent SKU-level and region-level prediction. It also allows brands to fully unlock AI use cases in the FMCG supply chain.

4. Agentic AI for autonomous monitoring

Agentic AI systems track anomalies in real time and prompt planners when deviations occur. They can trigger automated replenishment or initiate a review process when sudden spikes or drops appear. This reduces the burden on planning teams and supports a more agile planning cycle.

Together, these advancements can deliver improvements of up to 20 to 30 percent in forecasting accuracy, while reducing stock-outs and lowering inventory holding effort.

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The ADA Difference: We Start Where Everyone Else Skips

Most AI-driven forecasting solutions assume that data is already clean, connected, and reliable. In the reality of Indian FMCG operations, this is rarely the case. Disparate systems, inconsistent identifiers, and incomplete data streams are the norm, not the exception.

That is why every ADA engagement starts with building a strong data foundation. While often overlooked, this step is critical. It is the difference between a marginal improvement in forecast accuracy and a step-change impact.

One harmonised data spine

We stitch together every source you actually have like SAP, Marg, BeatRoute, Bizom, OkCredit, CRM, Amazon/Flipkart Seller Central, 3PL portals, even WhatsApp order screenshots into a single, consistent SKU–region–channel taxonomy. No more “Parle-G 82 g” appearing as 47 different names.

Agentic layer that only works on clean data

Once the foundation is rock-solid, the Gen AI and autonomous agents switch on detecting anomalies, adjusting promo lift, and triggering replenishment without human touch.

The brands we partner with don’t buy a tool. They get a co-built, future-proof demand-sensing backbone that becomes their single biggest competitive advantage.

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Conclusion

Demand forecasting is undergoing a permanent shift as FMCG and CPG brands move from reacting to market trends to anticipating them. As we look toward 2026, forecasting will evolve into a live data ecosystem where every distributor, warehouse, retailer, and channel feeds continuous insight back to the brand.

When forecasting becomes integrated, dynamic, and data-led, it delivers meaningful business impact. It reduces wastage, improves on-shelf availability, and accelerates decision-making. Brands that invest in stronger forecasting today will be better positioned to adapt, compete, and grow in a rapidly changing market.

To begin strengthening your forecasting capabilities, contact ADA. Our team works alongside brands to build connected, future-ready demand systems grounded in strong data foundations. If you’re ready to move beyond traditional forecasting and accelerate your shift to intelligent planning, ADA is here to help.

The New Inventory Optimisation Standard Indian CPG Leaders Are Building for 2026

Inventory is no longer a back-office “numbers exercise” of replenishment cycles; it is the strategic lever for unit economics in the 2025-2026 Indian market. In practice, it shapes some of the most important outcomes in a CPG business, from revenue protection and cash flow to service reliability and customer trust.

Even small inefficiencies add up quickly. Out-of-stock days can reduce potential revenue by 5% to 10%. At the other end of the spectrum, carrying too much inventory brings its own cost, typically 20%to 30% of inventory value each year once storage, handling, financing, and obsolescence are considered. Wastage compounds the issue further, with a meaningful share of stock lost to expiry, overproduction, or misaligned distribution.

The Death of the “One-Size-Fits-All” Model

In 2025, a singular inventory strategy is a liability. Leading CPG players are now adopting a Tri-Modal Inventory Orchestration approach to address the three distinct speeds of the Indian market:

• ‍The Q-Comm Sprint: Using AI to manage hyper-local dark stores where stock-outs are measured in minutes, not days.‍
• The Kirana Pulse: Leveraging AI to bridge the data gap in unorganized retail, predicting “Next-Gen” orders for millions of small shops.‍
• The Rural Reach: Optimizing long-haul logistics for Tier 3+ cities where infrastructure remains the primary bottleneck.

Understanding Inventory Optimization as a Capability

At its core, inventory optimization is about making informed trade-offs. It is the discipline of holding the right inventory, in the right locations, at the right time, while keeping cost and risk under control.

Rather than relying on broad buffers or fixed safety stock, inventory optimization uses data-led forecasting, reorder logic, and buffer strategies that reflect real demand patterns and supply constraints.This allows organizations to respond to variability with precision instead of excess.

When applied consistently, this approach improves cash flow by reducing unnecessary stock, while also supporting higher service levels. It also strengthens resilience. By understanding where inventory is exposed to risk, whether from long lead times, demand variability, or limited shelf life, businesses gain more control over outcomes. This is particularly relevant for inventory management CPG India operations, where scale, channel diversity, and distributor-led networks add complexity.

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Where Inventory Optimization Commonly Breaks Down

Despite its importance, many organisations struggle to optimize inventory in practice. A common issue is limited visibility into stock age and expiry. Without reliable batch, lot, and expiry tracking, FIFO and FEFO principles are difficult to enforce consistently. Inventory can age unnoticed across warehouses and distributors, leading to shortened shelf life, forced discounting, and write-offs.

Visibility challenges often extend beyond expiry. Disconnected systems across manufacturing, distribution, and retail make it difficult to see inventory holistically. Teams may know how much stock exists, but not where it is most needed or how quickly it is moving. This lack of clarity leads to stock outs and overstocking, a pattern that ties up working capital while still disappointing customers.

These inventory visibility challenges are rarely caused by a single failure. They are usually the result of fragmented data, manual processes, and decisions made without timely feedback from the ground.

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Why Traditional Approaches Struggle to Keep Up

Most legacy DMS, SFA, and ERP environments were designed to record transactions, not to continuously optimize decisions.They provide structure and control, but often lack the responsiveness needed in fast-moving, multi-channel environments.

In general and modern trade, this can result in replenishment cycles that are slow to adapt, forecast assumptions that drift from reality, and inconsistent OTIF performance. In ecommerce and direct-to-consumer channels, the challenge is compounded by the need to synchronise inventory across multiple systems, increasing the risk of a mismatch between available and actual stock.

These approaches tend to address issues after they occur. Without predictive inventory analytics and SKU-level demand forecasting, organizations are left reacting to outcomes rather than shaping them. Over time, this makes it difficult to reduce inventory cost CPG India businesses continue to carry while still protecting service levels.

The gap between legacy systems and the new standard is clear:

What a More Effective Approach Looks Like

A more effective approach to inventory optimization starts with a strong data foundation and a data-first mindset. This does not require replacing existing systems, but rather connecting and enhancing them so decisions are based on a shared view of reality.

With integrated inventory optimization systems, organizations gain visibility into batch-wise stock age and expiry, allowing near-expiry inventory to be identified early. This enables timely actions, such as adjusting distribution or triggering liquidation, before value is lost. Brands adopting these data-first, AI-driven systems are seeing expiry write-offs reduced by up to 30%, overall inventory costs drop 20–30%, and holding costs fall 15–25% (McKinsey, ToolsGroup, and industry benchmarks 2024–25). Slow-moving SKUs can be managed more deliberately through smarter purchase order decisions, reducing holding costs while improving cash flow predictability.

Replenishment also becomes more adaptive. Instead of fixed rules, AI-driven workflows learn from demand signals, lead times, and movement patterns. Orders adjust as conditions change, reflecting actual consumption rather than static plans. Assortment and stock movement decisions are continuously refined to balance inventory across locations.

AI in inventory management supports this process by enabling timely notifications and actions to flow back into enterprise systems through ERP integration forFMCG environments. This helps ensure that insights lead to execution, not just analysis, and strengthens data-driven supply chain optimization efforts.

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Conclusion

Inventory management in India CPG is gradually shifting from static, ERP-centred planning towards more dynamic, intelligence-led orchestration. Advances in demand sensing, multi-echelon optimization, warehouse automation, and generative AI for decision support are expanding what is possible.

In distributor-heavy markets such as India, these capabilities are particularly valuable. They help organizations manage complexity without relying solely on manual intervention or excess buffers.

By 2027, leaders will run near-autonomous networks; laggards will still chase expiry losses. The gap is widening now.

This evolution is not about removing human judgment. It is about supporting it with better information, clearer trade-offs, and faster feedback.

To strengthen your inventory optimization and supply-chain capabilities, contact ADA. Our team works alongside CPG brands and distributors to build connected, AI-driven inventory systems grounded in strong data foundations. If you’re ready to move beyond ERP-centric planning and accelerate your shift toward intelligent, autonomous inventory management, ADA is here to help.

From Weeks of Waiting to Second of Knowing

In every ASEAN enterprise, the same frustrating loop has been playing for years. Marketing or CX team: “Which customers in Indonesia are likely to churn this month?”

• ‍Sends email to data team​  ‍
• Data analyst pulls yesterday’s dashboard (already outdated)​  ‍
• Runs queries across 5 systems​  ‍
• Builds a PowerPoint​  ‍
• Sends to project lead → marketing lead → regional head​
• ‍Two weeks later, someone finally gets an answer​

​​→ By then, the customer has already left.​

That loop just died.​     ​

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The New Speed: Ask → Know → Act

What if, instead of waiting two weeks for a simple audience, any marketer could open a chat box and ask in plain English: “Show me customers in Jakarta who opened our last email but haven’t bought anything in the last 30 days.”

And 15 seconds later, get a clean, governed, ready-to-use list delivered straight back? No tickets. No back-and-forth with analysts. No outdated dashboards.

That better way is here today. It’s called Snowflake Intelligence, powered by Cortex, and it lives natively inside your existing Snowflake environment.

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How Snowflake Intelligence Works

1. Any authorised user like marketer, country manager, CX lead, or even the CEO can simply type or speaks a question in natural language.

2. The agent, fine-tuned with your company’s glossary and business terms, instantly understands the intent and local context.

3. It securely scans every table it has been granted access to.

4. Behind the scenes, it generates clean, production-ready SQL.

5. Seconds later, it returns the exact audience list or insight, fully governed, auditable, and compliant.

Insight that used to take weeks now takes seconds.

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Snowflake Intelligence: How enterprises are adopting it right now

1. ‍Activate the Intelligence Suite: Turn on Cortex AI, Copilot, Snowflake ML, and Document AI, and assign the right permissions in your Snowflake account.

2. Make Your Data Truly Intelligent: Ingest all enterprise sources and build clean semantic models so AI agents understand your exact business context (products, customers, campaigns, KPIs).

3. Deliver Instant Wins with Cortex AI Functions: Use built-in LLM capabilities like summarization, sentiment analysis, classification, translation, and embeddings, directly inside SQL. No code, no pipelines, immediate ROI.

4. Build & Run Production-Grade ML Natively: Train, validate, deploy, and monitor forecasting, propensity, or recommendation models entirely inside Snowflake. No data movement, no external clusters.

5. Launch AI Agents & Conversational Analytics: Create custom Cortex Agents for automated workflows and roll out Snowflake Copilot so every marketer, analyst, and executive can ask questions in plain English and get accurate answers in seconds.

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Why This Speed Is Only Possible with ADA + Snowflake Intelligence

1. ADA has already done the hard pre-work

We transformed raw transactional mess into clean, AI-ready customer spines long before Snowflake Intelligence arrived.

2. ADA’s enrichment lives natively in your Snowflake

No ETL delays, the ASEAN consumer graph, digital behaviour, and propensity models are already there, updated daily.

3. ADA closes the loop instantly

When Intelligence returns a governed segment, ADA can trigger the retention flow in minutes through pre-built, secure integrations. This isn’t an incremental improvement.

It’s the difference between managing your business with yesterday’s report and steering it with real-time, governed intelligence that immediately translates insight into customer action.

Get started in 30 Minutes, not 30 Days

We’ll connect to your Snowflake, let your team type real questions, and show the insight-to-action loop in action. Let’s kill the old loop together.

One API to Authenticate Them All: The Future of Frictionless Authentication with ADA Verify

Digital services require users to verify their identity at many points, whether logging in, recovering an account, completing a payment, or accessing sensitive information. These checks are important for security, but common methods often introduce friction.

OTPs sent through SMS or voice can be delayed, fail to deliver, or be exposed to manipulation. According to industry security guidance, such as the NCSC, SMS has increasingly become a weak link due to SIM swapping, social engineering, and vulnerabilities in telecom signalling protocols. Regulations are also changing, increasing the pressure on organisations to stay compliant. Meanwhile, users expect quick, smooth access and may drop off when the process feels slow or complicated.

Organisations face the same strain. High verification drop-offs reduce conversions, weaken satisfaction, and affect long-term engagement. Managing traditional OTP systems requires fraud protection, routing management, compliance work, and number pool maintenance, creating additional cost and complexity. These efforts become even harder as SMS delivery costs rise globally and carrier filtering grows stricter, issues highlighted across multiple industry analyses discussing the hidden cost of SMS OTPs.

These challenges have pushed many organisations to explore frictionless authentication, a modern approach that securely verifies identity in the background while reducing interruptions. The goal is to simplify the experience without compromising security.

This is the purpose of ADA Verify. It streamlines authentication by offering one API that supports intelligent, unified verification across channels, removing the need to manage multiple vendors or fragmented systems.

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What Is Frictionless Authentication?

Frictionless authentication validates identity quietly in the background, reducing manual steps such as entering passcodes. It aligns with modern identity systems designed to provide stronger security and a faster, easier experience for users.

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The Problem with the “Old” Standard

Traditional authentication methods, especially SMS OTPs and password-heavy flows, have become one of the biggest friction points in today’s digital journeys. Every added step slows users down, and multi-step authentication is still a major driver of drop-offs, with abandonment rates often reaching around 30% during sign-up or checkout.

Passwords remain one of the most painful parts of the experience. Nearly 7 in 10 people struggle to remember them, and 40% use more than 11 passwords across their daily digital life. This constant password fatigue adds friction, slows down onboarding, and erodes trust in the process.

When authentication feels like work, users leave. When it feels seamless, they stay,  and they convert. This is exactly why the old OTP-only standard struggles to meet the expectations of today’s digital consumer.

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‍The Friction Factor

According to Martechvibe, 60% of Users Abandon Transactions Due To Authentication Frustration. Every extra moment in a digital journey affects conversion. Multi-step authentication often causes noticeable user drop-off during registration or checkout. In today’s competitive market, losing a meaningful portion of potential customers right at the start is far from ideal.

The Cost of Fragmentation

From a product and engineering perspective, things aren’t any easier. Supporting global authentication often means dealing with a patchwork of vendors, one SMS provider for Southeast Asia, another for WhatsApp worldwide, and a few more for smaller regions. This kind of fragmentation creates integration headaches, inconsistent data, and higher costs due to inefficient routing and failover setups.

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How ADA Verify Redefines the Standard

ADA Verify does not replace an SMS gateway. Instead, it enhances your identity systems by offering one API that supports silent verification, intelligent fallback, and AI-driven routing. It acts as an orchestration layer that balances speed, security, and user experience.

ADA Verify evaluates every authentication attempt in real time to determine the fastest and most secure path available.

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1. The “Seamless” First Step: Zero-Friction Authentication

When a user begins authentication, ADA Verify first tries carrier-based verification using secure mobile network operator connections. This background cryptographic check uses the mobile data network.

No input needed, no switching apps, and no OTPs exposed to interception. The process is instant, allowing users to log in effortlessly.

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2. Intelligent Fallback and Orchestration

If silent verification cannot be completed, ADA Verify activates an intelligent fallback process. Instead of defaulting to SMS, it compares channels such as WhatsApp, SMS, Telegram, or Viber based on:

• Enterprise preferences
• User behaviour and preferred channels
• Regional communication patterns

This ensures the most reliable and familiar method is used for each user.

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3. Powered by AI-Driven Decisioning

At the heart of ADA Verify is ADA’s proprietary AI-driven orchestration engine, the “brain” that keeps everything running smartly. Static routing rules break when carrier routes change or when networks experience downtime. ADA’s engine is dynamic, continuously learning from millions of authentication attempts.

It analyses real-time signals, including delivery success rates, latency metrics, and conversion data. This continuous loop allows the engine to predict the optimal path for every attempt, resulting in delivery success rates that consistently exceed 90%,  a figure traditional providers struggle to match.

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Industries That Benefit From Frictionless Authentication

A wide range of sectors gain measurable value from reducing verification friction:

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Financial Services

Banks, fintechs, and payment providers rely heavily on secure digital identity. Faster authentication reduces drop-offs during sign-ups, transaction approvals, and account recovery.

E-commerce

Retailers and marketplaces use frictionless authentication to reduce cart abandonment and protect against fraudulent purchases, while keeping the buying journey smooth.

Telecommunications

Mobile network operators play a dual role in digital identity: they are both providers of the underlying technology and end-users of authentication solutions. By enabling or adopting frictionless verification, they can reduce fraud, strengthen customer trust, and unlock new identity-driven revenue opportunities.

‍Healthcare

Online portals, telehealth systems, and patient records require secure but accessible authentication to ensure both safety and usability.

Travel and Mobility

Ride-hailing, ticketing, and booking services benefit from less friction during account creation and high-risk transactions.

Digital Services and Apps

Any service with frequent logins or content access controls can achieve higher retention when identity checks run smoothly in the background.

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Beyond Authentication: The Future of Digital Identity

Digital identity is shifting from manual, user-driven actions toward background verification supported by secure networks and authenticated devices. Several trends are shaping the future:

• Security remains a top priority, especially for sectors dealing with transactions or sensitive data.
  
  
• User experience is increasingly important, as businesses link friction to customer churn.
  
  
• Passwordless systems are becoming more widely adopted, using biometrics, passkeys, and encrypted device-based verification.
  
  
• Telecommunications providers are expected to strengthen their role in authentication, using their network visibility to deliver secure digital identity services.
  
  
• Multi-channel verification continues to evolve, ensuring reliable backup methods when silent checks are not possible.
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Organisations are moving toward identity systems that work anywhere, anytime, with minimal disruption to the user.

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Potential Extensions in Frictionless Authentication

ADA Verify isn’t just solving today’s authentication challenges, it’s laying the groundwork for a complete Digital Identity Assurance Layer. Our roadmap looks beyond verification alone and tackles the wider set of identity challenges that businesses face.

Here’s what’s coming next:

• KYC & SIM Swap Verification

In future releases, we’ll tap into telco connections to enable real-time SIM Swap detection. Verify not just the device, but the actual identity behind the number. Prevent impersonation and account takeovers.

• Risk Scoring

Evaluate user trustworthiness based on behaviour, device patterns, and network signals.

• Persistent User Identity

We’re also working toward helping enterprises unify identity data across devices so the user on an Android phone and the same user on an iPad are recognized as one high-value customer.

As digital identity continues to evolve, ADA Verify provides a scalable, modern foundation that can grow with new use cases and future telco-driven features.

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Conclusion: A Strategic Advantage for Real Business Growth

Upgrading your authentication system isn’t just a technical move, it’s a strategic one. Customers expect every digital interaction to be fast, effortless, and secure, and frictionless authentication is how organisations meet those expectations. By reducing reliance on OTP-only methods and introducing silent, real-time checks, businesses can deliver smoother experiences, strengthen fraud prevention, and minimise operational strain. Industry studies make this clear: authentication is no longer just a security issue, it is a customer experience issue, directly tied to loyalty, abandonment rates, and brand trust.

ADA Verify brings all of this together in one unified solution. It gives modern businesses what they truly need: a single API that authenticates users seamlessly, without the friction, complexity, or hidden costs of traditional OTP systems. With Verify, you unlock easier onboarding, higher conversions, stronger security measures, and a scalable foundation for long-term digital growth.

Contact ADA today to integrate ADA Verify and turn your authentication process into a real competitive advantage.

Total Cost of Messaging Ownership: The Real Measure of Value in Business Messaging

Organisations tend to struggle with balancing the cost and effectiveness of their business messaging.

Now, procurement teams often find themselves comparing price-per-message rates or platform fees, only to realise later that these numbers do not reflect the true value or impact of their communication systems. Hidden inefficiencies, fragmented vendor management, and compliance risks can quietly offset what initially seems like cost savings. This gap has widened because business messaging is no longer a simple volume game, but now involves technology integrations, AI-driven customer journeys, and more complex operational requirements that introduce costs far beyond the message rate itself.

This is more than a budgeting issue. Business messaging plays a direct role in how customers experience a brand, how efficiently teams operate, and how reliably compliance is maintained. When messaging systems are evaluated correctly, they can become a strategic advantage that improves customer engagement, strengthens trust, and drives measurable returns. When handled poorly, however, even small inefficiencies can scale into significant operational and reputational costs.

For example, implementing a CPaaS (Communications Platform as a Service) solution typically means integrating it with your marketing automation, CRM, and billing systems, where each one adds its own integration cost well before the first customer interaction even happens.

To avoid these challenges, procurement leaders need a more complete view of messaging performance. When evaluations focus only on unit costs, it becomes difficult to see how communication systems influence wider business outcomes. This narrow approach often leads to short-term savings but long-term inefficiencies. And importantly, these inefficiencies can be felt immediately, not sometime in the future, thus making it critical for procurement teams to eliminate surprises and gain full visibility upfront. Staying informed through expert insights and modern evaluation methods is crucial for maintaining the optimal balance between cost, compliance, and return on investment.

True cost efficiency lies not only in reducing expenses but in understanding how each conversation contributes to business results. The Total Cost of Messaging Ownership (TCMO) framework provides this broader perspective, connecting cost, ROI, and compliance to reveal the real value behind every interaction.

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Understanding Total Cost of Ownership (TCO)

TCO is a framework that encourages organisations to look beyond the initial purchase price and consider the full cost of an investment over its entire lifecycle. This includes usage, maintenance, training, upgrades, and eventual replacement. By viewing ownership through this wider lens, businesses gain a clearer understanding of the true financial impact of their decisions.

The goal of TCO is to help organisations see the complete picture rather than rely on what appears to be the lowest-cost option today. This mindset leads to decisions that are more sustainable, efficient, and aligned with long-term growth.

This same principle applies directly to business messaging. The Total Cost of Messaging Ownership (TCMO) framework extends TCO thinking and reveals the full financial and operational implications of running messaging at scale.

Traditional cost assessments tend to focus on the most visible expenses, such as message rates or platform fees. While important, these reflect only one part of the investment. The TCMO framework expands this view with four key dimensions that together define the real cost of business messaging.

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Direct Messaging Costs

Direct messaging costs refer to measurable expenses like price per message and platform fees. These are often simple to calculate, yet they can become fragmented across regions, channels, and vendors. When organisations consolidate their messaging through a unified, global solution, contracting becomes more consistent and per-unit costs often decrease, creating a more manageable and predictable cost structure.

Indirect Tech Costs

Indirect tech costs arise from managing vendors, integrations, and ongoing technical support. These costs are frequently overlooked because they are spread across multiple teams and systems. Challenges such as coordinating different providers, duplicated integrations, or siloed data can inflate operational overhead. Using a single, integrated ecosystem for messaging, technology, and analytics reduces these inefficiencies and supports smoother day-to-day operations.

Scaling AI Costs

As organisations adopt AI to personalise experiences and automate journeys, the cost of scaling AI becomes increasingly important. Beyond AI model licences and usage-based pricing, hidden costs can arise through usage overruns, model retraining, or infrastructure sprawl. A unified AI platform helps simplify cost planning by providing predictable pricing and eliminating unexpected surcharges, making AI adoption more controlled and cost-effective.

Compliance Costs

Compliance costs relate to safeguarding data, meeting regulatory obligations, and ensuring proper reporting when incidents occur. In addition to hosting and data governance, organisations face the risk of fines, downtime, or audit-related disruptions. A messaging framework built on recognised certifications and regional regulations reduces this risk and helps maintain business continuity in environments where compliance expectations are high.

When these four dimensions are viewed together, procurement teams gain a more comprehensive understanding of their messaging ecosystem. The TCMO framework helps leaders look beyond surface-level cost comparisons and focus on how messaging contributes to both financial efficiency and business performance.

Procurement shouldn’t only measure the cost of messages but also the return generated from conversations. This perspective encourages organisations to uncover hidden costs early, prevent inefficiencies from scaling, and reinvest savings into areas that deliver long-term value. By applying the TCMO framework, leaders can ensure that every interaction contributes meaningfully to business objectives.

To put this into practice, organisations can start by assessing how they perform across these four dimensions. With tools such as the industry-specific TCMO Benchmark and ROI Simulation, procurement teams gain the clarity needed to make confident, evidence-based decisions and ensure nothing in their messaging strategy is overlooked.

Conclusion

The cost of business messaging should never be viewed in isolation. It is not simply about cutting costs but about uncovering how every interaction contributes to brand trust, customer satisfaction, and long-term business growth. By adopting a Total Cost of Messaging Ownership mindset, organisations can transform their business messaging from a basic operational expense into a measurable source of strategic value.

ADA’s trusted communication solutions help organisations eliminate hidden costs, simplify integrations, and gain instant clarity across their messaging ecosystem. With deep expertise in TCMO and a proven approach to uncovering real-world savings, ADA ensures businesses stay compliant, efficient, and fully in control.‍

If your organisation is ready to rethink its business messaging strategy, contact ADA today to discover how their proven services can help you build a messaging ecosystem that drives measurable results and lasting value.