Why AI Success Must Be Measured by Business Value, Not Technical Progress

Why AI Success Must Be Measured by Business Value, Not Technical Progress

As companies continue to increase investment in artificial intelligence, Kamal Yadav, Principal Data and Insight Analyst at Brambles, believes one of the most common mistakes organizations make is treating technical improvement as proof of AI success. Higher model accuracy, better data quality scores, faster processing, and newly delivered dashboards may all indicate progress, but they do not necessarily show that AI has created measurable value for the business.

According to his perspective, executive leaders evaluate AI differently from technical teams. Boards, CFOs, and commercial decision-makers are not only asking whether a model has improved. They want to understand whether AI has helped increase revenue, improve margins, reduce operational risk, strengthen customer experience, speed up decision-making, or release employees from repetitive work so they can focus on higher-value activities.

This difference is becoming more important as organizations move beyond individual AI pilots and begin scaling AI across the enterprise. Many companies can demonstrate that a model performs better in technical terms, but fewer can clearly explain how that improvement has changed business performance. This is where data and AI leaders need to change the conversation.

An improvement in forecast accuracy, for example, matters at the executive level only when it is connected to outcomes such as lower inventory costs, better service levels, fewer stockouts, or improved working capital. In the same way, better data quality becomes commercially meaningful when it leads to fewer failed orders, fewer customer issues, faster sales execution, or more dependable decision-making.

The key point is straightforward: technical progress alone does not equal business impact.

AI Value Often Escapes Through Operational Weakness

Many AI initiatives lose value not because the underlying technology is poor, but because the business environment around the technology is not prepared to support it. Organizations may invest significantly in AI models, cloud infrastructure, automation tools, and analytics platforms while still facing weak workflows, unclear accountability, inconsistent processes, and limited user trust.

When this happens, AI investment can behave like water poured into a leaking container. Resources are committed, but value slips away through gaps in operations.

A flawed process cannot be transformed simply by automating it. If a workflow is inconsistent, automation may only increase the speed of inconsistency. If the data is unreliable, employees may question AI-driven recommendations. If ownership is unclear, decisions may still be delayed. If users do not trust the system, adoption will remain limited regardless of how well the model performs technically.

This issue is becoming even more visible as organizations adopt generative AI and agentic AI at speed. Many businesses are building intelligent assistants and automated workflows, but they are not always redesigning the processes, governance models, and decision rights needed to support them. The result can be costly automation placed on top of already fragile business processes.

This is one reason many AI pilots create early excitement but struggle to scale. A pilot may work well, the technology may appear promising, and leaders may show interest. However, when the solution is introduced into the wider business environment, the surrounding process may not be mature enough to absorb the change.

In many cases, the biggest barrier is not the algorithm itself. It is the operating model that surrounds it.

Foundational Capabilities Must Grow With AI Delivery

Another major challenge is the tendency to separate visible AI use cases from the foundational work required to sustain them. Some organizations pursue quick AI wins while assuming that data quality, governance, process design, and ownership issues can be addressed later.

In reality, postponing that foundational work often becomes a reason AI initiatives fail to scale.

A more practical approach is to avoid both extremes. Organizations should not delay all AI activity until every data foundation is perfect. At the same time, they cannot ignore the capabilities that make AI reliable, trusted, and scalable.

Strong AI strategies build foundations and use cases together. For example, a pricing recommendation engine may create immediate commercial benefit while also revealing gaps in product master data. Rather than treating those gaps as a separate technical cleanup project, the pricing use case can provide a clear business reason to improve the underlying data foundation.

This makes foundational investment easier to justify because it links data quality, governance, semantic layers, workflow design, and ownership directly to business outcomes. These are not just technical architecture concerns. They become practical enablers of revenue growth, risk reduction, operational consistency, and customer trust.

Organizations Should Measure Net Value, Not Only Generated Value

AI value should be measured in terms of net impact, not only the value a system appears to generate.

Many AI business cases focus on positive outcomes such as revenue growth, time savings, cost reduction, accuracy improvement, or productivity gains. These indicators are important, but they do not tell the complete story.

A more mature approach considers net value: the value created after subtracting the value lost through weak execution.

Value can be lost through failed transactions, rework, customer dissatisfaction, operational exceptions, inconsistent data, inefficient workflows, or poor implementation. Each issue may seem minor in isolation, but together they can affect profitability, trust, and long-term business performance.

Customers do not experience these failures as internal data or process problems. They experience them as poor service. A valid address being rejected, an expired promotional code being sent, or incorrect product information causing an order failure can quickly damage confidence in the business.

Once that confidence is damaged, rebuilding it can take significant time and cost.

For this reason, AI measurement should include both the value AI creates and the hidden losses caused by poor execution. Leaders should ask what value is being created, what value is being protected, what value is being lost through operational weakness, and what the true net effect is on the business.

These questions help organizations develop a more realistic and commercially useful view of AI performance.

AI Investment Should Begin With the Business Decision

Successful AI investment starts with the business decision, not with the technology.

Too often, organizations begin with a desire to use a new capability such as generative AI, machine learning, AI agents, or knowledge graphs. Only afterward do they look for a business problem that fits the technology. This approach can lead to weak alignment and make value difficult to demonstrate.

A stronger approach begins by asking three questions: What business decision needs to change? What would success look like in business terms? How does the AI solution directly address the problem?

This decision-led approach helps teams identify who will act differently, what action will change, and how the business will know whether the AI system has worked. It also reduces the risk of technology-led projects becoming disconnected from commercial priorities.

In this view, AI should not be adopted simply because it is advanced, popular, or widely discussed. It should be adopted because it improves a decision that matters to the organization.

Human Judgment Still Plays a Critical Role

AI should often be positioned as an advisory capability rather than a fully autonomous decision-maker, particularly in sensitive commercial areas such as pricing, sales, credit, risk, and customer management.

These decisions frequently require context, relationship awareness, customer history, negotiation understanding, and professional judgment. AI can support teams by providing recommended ranges, explanations, risk signals, and visibility into trade-offs. However, the final decision may still need to remain with experienced business professionals.

This model, where AI informs and humans decide, can improve adoption because it presents AI as a tool that strengthens expertise rather than replacing it. Employees are more likely to trust and use AI when it helps them make better decisions instead of appearing to undermine their role.

The broader lesson is that AI adoption is not purely a technical challenge. It is also an organizational and behavioral one. Successful AI programs are built around the way people make decisions, not only around the way models produce outputs.

Data Leaders Must Become Profit Enablers

The wider message is that data and AI leaders need to redefine their role within the enterprise. They cannot be seen only as technical delivery teams, platform owners, or cost centers. They must increasingly act as enablers of business performance.

This shift requires commercial fluency. Data and AI leaders need to understand revenue, margin, cost, risk, customer experience, and operational trade-offs. They must be able to explain not only what has been built, but why it matters to the organization.

The most effective leaders in this space combine technical depth with business understanding. They connect platforms to decisions, models to margins, governance to risk reduction, and data quality to customer trust.

AI value is not created by technology alone. It emerges when models, people, processes, governance, and business decisions work together.

As companies move further into generative AI, automation, and agentic systems, this distinction will become even more important. The organizations that succeed will not simply be those that deploy the most advanced tools. They will be the ones that can show, in clear business language, that AI is delivering measurable value.

For Kamal, this is the real measure of AI maturity: not whether AI looks impressive on a technical dashboard, but whether it delivers value the business can see, measure, and trust.

From Information Asymmetry to Transaction Confidence: The Role of a Virtual Data Room in High-Stakes Deals

From Information Asymmetry to Transaction Confidence: The Role of a Virtual Data Room in High-Stakes Deals

New Delhi [India], March 10: In India’s evolving corporate landscape, high-value transactions are no longer rare events. Insolvency resolutions, mergers and acquisitions, strategic stake sales, shareholder exits, IPO-linked restructuring, and court-monitored corporate actions are now a regular part of doing business.

Yet in many such transactions – especially under IBC, M&A, and complex restructuring frameworks – the biggest risk is not valuation or negotiation. It is information asymmetry.

An often-overlooked dimension of this asymmetry is access to price-sensitive information within organisations. Recent actions by regulators, such as SEBI, against large consulting firms for information leaks within group entities underline how serious this risk has become. While enforcement is increasing, organisations themselves must demonstrate intent by adopting stronger controls.

Using a dedicated external data room, rather than informal collaboration tools such as shared drives or internal servers, sends a clear signal of compliance, governance, and professionalism. Globally, this is already standard practice and Indian organisations are rapidly moving in the same direction.

From Ethics to Efficiency: Why Structure Matters

Beyond governance, efficiency is a decisive factor in high-stakes corporate transactions. In reality, critical information is often scattered across regulatory filings, financial statements, contracts, litigation records, compliance reports, and historical disclosures, making it difficult for stakeholders to form a clear, unified view. When data is fragmented, decision-making slows and risk increases. Strong outcomes require information that is complete, current, well-structured, and instantly accessible. This is where a structured Virtual Data Room like Right2Data delivers measurable impact.

India’s Best Virtual Data Room: Right2Data VDR

Right2Data VDR was born from client demand within the Right2Vote ecosystem, where users sought a secure, structured platform to manage sensitive transaction data beyond e-voting processes.

Right2Data evolved as a natural extension of Right2Vote, widely used in India’s IBC resolution ecosystem for over a decade. Founded by Neeraj Gutgutia, a Chartered Accountant and IIM alumnus with extensive CFO-level leadership experience, the platform was built on strong governance and security foundations. As transaction complexity increased, clients required a secure Virtual Data Room to manage due diligence and restructuring data. Rohan Randery, an IIM graduate and entrepreneur, joined as Co-Founder to scale operations and strengthen strategic growth.

Today, Right2Data was largely driven by IBC-related use cases, the platform has since evolved into a premium transaction intelligence solution, now increasingly used for M&A, restructuring, IPOs, strategic investments, and due diligence.

What Right2Data Does

Right2Data consolidates, structures, and safeguards critical information in high-stakes corporate transactions, ensuring multiple stakeholders access accurate, secure, and well-organised data within a controlled Virtual Data Room environment.

Whether the transaction involves:

  • an IBC resolution process
  • a merger or demerger
  • a stressed-asset acquisition
  • due diligence or fundraising
  • a strategic investment or corporate restructuring

In an IBC resolution under the Insolvency and Bankruptcy Code, 2016, resolution applicants must review financial statements, litigation records, claims data and compliance filings within strict timelines. In mergers or fundraising rounds, investors evaluate contracts, cap tables, regulatory approvals, and historical disclosures. When this information is scattered across emails and shared drives, confusion and risk increase. Right2Data centralises these materials into a structured Virtual Data Room with role-based access, audit trails, and watermarking creating a single source of truth that connects financial, legal, and regulatory insights seamlessly.

The result is faster decision-making, reduced transaction risk, stronger compliance posture, and greater confidence among promoters, lenders, investors and advisors navigating complex corporate events.

By bringing legal, financial, regulatory, and transactional data into a coherent structure, decision- makers gain clarity under tight timelines. At the same time, the use of an auditable external platform ensures compliance, traceability, and control at every stage of the transaction.

Key Risks in Corporate and Financial Transactions

High-stakes corporate transactions rarely fail because of strategy alone. More often, they are delayed, disputed, or weakened due to preventable information risks. When data is fragmented or poorly governed, uncertainty increases, trust erodes, and execution slows. In regulated environments, these weaknesses can escalate into legal exposure, valuation erosion and reputational damage.

The most common and dangerous risk factors include:

  • Critical data that exists but is poorly organised or incomplete
  • Limited visibility across legal, financial, and regulatory dimensions
  • Heavy reliance on manual collation and fragmented advisor inputs
  • Late discovery of hidden liabilities, encumbrances, or pending disputes
  • Leakage of price-sensitive information due to uncontrolled or informal access

In processes under the Insolvency and Bankruptcy Code, 2016 (IBC), compressed statutory timelines magnify these risks. Resolution applicants, lenders and advisors must make high-value decisions quickly often while multiple versions of documents circulate through emails, shared drives or unsecured channels. A single overlooked clause or data leak can alter deal outcomes or trigger regulatory scrutiny.

Right2Data mitigates these risks by creating transaction-ready Virtual Data Room environments with structured folders, audit trails, watermarking, and role-based access controls transforming vulnerability into compliance, clarity and controlled execution.

What Sets Right2Data VDR Apart

Clients value not just the technology, but the way Right2Data partners with them during high-pressure transactions. Beyond software features, the platform delivers responsiveness, cost-efficiency, and regulatory alignment, critical factors when sensitive information, tight timelines, and multiple stakeholders are involved in complex corporate processes.

High-Touch Customer Support

Corporate transactions often move under intense time pressure, where even minor delays can affect valuation, negotiations or compliance deadlines. Right2Data provides responsive, hands-on assistance through dedicated support teams who understand transaction workflows, not just technology. This ensures quick issue resolution, smoother onboarding, guided document structuring, and continuous coordination during critical deal phases.

Most Affordable and Accessible

Many global virtual data room providers operate on high-cost models designed primarily for large multinational deals, often relying heavily on automated or chatbot-driven support. Right2Data offers a cost-effective alternative tailored for Indian market realities, enabling mid-sized companies, resolution applicants, investors, law firms and advisors to access enterprise-grade level security and structure without prohibitive pricing barriers.

Data Hosted on Indian Servers

With evolving regulatory expectations and increasing scrutiny over sensitive financial and personal information, data sovereignty has become a serious consideration. Right2Data.in hosts all transaction data on Indian servers, aligning with compliance expectations and the principles of the Digital Personal Data Protection (DPDP) Act. This strengthens control, enhances regulatory comfort, and reinforces trust in high-stakes corporate environments.

Who Uses Right2Data

Right2Data is trusted by professionals who operate in environments where accuracy, confidentiality, and time-bound execution are non-negotiable. These are decision-makers responsible for managing sensitive financial data, regulatory disclosures, and high-value negotiations. For them, a Virtual Data Room is not just a storage tool, it is a control mechanism that shapes deal confidence and credibility.

Right2Data works closely with:

  • Resolution professionals and stressed-asset investors managing IBC-driven processes under tight statutory timelines
  • Promoters, boards and CFOs navigating restructuring, stake sales, strategic exits, or capital raises
  • Legal advisors, investment bankers, and financial consultants handling due diligence, compliance reviews, and complex mandates
  • Listed companies, startups, and growth-stage enterprises preparing for fundraising, M&A, or corporate actions

Clients such as BOB Capital, JC Flowers, Ernst & Young, and Axis Capital, along with independent resolution professionals and emerging enterprises, rely on Right2Data for structured execution and dependable support. Their experience typically highlights three factors: intuitive navigation even for first-time users, responsive human assistance during critical phases and strong control over document access and tracking.

Turning Data into Transaction Confidence

In high-stakes corporate transactions, speed may initiate momentum, but confidence sustains it. Decision-makers need clarity on liabilities, contracts, regulatory exposures and financial performance before committing capital or approvals. When information is structured, searchable, and securely hosted within a controlled environment, uncertainty reduces significantly.

Right2Data helps stakeholders transition from fragmented email trails and scattered folders to a unified, auditable data environment. Instead of reacting to last-minute document requests or version confusion, teams operate with preparedness and transparency. Some organisations adopt secure Virtual Data Rooms only when mandated by advisors or regulators. Others recognise early that proactive structure reduces disputes, accelerates diligence, and strengthens negotiation positions. The difference is not just compliance, it is transaction confidence built on organised, accessible, and controlled information.

If you are involved in IBC, M&A, restructuring, or strategic corporate actions, the quality and custody of your data can shape the outcome.

In complex corporate transactions, the strength of decisions depends on the strength of information governance. As regulatory scrutiny increases and transaction timelines tighten, structured and secure data environments are becoming less optional and more foundational. Platforms like Right2Data reflect a broader shift in Indian corporate deal-making toward control, clarity, and compliance by design.