Welcome to the inaugural episode of Karmine Kompass: Pivotal Conversations!
We kick off our journey to excellence with Shreyas Tonse of Zensible, the world’s first Total Experience (Tx) company in HR technology. This conversation is your roadmap to understanding the strategic shift needed to succeed in the digital-first era.
We dive deep into why enterprises need to stop viewing HR software as fragmented tools and start treating it as a unified, strategic ecosystem that maximizes business value and employee experience.
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The world of finance operations is undergoing rapid transformation. Over the past decade, organizations have pursued greater efficiency, moving from manual processes to transactional Robotic Process Automation (RPA), and then to holistic hyper-automation. While each phase has delivered incremental gains, the next evolutionary leap is not merely about doing things faster but about doing things autonomously and intelligently.
Today, Agentic AI – autonomous AI “agents” that can perceive, reason, and act, is emerging as the next evolutionary step. Industry experts note that this transition to agentic AI is a natural progression in the automation journey, building on the foundations of machine learning, traditional AI models, and generative AI. In fact, agentic AI is touted as “the operating logic of tomorrow’s enterprise,” promising new levels of cost efficiency and growth for those who embrace it.
What is Agentic AI?
Agentic AI refers to intelligent systems designed to autonomously accomplish specific goals with limited human intervention. The difference becomes clear when comparing their operating models:
Traditional Automation/RPA: Follows predefined rules or scripts; great for repetitive tasks but brittle when conditions change.
Generative AI: Produces outputs (text, code, etc.) in response to prompts; powerful for content and analysis, yet it’s largely reactive.
Agentic AI: Goes further by being proactive. It can set objectives, plan multi-step actions, make independent decisions, and adapt to new information. An agentic AI is less like a calculator and more like a junior colleague that can handle tasks end-to-end. Importantly, it operates on a goal and feedback loop rather than one prompt at a time
This ability to carry out multi-step processes and integrate with enterprise systems is a hallmark of agentic AI.
Key Attributes of Agentic AI – The Five Pillars
Agentic AI is defined by five core pillars that set it apart from traditional automation and earlier AI systems:
Goal-driven: Agentic AI operates with clear objectives and continuously aligns its actions to achieve defined outcomes (e.g., reduce accounts payable cycle time), keeping the end goal central across all activities.
Multi-step Planning & Orchestration: It can break complex objectives into sequenced actions, coordinate multiple tools (e.g., ERP, data warehouse, GenAI for analysis) or sub-agents, and execute end-to-end workflows through an iterative think-plan-act-evaluate-refine loop.
Autonomous Decision-Making: Unlike static automation, the agent makes independent, context-aware decisions and manages exceptions dynamically without needing step-by-step human intervention, enabling true 24/7, near-continuous operations.
Continuous Learning & Adaptation: Through feedback and learning mechanisms, agents models improve over time, adapting to new scenarios, regulatory changes, and process variations thus increasing accuracy and outperforming static rule-based automation.
Transparency, Auditability & Trust: Built-in explainability, robust audit trails, and human oversight ensure decisions are traceable, compliant, and reviewable, upholding the highest standards of governance.
Together, these pillars allow agentic AI to function as a reliable, autonomous colleague in finance, capable of understanding context, executing complex processes, learning from outcomes, and operating transparently within defined guardrails.
Why Agentic AI in Finance?
The business case for Agentic AI in finance lies in its fit with the realities of modern financial operations – high data volumes, repetitive processes, time-critical decisions, and strict compliance requirements.
End-to-end automation: Agents can potentially orchestrate entire finance processes, not just tasks, reducing handoffs and freeing teams for higher-value work.
Faster decision-making: Real-time analysis and execution compress cycle times, enabling instant routine decisions and quicker insights for risk, treasury, and control functions.
Improved accuracy and compliance: Reduced manual intervention lowers error rates, while consistent policy application and anomaly detection strengthen compliance and fraud detection.
Scalable, 24/7 operations: Agents can operate continuously and scale seamlessly during peak periods without proportional increases in headcount
Adaptive handling of complexity: Unlike rigid automation, Agents learn, manage exceptions, and adjust workflows as scenarios change. Of course, with sufficient ‘human-in-the-loop’ interventions.
In essence, Agentic AI allows finance teams to achieve more throughput and intelligence with less manual effort – cutting costs, improving resilience, and shifting human focus from routine execution to analysis, strategy, and value creation.
The Architecture: Moving Beyond Silos
In an agentic finance model, the CFO’s role expands from a sponsor to an architect. CFOs define the outcomes agents are accountable for, the risk boundaries they must respect, and the governance structures that ensure trust.
The true complexity and power of this era lie in the Agentic Architecture. It is not about deploying a single “super-bot,” but rather orchestrating a federation of specialized, coordinated agents that communicate seamlessly.
Consider the complexity of a global supply chain finance process. This might require:
Handling invoice matching and payment initiation within the ERP.
Optimizing cash flow and managing foreign exchange exposure based on payment timing.
Continuously screening vendors and transactions against sanctions lists and internal policy.
These agents operate like a well-drilled team, sharing context and passing execution authority based on their specialized skills. This architectural shift enables organizations to break down functional silos, achieving true end-to-end process automation and optimization that traditional RPA could never manage.
Key Use Cases and Opportunities in Finance
1.Dynamic Forecasting Planning & Analysis (FP&A): One of the most impactful areas is financial planning and analysis. Agentic AI can turn traditional periodic forecasting into a continuous, real-time activity. For example, AI agents can integrate data from ERP systems, market feeds, and spreadsheets to constantly update forecasts and run “what-if” scenarios. This creates a kind of digital financial twin that can simulates outcomes.
Agents can also provide nuanced analysis, spotting trends or anomalies in financial data that warrant attention. In essence, forecasting becomes more precise and proactive, with AI continuously recalibrating projections.
Impact: Forecasting becomes more precise, proactive, and directly actionable, dramatically improving resource allocation and capital efficiency.
2. Procure-to-Pay (P2P) Orchestration: AI agents can streamline invoice handling, for example, by automatically pulling data from incoming invoices, cross-validating it against purchase orders and goods receipts, and flagging any discrepancies. Tedious tasks like invoice coding, approval routing, and journal entries can be handled start-to-finish by an agent.
Impact: Lower error rates, accelerated payment cycles, and a shift of A/P staff from data entry to exception resolution.
3. Accelerated Vendor onboarding & Due Diligence: Multi-agent workflows can accelerate KYC/KYB, sanctions screening, and risk scoring, reducing onboarding from days to minutes while enabling continuous monitoring and robust audit trails. Imagine a team of AI agents working together: one agent gathers the vendor’s public data and documents, another cross-checks them against databases (for sanctions, politically exposed persons, adverse media), and a third evaluates the risk level or compliance requirements, all with no human handoffs in between.
By handling the grunt work of due diligence and doing it thoroughly and consistently Agents can help onboard vendors faster while enhancing compliance. Compliance officers can then focus on the truly suspicious cases rather than sifting through false positives.
Impact: Onboarding timelines reduced from days to minutes, robust and continuous monitoring, and allowing compliance officers to focus solely on high-risk, ambiguous cases.
4. Continuous Financial close & consolidation: The accounting close process (monthly, quarterly, annually) involves aggregating data from various systems, reconciling accounts, and preparing consolidated financial statements. It’s typically a labor-intensive crunch. In one case, a manufacturing company deployed an AI agent to manage its month-end close. The agent autonomously gathered trial balances from multiple ERPs, applied matching rules to reconcile entries, and even proposed adjusting journal entries for the finance team to review. It ultimately cut the close cycle by roughly 50%.
This example highlights how an agent can take over repetitive close tasks and execute them faster and more accurately. Additionally, because the agent works continuously, it enables a continuous close environment.
Impact: Organizations have adopted an Agentic AI solution to manage their month-end close, cutting the cycle time by approximately 50% and freeing up accounting staff for variance analysis.
Conclusion: Embracing the Agentic Future
Agentic AI marks a fundamental, irreversible shift, transforming finance from an operations utility into an agile, strategic growth engine. Early adopters are already seeing material gains, including faster closes, meaningful cost reductions, and improved accuracy, while freeing finance teams to focus on strategy, analysis, and innovation rather than execution.
Adoption, however, is not plug-and-play. It requires strong governance, transparency, ethical guardrails, and deliberate change management to ensure trust, control, and human oversight remain intact. When these foundations are in place, the operational and strategic upside far outweighs the risks.
Looking ahead, finance functions will not simply become faster or more efficient, they will become decisively intelligent and increasingly autonomous. Agentic AI marks the inflection point where finance shifts from executing processes to continuously steering outcomes, operating with speed, precision, and foresight that traditional models cannot match.
Organizations that invest early and responsibly will secure enduring advantages in cost efficiency, resilience, and decision quality transforming finance from a transactional back office into a strategic, always-on growth engine. The era of autonomous finance is no longer theoretical; it is already taking shape. Those who embrace it with strong governance, clear intent, and human judgment at the core will not only lead the transition, but help set the standards by which the future of finance will be defined.
Work today isn’t steady or predictable. Roles evolve, skills expire faster, and teams form and reform around shifting priorities. Technology keeps rewriting how we connect, while employees expect more relevance, flexibility, and purpose from their organisations. In such a fluid environment, the real differentiator isn’t just strategy or tools, but whether a company can truly keep pace with how its people work and grow.
The way organisations measure people has come a long way. It started with counting heads and tracking costs, then moved into analysing skills, engagement, and HR processes. Each step gave leaders sharper insights, but the focus had mostly been on outcomes. Did employees meet targets? Did they complete the training? What do performance reviews say? What is the attrition rate? These are valuable, sure. But they’re lagging indicators that tell us what happened, not why, when, or how. The real shift begins when you start asking not just what the numbers show, but how people got there. Did someone overwork to hit a goal, collaborate effectively, or lean on old habits instead of learning?
Hitting a target is the visible part of performance, but the drivers sit beneath the surface. The way people prioritise, solve problems, share knowledge and lean on each other is what shapes the end result. Once you see those patterns, you can shape them too. That’s where behavioural analytics enters the picture – uncovering real-time patterns in engagement, adaptability, collaboration, communication, leadership, and motivation. By paying attention to these signals early, leaders can move from reactive to proactive, using these insights as a springboard for action and growth. That’s potential.
From Manpower to Behaviour
HR analytics has been steadily growing, but most organisations are still at the early stages. The roadmap to analytics started with focus on the number and headcount, evolved to emphasising on engagement and performance and is now slowly transitioning to Behavioural Analytics which is the new order of workforce intelligence.
Manpower Analytics – includes workforce basics focusing on numbers like headcount, attrition, and cost-to-hire. It’s quantitative and operational, ensuring the right number of people at the right place and cost. According to ISG’s 2023 HR Tech Survey, only 36% of companies use predictive analytics in HR, and 43% say they’ve built a data-driven HR culture. Most remain stuck in descriptive reporting.
People Analytics – From manpower analytics, it matures intogoing beyond headcount, to analyse talent, HR processes, and connects with impact on business results, such as quality of hire, engagement, learning effectiveness, succession, and diversity. This is where companies begin predicting rather than just reporting. Deloitte found 70% of organisations were already using people analytics by 2022, with adoption expected to exceed 80% by 2025.
Behavioural Analytics – Today there is a need to take a deeper lookto understand the human layer of work, how employees act, interact, and make decisions. It’s more qualitative, linking behaviour to competencies, culture, and performance. This data often comes from various sources which includes but is not limited to, collaboration tools, surveys, and assessments. Behaviour Analytics and its role in shaping organisation culture is reflected in an example; where a U.S bank adopted a platform called ‘Humanyze’, applied organizational network analysis to understand collaboration dynamics. They found that teams who shared more informal interactions, like overlapping lunch breaks, performed significantly better. By restructuring schedules to encourage this, the bank achieved a 27× return on investment, reduced turnover by 28%, and improved call resolution speed by 23%.
These are small yet significant findings that behavioural analytics can bring to the forefront, bearing a significant impact on key business metrics in a positive manner. The maturity curve is less a steady climb and more a leap. Most organisations are comfortable counting, many are starting to predict, but only a few are bold enough to decode how people truly behave and connect.
Dimensions of Employee Behavioural Analytics
As HR moves from transactional to transformational, behavioural analytics steps in to go beyond basic metrics and answer questions such as:
How are time and effort being invested?
How are people interacting and collaborating?
How are employees pursuing development and feedback?
How are they contributing to shared intelligence?
How do employees feel and sustain performance?
How do leaders inspire, align, and govern responsibly?
These questions anchor six key dimensions of behavioural analytics that bring the human side of organisational performance into focus:
Flow of Work: Captures how employees allocate energy, balance demands, adopt new ways of working, and uphold ethical behaviours – Time usage, adaptability, workload rhythms, ethical compliance
Web of Connections: Reveals the density, diversity, and responsiveness of professional networks – Communication quality, responsiveness, team cohesion, network health
Growth Mindset Signals: Shows proactive behaviours around learning, adapting, and seeking input – Learning behaviours, adaptability, feedback loops, change adoption
Knowledge Capital: Focuses on contribution, documentation, and thought leadership – Knowledge sharing, visibility, innovation contribution
Wellbeing & Sentiment Pulse: Adds the emotional and psychological layer to behavioural data – Emotional state, engagement, recognition, resilience
Leadership & Purpose Dynamics: Captures the clarity of purpose leaders provide, the ethical tone they set, and how effectively they align teams to shared goals and long-term vision – Leadership effectiveness, influence, purpose alignment, trust
Six Dimensional Behavioural Analytics Maturity Framework by Karmine
The Organisational and Employee Value of Behavioural Analytics
Benefits for Organisations
Early Warning Signals for Productivity and Engagement: Instead of waiting for quarterly engagement surveys, organisations can detect issues in real time. Microsoft saw a 16% rise in late-night meetings, 50+ messages sent outside hours, and 20% of staff working weekends. These patterns flagged risks of burnout and workload imbalance, prompting leadership to set clearer boundaries and prevent productivity collapse.
Strengthened Culture and Resilience During Change: Helps organisations spot morale dips and act quickly to protect culture. During an unsolicited takeover attempt, Unilever used automated listening tools and sentiment analysis to track employee engagement and internal communication. This helped detect early signs of falling morale and launch support programs. By acting swiftly, they maintained productivity and workforce resilience. Transparent communication and a strong culture focus enabled Unilever to withstand the takeover pressures and protect employee trust.
Data-Driven Management and Strategies: Instead of relying on assumptions, companies can test which behaviours drive performance and coach managers accordingly. Google’s Project Oxygen proved that effective managers aren’t born, they follow specific, observable behaviours. By analysing more than 10,000 data points, Google identified ten observable & coachable behaviours that reshaped manager training, recognition systems, and even promotion criteria. Within a year, 75% of underperforming managers had improved significantly, leading to stronger team performance, higher engagement, and measurable productivity gains.
Benefits for Employees
Stronger Voice and Sense of Belonging: Empowers employees by ensuring their experiences are heard and acted upon. Mercer launched “Your Voice Matters” initiative after discovering that their staff felt disconnected at work, encouraging encouraged open communication and feedback through regular surveys and focus groups. This raised engagement from 50% to 75% in two years. Employees felt genuinely listened to, which boosted motivation, reduced turnover, built trust and increasing overall productivity.
Smarter Workload Distribution Through Real Insights: Uncovers patterns of overwork or underutilisation, enabling leaders to spread tasks more evenly across teams. Microsoft’s after-hours analysis helped leaders set clearer boundaries and expectations, ensuring teams stayed productive without burning out.
Fairer Development and Growth: When leadership behaviours and performance drivers are grounded in real data, employees benefit from more transparent and fair growth pathways. Google’s Project Oxygen gave employees tangible benefits by vague ideals of “good leadership” to clear coachable actions. Instead of hoping their manager was supportive, employees could expect consistent practices – like regular check-ins, meaningful feedback, and visible support for career growth. This improved trust in leadership and created fairer career paths.
Simply put, behavioural analytics empowers organizations get sharper decision-making, and employees gain a healthier, more supportive workplace.
AI-Powered Employee Behavioural Analytics
AI-powered behavioural analytics is transforming how organisations understand and support their workforce by moving beyond quarterly reviews and annual surveys to real-time insights drawn from collaboration tools, communication channels, and learning systems. Imagine a system that detects a 30% drop in team engagement over two weeks or flags when a top performer’s response time slows by half. AI interprets tone, collaboration patterns, and learning engagement to provide context-rich alerts that allow leaders to act quickly and strategically. The benefits are clear: speed, with instant notifications instead of delayed feedback; context, with cues that highlight root causes rather than raw data; and focus, with precise signals on risks like engagement dips or collaboration breakdowns. As companies adopt these tools, they create more adaptive and personalised workplaces where employees gain tailored career recommendations and learning paths while HR benefits from ethical, explainable analytics that build trust.
Microsoft 365 Copilot is embedded in Teams and Outlook to summarise meetings, detect communication overload, and suggest more efficient collaboration patterns. Similarly, Workday’s AI capabilities analyse sentiment and skills data to provide managers with ethical, explainable insights for talent planning.
Why Behavioural Analytics in HR Is Still Underleveraged
Behavioural analytics has long been used for understanding consumer behaviour. Retail giants, streaming services, digital platforms have refined how they capture customer clicks, preferences, choices, and loyalty. All of this fuel personalisation, retention, and revenue growth. But when it comes to human capital, that kind of behavioural insight remains under-leveraged with the following key challenges holding back adoption:
Privacy, Ethics, and Trust Employees expect far higher privacy and dignity at work than consumers do in markets. Tracking collaboration, keystrokes, or sentiment can easily cross ethical lines without clear consent or transparency. Unlike consumers who trade data for discounts or personalisation, employees value autonomy, fairness, and legal protection.
Fragmented and Inconsistent Data Employee data is scattered across emails, chat logs, meetings, surveys, and HR systems. Only 40% of HR professionals say their organization is ‘good or very good’ at analysing people data, and just 48% rate their data generation capabilities highly. This fragmentation makes insights unreliable and scaling difficult.
Capability and readiness gaps Even when the will is there, most companies lack the systems and skills needed for advanced behavioural analytics compared to digital customer-facing functions. Companies need mature analytics capabilities, reliable data, and sophisticated technology infrastructure. Many are still building maturity in workforce and people analytics before they can dive deeper.
Unclear ROI compared with consumer use cases Marketing analytics delivers clear returns in sales and conversion, but HR outcomes – engagement, collaboration, or well-being – are harder to link directly to financial impact. This makes budget holders hesitant to invest, even though the long-term value is significant.
Until such issues are addressed, behavioural analytics will remain underused in HR, despite its clear potential to strengthen both employee growth and organisational performance.
Building the Foundation for Behavioural Analytics
Behavioural analytics sits at the advanced end of the HR analytics maturity curve. Most organisations begin with descriptive reports, move into diagnostic dashboards, and then step into predictive & prescriptive models. Behavioural analytics relies on multiple layers of technology, data and culture being in place.
Laying the Foundation for Behvioural Analytics
Ethical Considerations: Watchful but Respectful
Here’s where a bit of nuance matters. Behavioural analytics only works if emSample metrics for 6-dimension behavioural analytics pyramid across maturity levelsSample metrics for 6-dimension behavioural analytics pyramid across maturity levelsployees trust it. Done openly, it strengthens collaboration, development, and opportunity. Done poorly, it risks undermining culture. The goal should always be support, not surveillance. Here are ethical considerations that companies should apply:
Transparency: Clearly explain what data is collected and why. Position it as development-focused, not surveillance
Privacy: Use aggregate or anonymised data where possible. If individual behaviour is analysed, do so with consent and for growth, not punishment.
Opt-In Choices: Make participation voluntary where you can, with clear benefits such as tailored support.
Empathy-Driven Use: Interpret behaviour data with context – late responses may reflect deep work or personal matters, not disengagement. Data should start conversation, not drive judgement.
Clear Boundaries: Define what will not be measured (e.g., private chats, personal devices) to build trust.
Shared Value: Show how insights help employees grow in their careers and learning, not just how they benefit the organisation.
Human Oversight: Algorithms can flag patterns, but people should interpret and act with care
Feedback Loops: Give employees a voice to question or clarify how their data is read, making it a two-way process.
Cultural Sensitivity: Behaviours vary by culture and role; avoid one-size-fits-all interpretations.
Positive Reinforcement: Use analytics to encourage constructive behaviours, not just detect risks.
Linking Behavioural Analytics to Learning & Development
Behavioural analytics provides a data-driven foundation for modern L&D. By measuring signals such as collaboration patterns, feedback-seeking, or adaptability to change, organisations can identify the precise learning needs that hold teams back. Instead of rolling out generic programs, analytics enables the sharper and personalized learning journeys across technical skills, soft skills, leadership development, or competency training.
This enables employees to engage with learning that feels relevant to their roles, while leaders can track measurable progress through the same behavioural indicators that highlighted the need. This creates a closed loop between insight and action – analytics identifies gaps, L&D addresses them, and follow-up analytics measures the impact. Done well, this approach not only builds stronger skills but also nurtures a culture of continuous learning, adaptability, and high performance.
Conclusion
Behavioural analytics is moving fast to becoming a core part of how organisations understand and support their people by using real behavioural signals to shape smarter learning, more relevant development, and stronger team performance. The real win is that it helps HR step out of the back office and drive resilience, adaptability, and culture at scale. And with AI in the mix, the future goes further than just analysing behaviour, by simulating outcomes, personalising growth, and creating workplaces that continuously learn and improve. It is not just a tool, it is the next frontier in data-driven talent intelligence that provides strategic, corporate-focused insights
References
Deloitte. (2023). Global Human Capital Trends 2023 Report. Deloitte Insights.
Deloitte. (2025). Global Human Capital Trends 2025 Report. Deloitte Insights.
ISG. (2023). Survey on Industry Trends in HR Technology and Service Delivery 2023. ISG Research.
Bersin, J. (2018). People Analytics Maturity Model. Bersin by Deloitte
George, W. W., & Migdal, A. (2017). Battle for the Soul of Capitalism: Unilever and the $143 Billion Takeover Bid. Harvard Business School Case 317-127.
As mid-sized listed companies scale, their risk landscape grows more complex. Many still operate with fragmented data systems and ad hoc reporting frameworks. Unlike large enterprises with mature infrastructures, or smaller firms with manageable oversight, mid-sized companies often fall into a blind spot: “too complex to run manually, too constrained to modernize decisively.”
The result? Data exists but is scattered across systems, spreadsheets, and silos. Unstructured, unsurfaced, or untrusted. Risk visibility becomes partial, reporting cycles are reactive, and decision-making is shaped more by instinct than insight.
In this article, we unpack the top root causes behind this challenge. We also outline five strategic remediation moves; practical, scalable steps that mid-sized firms can take to build integrated, resilient, and insight-driven risk data ecosystems.
Because today, risk management is a data problem and solving it is a competitive advantage.
Top Root Causes of Underdeveloped Data & Reporting Frameworks
1 – Absence of a Strategic Data Governance Framework
Most under-developed data environments can be traced to the absence of a robust data governance strategy. Data governance encompasses the policies, standards, and processes that ensure data is accurate, secure, and available. In many mid-sized companies, it is either ad hoc or entirely missing. There’s no centralized framework assigning ownership or standardizing how data must be managed.
How it manifests: Different business units define and handle data independently. For instance, a single counterparty (customer/vendor/partner) may have multiple IDs across systems, distorting their true profile. These inconsistencies stem from the lack of enterprise-wide data definitions, taxonomies, and data catalogs.
Why it persists: Instituting data governance is challenging. It requires cross-functional coordination and often a cultural shift. Mid-sized firms may not necessarily have dedicated a Chief Data Officer or equivalent, leaving IT teams to enforce standards without executive clout. Moreover, some firms perceive governance as bureaucracy that slows down operations. If leadership is unconvinced, they won’t allocate time to build a governance committee or policies.
Impact on risk management: Without strong governance frameworks, companies struggle to aggregate and report risk data effectively leading to poor risk assessments and decision-making. A mid-tier bank without clear data ownership might find that its finance and risk departments use different definitions of “exposure,” resulting in conflicting risk reports. In manufacturing, lack of governance might mean safety incidents or quality defects aren’t logged uniformly, obscuring critical risk trends.
2 – Siloed Systems and Fragmented Data
Mid-sized companies often grow through business silos, each department or subsidiary implementing its own framework, models and structure to suit their maturity curve. The result is fragmented data architecture: customer data in one platform, sales in another, risk metrics in a spreadsheet, and so on, with poor integration between them.
How it manifests: Data silos hinder enterprise-wide visibility.
Attempts to create a “single source of truth” fail if systems don’t talk to each other. A bank’s lending unit and treasury unit might use separate reporting tools, making it laborious to compile an integrated risk report. Or consider a manufacturer where procurement and production each maintain separate inventory records. Without integration, the company cannot accurately assess supply chain exposures or working capital at a consolidated level.
Why it persists: Ironically, despite years of trying to build interfaces, the problem has in some cases worsened – over 40% of companies report that the number of data silos has actually increased, while only ~10% have improved company-wide information access.
Teams might resist sharing data (protecting their turf), and technically it can be challenging (or expensive) to connect legacy systems lacking modern APIs.
Impact on risk management: Data silos are kryptonite for risk oversight. If risk data is scattered, it’s difficult to get a holistic view of the organization’s risk profile. Correlations between risks may go unnoticed as seen in some recent bank failures. In summary, fragmentation undermines any robust risk management framework by preventing timely, accurate data consolidation.
3 – Legacy IT Systems and Technical Debt
The burden of legacy technology, outdated core systems or homegrown solutions that have been patched over time is nothing short of an industry norm. Legacy systems are often inflexible, incompatible with modern data tools, and prone to failure, collectively contributing to underdeveloped reporting frameworks.
How it manifests: A bank might still rely on a decades-old core banking system that wasn’t designed for today’s data demands, requiring batch processes to produce reports (meaning no real-time insight). A manufacturing company could be running an old version of an ERP that lacks modern analytics modules, forcing employees to export data into spreadsheets for analysis.
The prevalence of legacy tech is notable. Nearly 96% of IT professionals in one 2023 survey said they stillneed legacy applications in their environment, and only 4% reported not using any legacy applications.
Why it persists: Replacing core systems is often viewed as risky, expensive, and disruptive. The classic “if it isn’t broken, don’t fix it” mentality.
Technical debt (the cumulative cost of quick-fix IT decisions) accumulates because the company opts for short-term patches over long-term rebuilds.
Impact on risk management: Outdated technology directly impacts risk monitoring and reporting. Legacy systems may not capture the level of data granularity needed for advanced risk analysis (for example, a legacy manufacturing system might not log each production anomaly needed to predict equipment failure risk). They often lack audit trails or modern security, elevating operational and cyber risks.
4 – Cultural Resistance to Change and Data Sharing
Organizational culture plays a pivotal role in the success of data initiatives. Long-standing habits and attitudes create resistance to adopting new data practices or sharing information freely.
How it manifests: Front-line managers may cling to their known and used ‘excel spreadsheets’ and gut-feel decision making, viewing new data systems with suspicion. In many ways, new data systems also expose known but unaddressed failures to the limelight.
Some departments also treat data as a power source to hoard. For instance, the sales team might be reluctant to input detailed client data into a central CRM if they’ve historically managed relationships personally. The XPLM industry survey highlights that two-thirds of respondents said their corporate culture actually favors the emergence of data silos, and 71% admitted that departments “do not want to share their knowledge” across the organization.
This culture can doom data projects; employees might refuse to adopt a new reporting tool, or deliberately bypass official processes (keeping shadow records) because they don’t trust or understand them.
Why it persists: Cultural change is one of the hardest challenges in any organization. Mid-sized companies often have veterans and legacy practices deeply ingrained – “this is how we’ve always done it” can be a mantra. If leadership isn’t actively driving a data-centric culture, middle management is unlikely to enforce it.
Additionally, without adequate training or clear communication of benefits, staff may genuinely fear that new data systems could make their roles redundant or expose their mistakes, thus resisting involvement. There’s also the issue of incentives: if performance metrics don’t reward data sharing or accuracy (and instead only reward short-term results), employees have little motivation to change their behavior.
Impact on risk management: Cultural resistance can sabotage even well-intentioned risk data initiatives. If, say, the risk team implements a new enterprise risk management (ERM) system but business units don’t feed it with timely data, the system becomes an empty shell. An unsupportive culture can nullify the best tools and keep the organization in a reactive stance, where data is seen as a threat or burden rather than a shared asset for informed risk-taking.
5 – Increasing Regulatory and Reporting Complexity
The external environment is raising the bar on data and reporting, and many companies are finding their frameworks lagging behind these evolving requirements. Whether it’s financial regulations, data privacy laws, or sustainability reporting standards, the complexity and volume of reporting expectations have grown exponentially – and mid-sized firms are struggling to keep up.
How it manifests: A regional bank might face new stress-testing data requirements from regulators that its current risk systems cannot support, resulting in frantic efforts to pull the right data. Manufacturing companies now encounter detailed ESG expectations, for instance, European mid-sized listed firms will soon need to comply with the EU’s Corporate Sustainability Reporting Directive (CSRD), tracking metrics from carbon emissions to supply chain due diligence. Many are unprepared.
Why it persists: Unlike large corporations, mid-sized companies typically do not have big compliance departments or the latest Reg-Tech tools. They may be caught off guard by new regulations or find them disproportionately burdensome.
Impact on risk management: Compliance risk becomes a top concern. But beyond compliance, the spirit of these regulations (be it transparency in risk or sustainability) is to drive better decision-making. If a mid-sized firm is only doing the minimum, it likely isn’t leveraging the data to actually improve risk management.
6 – Talent and Skills Gap in Data Analytics
Even with the right tools, organizations need skilled people to build and maintain robust data frameworks. Mid-sized companies often face a talent crunch in this area. They may lack experienced data architects, analysts, or risk data specialists on staff.
How it manifests: The IT team might be small and generalized, without a dedicated data engineer or data scientist. Mid-sized firms often cannot offer the same compensation or career trajectory as large tech firms or banks, leading to a smaller talent pool.
Why it persists: The demand for data and analytics talent has exploded in recent years (with the rise of AI, big data, etc.), and supply has not kept up. Mid-sized companies often have to “grow” their own talent internally, which takes time. Hiring experienced professionals is competitive and costly. Additionally, some mid-tier companies are located outside major tech hubs, making recruitment harder. There’s also the issue of retention.
Impact on risk management: A skills gap can severely hamper risk oversight. Insufficient talent leads to heavy reliance on a few key individuals or external vendors; this concentration is a risk in itself. If those individuals leave or contracts lapse, the organization’s data capability could collapse. Risk professionals in such settings often find themselves doubling as data cleaners and report builders, diverting them from higher-value risk analysis.
5 Strategic Remediation Moves for Mid-Sized Organizations
Mid-sized companies can turn these challenges into opportunities by proactively strengthening their data and reporting frameworks. Below are five strategic remediation moves spanning technology, governance, and people to help resolve or mitigate the above root causes. These strategies are interrelated and can be pursued in parallel:
1 – Establish a Robust Data Governance Framework with Executive Ownership
Firms should formalize a data governance program that defines clear roles, responsibilities, and policies for data management. This also means appointing accountable data owners/stewards in each domain. To succeed, governance cannot be an IT-only initiative.
It needs top-down endorsement and enforcement. Leadership should treat data as a strategic asset, regularly reviewing data governance progress just as they would financial results.
The key is also continuous improvement: governance isn’t a one-time project but an ongoing program that adapts as the company grows and regulations change.
2 – Invest in Modern, Scalable Data Architecture and Tools
A strategic upgrade of technology can pay huge dividends. Mid-sized organizations should evaluate and invest in scalable data infrastructure that could involve moving to cloud-based platforms, implementing a unified data warehouse or lake, and deploying business intelligence (BI) and reporting tools that automate data aggregation and visualization.
Modern cloud solutions are increasingly accessible to mid-market companies (often offered in modular, pay-as-you-go models), lowering the barrier to entry. Key considerations would be toprioritize integration-friendly solutions and adopt tools that reduce manual work, such as ETL for moving and reconciling data
3 – Strengthen Data Talent and Literacy Across the Organization
People are the linchpin of any data strategy. Companies should invest in their human capital by both acquiring and developing data skills. If hiring full-time is difficult, engaging external consultants or service providers on a project basis can jump-start initiatives while transferring knowledge to internal staff.
On the development front, companies should launch data literacy programs so that employees at all levels become more comfortable with data and analytics tools.
A focus on talent and literacy sends a message that data isn’t just the IT team’s job, it’s everyone’s responsibility.
4 – Foster a Data-Driven Culture with Strong Change Management and Incentives
Leaders should consistently communicate the importance of data in achieving the company’s goals, and celebrate data-based decision making.
Some firms establish cross-functional teams or “communities of practice” around data, which break down silos by design. It can also help to start with small wins. Pilot the new framework in one department, refine it, and then expand, so people see proven benefits.
A data-driven culture also means employees become more likely to report issues or anomalies when they occur, rather than hiding them, because they know management wants to hear the data even if it’s bad news.
In essence, technology and processes might provide the tools, but culture is the soil in which a data-driven enterprise either withers or thrives.
5 – Align Data Initiatives with Risk Management and Compliance Objectives
Lastly, mid-sized organizations should explicitly try and link their data framework improvements to their broader risk management and compliance goals. In practice, this means using risk-based criteria to drive data projects: focus on the data that matter most for the company’s risk profile and regulatory requirements.
Some mid-sized firms establish a Risk and Data Steering Committee that meets regularly to ensure data initiatives are evaluated in terms of risk reduction and compliance impact. Additionally, keep an eye on upcoming regulations and proactively build capability to meet
Ultimately this alignment creates a virtuous cycle: good data feeds into good risk management, which identifies areas for improvement, which in turn drives further data enhancements. By making risk management a key outcome of data strategy, companies ensure their data framework upgrades truly fortify the organization’s resilience and not just its operational efficiency.
Conclusion
Transitioning to a mature data and reporting framework is undoubtedly a journey, not an overnight fix. However, by understanding the root causes behind their current shortcomings, organizations can target their efforts more effectively.
The challenges outlined often interact, but the good news is that the remediation moves are mutually reinforcing as well. With committed leadership, smart investments in technology, empowered people, and a culture that values information, companies can evolve their data practices significantly. The payoff is more than just better reports. It is improved risk foresight, stronger compliance, and enhanced decision-making agility.
Sources:
Basel Committee on Banking Supervision (BCBS 239) progress reports (2023)
BIS reports on supervisory expectations for risk data frameworks
Case studies: Silicon Valley Bank collapse analysis, 2023 U.S. Senate testimony and Fed reviews
Sero Group: Implementing Data Governance for Small and Medium-Sized Businesses
XPLM (2023): Study on Enterprise Data Silos and Cultural Resistance to Data Sharing
Gartner, Forrester, and IDC insights on enterprise data architecture adoption
QBE Global Risk Index (2023): Mid-Market Risk Prioritization and Preparedness Survey
Hyperproof GRC Benchmark (2024): Risk and Compliance Operations in Fragmented Environments
Sage (2023): SME Cloud and Sustainability Technology Trends Report
IDC SMB Tech Pulse (2023–24): Cloud adoption rates and tech spend forecasts for mid-sized firms
McKinsey Digital: The Value of a Scalable Data Architecture for Mid-Sized Enterprises
World Economic Forum: 2023 Global Talent Outlook
Udemy for Business: Skills Gap in Data Literacy 2023 Report
Let us for illustration purposes understand the approximate scale of the compliance requirements for mid-sized enterprises in India.
India’s regulatory ecosystem has tens of thousands of requirements, over 69,000 unique compliance requirements across 1,536 laws by one count. These are not abstract numbers; they translate into a daily grind of filings and checkpoints.
A medium-sized manufacturing company in India, for example, might need to comply with 5,500+ distinct regulations, whereas even a small manufacturing unit must follow around 750 regulations. These include everything from labor law registers and tax returns to factory safety displays and environmental permits.
TeamLease Regtech, a compliance technology firm, estimates that an Indian MSME with just over 150 employees faces 500–900 applicable compliance requirements. It’s no surprise that business owners feel the weight of this “control bloat” in their operating costs.
Although the organizations are free to assess their own risk appetite and calibrate approach to suit a “Risk Based Approach”, in reality, the fear of potential non-compliance leads to excessive compliance burden.
Rising Compliance, Spiraling Costs, Unclear Value
One of the clearest signs of “compliance fatigue” is the growing cost of compliance, relative to its perceived benefit. Compliance budgets have been rising rapidly, often without commensurate clarity on what risks are actually being mitigated or value gained.
Despite massive compliance expenditures in certain industries, breaches and fines continue unabated. For instance, global banks collectively paid billions in penalties in recent years even as their compliance departments grew larger than ever. Regulators have openly noted that they remain unimpressed by the amount of money spent on compliance, what matters are outcomes. If compliance spending doesn’t translate to fewer incidents, its ROI is fundamentally in question.
Across industries, leaders are asking hard questions: “What are we really protecting with all this spending?” It’s often difficult for compliance officers to answer with hard data. Ideally, compliance investments protect the business from fines, fraud, data breaches, safety incidents, reputational damage, etc. But quantifying the absence of a crisis is challenging. Compliance’s success is often that “nothing bad happened,” a counterfactual that’s tricky to monetize.
In fact, most organizations don’t even measure their compliance spending in a holistic way. A 2023 survey by Thomson Reuters found that 45% of firms did not monitor the total cost of compliance across the organization in any form. Without tracking cost, it’s nearly impossible to calculate ROI. It’s not surprising, then, that compliance teams struggle to demonstrate tangible value.
The bottom line: Many organizations feel trapped in a compliance cost spiral; pouring more and more money and effort in, without a clear picture of risk reduction or business value out. Business leaders don’t want to write blank checks for compliance; they want to know their investments are actually protecting the company’s most critical assets and stakeholders.
Audit Overload and Tick-Box Compliance Culture
Why Leaders Are Concerned
When we weigh fragmented initiatives, audit overload, ballooning costs, reactive spending, and staff burnout, it becomes clear why many organizations see a cost-benefit imbalance in their compliance programs.
The benefits (risk reduction, avoidance of fines/incidents, improved reputation), while very real, are often opaque and lagging, whereas the costs are immediate, tangible, and rising. This imbalance is leading some executives and board members to question whether they are getting value for money from compliance.
In blunt terms, if we doubled our compliance spend in the past 5 years, are we twice as safe? Or as one expert framed it: “What is the probability that the usual GRC investments are genuinely protecting the business?”. If that probability is low or unknown, it signals a problem in how the program is structured or measured.
Business leaders don’t want compliance to be a necessary evil; ideally, they want it to protect what truly needs protecting and enable the business to thrive. The challenge ahead is how to rebalance the equation so that the compliance function’s value is as plain as its cost.
Improving ROI Clarity: Strategies for Better Compliance Value
Despite the daunting picture, there are concrete steps organizations can take to rebalance their compliance efforts and improve clarity. Below are several actionable recommendations and strategic shifts that can help transform compliance from a fatigue-inducing cost center into a more efficient, value-driven function:
1 – Adopt a Risk-Based, Strategic Approach:
Rather than treating all compliance activities as equally critical, prioritize resources toward the risks that could most seriously harm your organization. This means clearly answering the question, “What are we really protecting?” Is it customer data? Financial integrity? Safety of employees? Once you identify your crown jewels and top threats, align compliance controls to those areas first.
A risk-based approach also involves defining your risk appetite (what level of risk you’re willing to accept). This helps right-size compliance efforts; in areas of low risk, avoid over-engineering costly controls that don’t add value. By focusing on what truly matters, you can start to quantify benefits (e.g. “we reduced the probability of a major data breach by X% through these controls”) and thus demonstrate ROI in terms of risk reduction.
2 – Consolidate and Streamline Programs:
Break down the silos between various compliance initiatives. Often different teams manage overlapping requirements with separate processes and tools.
Conduct a program audit to identify overlap and inefficiency. You may find, for example, multiple teams separately assessing vendor risk or multiple tools tracking similar control inventories. Consolidating these efforts not only cuts cost but improves consistency.
Consider establishing an integrated GRC (Governance, Risk, Compliance) framework where a single system maps all controls to relevant regulations. This allows one control (say, an access security control) to satisfy multiple requirements at once, reducing duplicate work.
Streamlining should also extend to audits: whenever possible, use a single evidence repository so that one piece of evidence can serve multiple audit objectives, alleviating audit fatigue.
3 – Leverage Technology and Automation:
Invest in modern compliance tools that automate and improve visibility. According to Accenture research, 93% of compliance leaders agree that AI and cloud-based compliance tools can remove human error and automate manual tasks, boosting efficiency.
Some areas to target with technology include: continuous monitoring of controls, workflow tools for policy management and attestation, and data analytics to detect compliance issues early. However, technology is not a silver bullet. It should be implemented alongside process improvements, not just layered on top of bad processes.
4 – Define Metrics and Communicate Value:
To make ROI clear, define key performance indicators (KPIs) for your compliance program that relate to both cost and benefit. It’s notable that nearly half of the firms do not monitor their cost of compliance at all; simply starting to measure it is step one. Next, translate compliance outcomes into the language of business. Even if not perfect, they signal that the compliance function is evaluating its own effectiveness.
5 – Foster a Culture Beyond Box-Ticking:
Cultural change is critical. Tone at the top matters. Leadership should emphasize that compliance is about protecting the company and its stakeholders, not just pleasing regulators.
Make compliance part of performance evaluations for everyone, not as an extra burden but as an expected aspect of good business practice.
When compliance is culturally rooted, people are less likely to see it as an external imposition and more as a shared value.
Engaged employees are the best defense and also the best champions to demonstrate that compliance work has real impact.
6 – Right-Size the Compliance Organization:
Leverage external expertise strategically. For example, use outside counsel or consultants for niche regulations or periodic compliance program reviews, rather than carrying that full expense in-house year-round.
This can provide access to expert knowledge on demand and help answer tricky ROI questions.
At the same time, cross-train team members on different aspects of compliance; a well-rounded team can handle a wider range of issues, improving efficiency.
7 – Align Compliance Objectives with Business Goals:
One way to underscore ROI is to tie compliance initiatives directly to business objectives. For example, if a company’s goal is to expand into European markets, frame the enhancement of your privacy compliance (GDPR, etc.) as an enabler of that expansion (gaining customer trust and avoiding legal roadblocks).
If the business is embracing digital transformation, position your cybersecurity compliance upgrades as protecting that digital innovation (thus avoiding costly setbacks from breaches). By framing it this way, you shift the narrative from “compliance is a cost we must bear” to “compliance is helping us achieve X business outcome securely.”
Consider building “compliance by design” into product development and strategy, ensuring that new initiatives consider regulatory requirements from the start.
8 – Review and Reduce Bureaucracy:
Periodically conduct a “clean-up” exercise. Many compliance programs accumulate layers of checks over time (often as reactions to past problems) and never shed any.
Sometimes, simplifying a control or combining two steps into one can maintain effectiveness and save hundreds of person-hours. Every hour saved is essentially money saved or re-allocated to more meaningful work. This improves the perceived ROI because people see that compliance is mindful of efficiency and not just adding procedures endlessly.
Implementing the above strategies requires effort and commitment, but the pay-off is two-fold: reduced fatigue and higher ROI clarity. Firms that have pursued such improvements report not only cost savings, but a stronger confidence among leadership that compliance investments are worthwhile.
Conclusion
Companies today find themselves juggling a multitude of regulatory demands, from financial controls to data privacy to ESG, with teams that are overloaded and budgets that seem to grow faster than the perceived benefits. The current state in many organizations is fragmented compliance efforts, reactive fire-fighting, and a culture of ticking boxes to get through audits, all contributing to high costs and murky value. Mid-size firms feel this pain acutely as they shoulder enterprise-level rules with far fewer resources.
Yet, it doesn’t have to remain this way. By reimagining compliance through a strategic lens, focusing on risk-based priorities, integrating programs, leveraging technology, and fostering a compliance-positive culture, businesses can turn compliance into a more streamlined, proactive, and yes, valuable part of operations.
In the end, the goal is to establish compliance programs that confidently answer the ROI question. That means being able to articulate, at a high level: Here’s what we’re protecting, here’s what it would cost if we failed, and here’s how our compliance efforts prevent that.
Sources:
Wipro Sustainability Report FY 2023-24 – warning against “compliance fatigue” leading to a checkbox mentality
Mid-sized companies across the globe are grappling with an increasingly complex risk landscape. From cyber threats and supply chain disruptions to regulatory changes and market volatility, operational risks today are more interlinked across business functions than ever before.
Yet, many of these organizations lack a harmonized risk language and accountability, a shared, enterprise-wide way to understand, categorize and monitor risks. Instead, each department often speaks its own dialect of risk, using different taxonomies and sometimes, tools to monitor. The result is that critical issues can go unspoken, miscommunicated across silos, leading to unclear ownership of risks, duplicated compliance efforts, and missed early warning signs of trouble.
What is a “common risk language”?
In simple terms, it’s a standardized vocabulary and classification of risks that everyone in the organization uses. This involves agreeing on a risk taxonomy, risk ratings and terms across all teams. The purpose of a common risk language is to ensure that a finance manager, an IT analyst, and an operations supervisor all mean the same thing when they discuss “high operational risk” or a “compliance issue”. A a common framework enables people with diverse backgrounds to communicate effectively about risk and identify issues more quickly.
According to one benchmarking survey, teams managing risk in silos reported spending nearly 38% of their time on administrative tasks (assembling reports, updating spreadsheets) and the vast majority said at least one-third of their effort went to repetitive manual work. These inefficiencies directly translate to higher compliance costs and lost productivity.
One common symptom is unclear risk ownership. For example, consider a mid-sized manufacturing firm. The operations team tracks safety incidents and supply disruptions, the IT team handles cybersecurity threats, and CISO monitors regulatory issues. When a critical supplier suffered a cyber breach, operations labeled it a supply chain issue, IT labeled it a vendor cyber risk, and CISO saw a third-party data privacy concern.
Another example ailing many financial institutions pertains to preventing, detecting Money Mules (Money mule risk refers to the threat posed to a financial institution when its accounts, systems, or services are exploited knowingly or unknowingly by individuals, to move illicit funds, thereby exposing the institution to fraud losses, regulatory breaches, and reputational damage.)
Who truly owns this risk? Is the fraud risk team or the AML Compliance team or the cyber team or the first line of defense? Money mules are a classic case of an interconnected risk without a common language. Multiple functions in the same organization perceive the risk differently and hence, are never able to solve the root cause issues are a singular unified view. Without a unified view, early indicators that might have been obvious in say, a consolidated dashboard, remain scattered.
Since there are no common taxonomies linking these perspectives, no single owner is alerted to the full picture. This overlap and ambiguity mean everyone assumes someone else is mitigating the problem. The early warnings are hence, often missed amidst the fragmented reports.
The problems exacerbate in case of un-regulated sectors.
Why a Common Risk Language Matters:
Aligned Risk Appetite and Decision Making: A common risk language helps align the organization’s risk appetite with operational decisions. Risk appetite, the level and type of risk a company is willing to accept in pursuit of its objectives, is typically set at the top. With a unified taxonomy, management can define risk appetite in concrete terms for each risk category, and everyone from the board to the business units understands it the same way. This means decisions on the ground are made with a clear understanding of how they fit the company’s risk tolerance.
Clear Ownership and Accountability: With the unification, every major risk category has an owner and stakeholders who all understand what falls under that risk. There’s less chance of “grey area” risks being unowned. Responsibilities can be assigned without ambiguity ensuring someone is watching each risk and accountable for responding.
Enterprise-Wide Visibility: Using one risk language allows aggregation of risk data across the whole company. Executives can see the full risk profile without blind spots. Early warning indicators become more apparent when all inputs feed into one picture. Patterns (like similar issues cropping up in different regions or departments) can be detected via the common categories. This holistic view is essential for spotting systemic risks that individual silos might overlook.
Efficiency and Reduced Duplication: Standardizing risk categories and reporting streamlines processes. The same risk does not need to be assessed in triplicate by different teams; one assessment can serve multiple purposes. Controls and mitigations can be designed to address multiple related risks at once. This cuts down the repetitive administrative burden. In mid-sized firms where resources are limited, this efficiency can be a game-changer, freeing staff to focus on high-value risk mitigation.
Improved Communication and Collaboration: A shared vocabulary breaks down communication barriers between functions. In day-to-day operations, this means cross-functional teams can come together quickly around emerging issues, because they have a common reference point. Stakeholders from different domains can contribute insights without talking past each other, leading to more robust risk assessments.
A Contrast: Harmonized Taxonomy in Action
Building a Common Risk Language: Practical Steps for Mid-Sized Companies
Implementing a harmonized taxonomy may sound daunting, but it can be achieved with a series of practical, staged steps. Mid-sized corporates, in particular, should tailor these steps to their scale and culture, focusing on enabling cross-functional collaboration without excessive bureaucracy.
Below is a roadmap to strengthen enterprise-wide risk insight and decision-making through a common language.
1 – Inventory and Reconcile Existing Risk Terminologies:
Identify overlaps and gaps – Gather the risk lists and terminologies currently in use across departments (e.g. finance risk register, IT risk log, HR compliance checklist, etc.). It’s common to find different names for essentially the same risk. For instance, “data leak” in IT, “confidentiality breach” in legal, and “privacy compliance failure” in compliance might actually refer to overlapping risk events.
Draft an initial unified risk taxonomy – Form a small working group with representatives from key functions to review and start mapping equivalences. Leverage industry frameworks as a starting point, for example, ISO 31000 or COSO ERM categories but customize them to fit the company’s context. This collaborative approach brings deep expertise from each area and ensures the taxonomy isn’t imposed top-down but rather agreed upon.
Develop a Common Risk Glossary and Definitions – For each risk category and sub-category in the taxonomy, write down a clear definition and examples. This becomes the glossary of the common risk language and a common rating criterion.
2 – Assign Clear Risk Ownership and Governance
Assign Risk Owners – With the taxonomy in place, assign risk owners for each major category or for specific key risks. In a mid-sized company, a single executive or senior manager might own multiple related risks (for instance, the Head of Operations might own Supply Chain and Safety risks, the CFO might own Financial and Compliance risks, etc.). The important part is that it’s documented and communicated.
Establish a cross-functional working groups – Set up risk workking group that meets regularly to discuss risks enabled through the common language. Having this governance structure formalizes the common language, it’s where everyone “speaks risk” together. It helps break the historical silo mindset and replace it with a culture of information-sharing.
3 – Implement Enabling Tools and Central Risk Register
Establish a single source of truth – This could be as simple as a shared spreadsheet or database in smaller companies, or a module in GRC (Governance, Risk & Compliance) software for those who have it. The key is that all departments log their identified risks, incidents, and mitigation plans in this central repository using the agreed taxonomy and ratings.
Provide visibility to the central source of truth – This central risk register gives everyone visibility into risks across the enterprise. It also simplifies reporting; one can generate an enterprise risk dashboard from it for management or board reporting, instead of manually compiling data.
4 – Integrate Risk Discussions into Operational Processes:
Having a common language and tool is half the battle; the other half is making sure it’s used in decision-making. Mid-sized firms should embed the common risk language into their routines. For example:
Department heads can be required to include an update on key risks (using the common categories) in their monthly reports.
Project proposals can have a section assessing risks in common language terms.
Incident post-mortems should map causes and follow-up actions to the taxonomy categories.
Gamify or use simple checklists to guide staff on identifying and reporting risks consistently.
The goal is to avoid situations where only risk managers talk about risk. Instead, every team uses the common language in their context.
5 – Link the Common Language to Risk Appetite and Strategy:
Articulate risk appetite – Ensure that the company’s risk appetite is articulated in the same terms as the risk taxonomy. This practice directly ties operational risk oversight to strategic goals and thresholds. It also helps in aligning mitigation efforts with what the company cares about most.
Periodically review enterprise risk profile – Companies should review these appetite statements periodically in their risk committee and adjust as necessary (for instance, if entering a new market or launching a new product, adjust appetite and categories accordingly).
6 – Continuous Education and Refinement:
Implement Ongoing Training – Conduct periodic workshops or scenario drills where cross-functional teams practice responding to a hypothetical risk event using the shared framework. The risk landscape also changes so the common language must evolve too.
By following these steps, mid-sized enterprises can gradually build a common risk language that permeates the organization. This is as much a cultural initiative as a technical one. Leadership should articulate the “why”; explain to all staff that the company is establishing a common risk language so that everyone can work together to safeguard the business. Teams start to see how their concerns connect with others’.
Mid-sized companies may not have the massive risk departments of large corporations, but they can absolutely achieve world-class risk oversight through this exercise. When risks stop being described in incompatible ways and instead are discussed on a shared platform, previously “unspoken” priorities become clear. Early warning signals emerge from noise. Compliance efforts become more about insight than paperwork.
In an environment of ever-interconnected risks, establishing this shared understanding is fast becoming not just a best practice, but a necessary priority for sustainable growth.
As the old proverb goes, “if you want to go fast, go alone; if you want to go far, go together”. A common risk language ensures that a company’s departments go together, equipped with unified insight, as they navigate the risks on the road ahead.
Sources:
Boultwood, B. How to Develop an Enterprise Risk Taxonomy. GARP (2021) – Importance of a hierarchical common risk language for ERMgarp.orggarp.org.
LogicGate Risk Cloud. The Language of Risk (2021) – Benefits of a shared risk vocabulary; 50% of companies lack consistent risk data/languagelogicgate.comlogicgate.com.
Chambers, R. Break Down Silos for Visibility Into Enterprise Risk. MIT Sloan Management Review (Feb 2025) – 86% of risk professionals say silos hinder risk management; need for holistic approachsloanreview.mit.edu.
OneTrust Blog. Who Owns Third-Party Risk: Breaking Down Silos (Mar 2022) – Risk silos create duplication of efforts, analysis gaps, lack of knowledge sharingonetrust.com.
Hyperproof. 2025 IT Risk & Compliance Benchmark Report (Oct 2024) – Data silos link to higher breach frequency; 46% of siloed-risk firms had breaches vs 30% with integrated approachhyperproof.io. Also, siloed teams spend ~38% time on admin taskshyperproof.io.
MetricStream Case Study. Almarai – Enterprise Risk and BCM (2020) – Fragmented approach led to inconsistent risk understanding, limited visibility, duplicate workmetricstream.commetricstream.com; introducing common risk taxonomy improved data accuracy, visibility and cut effort by 50–70%metricstream.commetricstream.com.
MetricStream Case Study. Fortune 1000 Insurance Co. GRC Journey (2021) – Lack of common risk language caused inefficiencies, solved by centralized taxonomy and platformmetricstream.com.
Chakraborti, A. Challenges of ERM Implementation in India (Jan 2024) – Mid-sized enterprises struggle with resource constraints for risk managementlinkedin.com.
DeLoach, J. Using a Risk Model as a Common Language. Corporate Compliance Insights (2014) – Purpose of a common risk language is to ensure completeness in risk identification and effective communicationcorporatecomplianceinsights.com.
Risk management failures in mid-sized and emerging companies have made headlines from Silicon Valley to Mumbai, often tracing back to a troubling disconnect between boardroom understanding and on-the-ground realities. This “board-versus-operational reality” gap in risk oversight has tangible consequences; from financial losses and regulatory penalties to reputational damage. A recent consulting survey indicated nearly 55% of board members say their company’s risk management struggles to keep pace with business strategy changes.
In an era of rising uncertainties, board members and independent directors are expected to serve as crucial sentinels, yet their effectiveness is often hampered by cultural and informational barriers. As a part of this series, we explore in this article as to why mid-sized enterprises are prone to governance gap, the real-world fallout when it goes unaddressed, and how boards can close the chasm between the view from the boardroom and the operational reality on the ground.
Understanding the Oversight Gap
Every corporate board has a fiduciary duty to oversee risk, but there’s often a disconnect between what boards believe about risk management and what’s actually happening within the organization. In many mid-sized firms, boards receive periodic risk reports and updates that paint a reassuring picture. Risks identified, controls implemented, compliance boxes checked. Yet the day-to-day reality in business units or project teams can be very different. Metrics and reports presented to the board may be incomplete or overly optimistic, leading to a false sense of security at the governance level.
Boards often overestimate risk management effectiveness due to incomplete information and structural weaknesses, leaving mid-sized firms vulnerable to crises. This gap is not due to negligence or indifference from boards, but rather structural and cultural challenges.
Root Causes of the Gap
Information Asymmetry: Senior executives may filter what they escalate to the board, and mid-level managers might downplay or fail to report issues upward, especially in a culture that ‘shoots the messenger’.
Limited Risk Expertise: Limited expertise in specific risk areas often exacerbates the problem. If directors aren’t well-versed in emerging risks (be it cybersecurity, regulatory compliance, or operational safety), they may not know the right questions to ask or may accept vague assurances. In fact, one analysis observed that a lack of operational risk expertise can make board members reluctant to stray from their domain.
Siloed Reporting: Operational risks are often tracked inconsistently, failing to reach the board in a meaningful way. Without the right data and Key Performance Indicators (KPIs), they might not realize the true magnitude of certain risks.
Differing Perspectives & Priorities: It helps to recognize that boards and operational teams often view risk through different lenses requiring better communication to align high-level oversight with ground-level realities.
Why Mid-Sized Companies Are Especially Vulnerable
Weak Risk Framework: Large multinational corporations often have extensive risk management frameworks, dedicated risk officers, and layers of oversight. In contrast, small and mid-sized enterprises (SMEs) frequently operate with leaner structures which can widen the board-operational gap. Research shows that many mid-sized companies do not have fully defined Enterprise Risk Management (ERM) programs due to cost constraints, limited resources, and fewer dedicated risk professionals.
Lean Structures: Often, employees wear multiple hats; for example, the finance head might also oversee compliance, or operations managers double as safety officers. This can lead to gaps in expertise and bandwidth when it comes to systematically identifying and mitigating risks. The board might assume that “someone in management” is handling risk, but in reality, risk responsibilities can fall through the cracks in a mid-size organization’s structure.
Rapid Growth: Mid-sized firms are frequently in high-growth mode. They are expanding into new markets, launching products, or undergoing digital transformation, all of which introduce new risks. However, governance processes in these companies often lag behind their growth. A post-mortem by regulators on Silicon Valley bank observed that the bank’s growth far outpaced the abilities of its board and management to install a suitable risk control infrastructure.
Cultural Pressures: A ‘Business Today’ magazine analysis of recent startup scandals noted a “convenient lack of oversight from boards, as start-ups get caught up in the rat race of growth over profits”. Mid-sized enterprises, especially those led by founders or family owners, can have tight-knit cultures with strong top-down influence. If the leadership’s emphasis is on aggressive growth or hitting targets “at all costs,” employees may feel pressure to prioritize results over risk compliance.
Weak Internal Controls: Mid-sized firms often lack the robust internal controls and audit functions that larger firms use to catch issues early. Risk assurance processes in a smaller company might be outsourced or minimal, and risk reporting may not be integrated company-wide. This means the board’s usual safety net, internal audit and compliance reports, may not be effective.
Governance lapses in mid-sized firms lead to serious failures, underscoring the need for boards to bridge the oversight gap. Recent cases illustrate how the board-operational disconnect fuels crises:
These examples across different sectors highlight the critical gap between boards oversight and operational realities, where incomplete knowledge of day-to-day operations led to risk management failures. Despite having boards and risk policies on paper, governance breakdowns allowed small issues to escalate into major crises. For mid-sized and emerging companies, closing the board-operations gap in risk oversight is not just a best practice but a strategic necessity for survival and success.
Closing the Gap: Practical Steps for Boards to Enhance Risk Oversight
Bridging the divide between boardroom perception and operational reality in risk management requires concerted action. Boards of mid-sized and emerging companies can take practical, actionable steps to enhance the sanctity of their risk oversight role. These steps span tools and technology, structural and process improvements, and cultural shifts. Below are key recommendations for boards and their companies:
Unfiltered Communication: Boards must insist on clear and candid risk reporting. Boards should demand that risk reports be forward-looking, impact-focused, and unfiltered. Instead of high-level summaries that gloss over issues, reports should explicitly connect risks to business outcomes. This can be done through reviewing “risk dashboards” that include key risk indicators, incident logs, and mitigation status updates for major / emerging risks.
Strengthen risk governance structure: Many mid-sized companies suffer because no single leader is accountable for enterprise-wide risk – plugging this gap is vital. Establish regular sessions where the risk officer and internal audit head can speak to directors without senior management in the room, fostering open communication.
Translate Technical Risks & Elevate risk discussions: Operational details (e.g., “unpatched firewalls”) should be framed in business terms (e.g., “potential $2M loss from a breach”).
Make risk a standing priority at every board meeting. Just as financial performance and strategy are regularly discussed, insist that significant operational and strategic risks get airtime in proportion to their importance. Boards could also consider scenario planning and deep-dives: pick a few “what if” scenarios.
Leverage Technology and Data for Risk Monitoring: In today’s digital age, even mid-sized companies can afford tools to enhance risk oversight. Boards should encourage management to utilize risk management software, dashboards, and data analytics to gain real-time visibility into risks. According to a 2025 survey, 76% of mid-market businesses already use technology in some aspect of risk management, but only 11% have fully integrated. There is immense room to grow here.
Fostering risk aware culture through appropriate tone at the top: Perhaps the most critical yet intangible fix is cultural. The board and executive leadership must set the tone that risk management is everyone’s responsibility and is valued. Leadership should visibly recognize and reward teams that identify and manage risks well, turning risk management successes into learning moments company-wide. Conversely, there should be accountability when risk processes are ignored or warnings silenced. The board could ask for a “Risk Culture” assessment. If results show problems say, the board must push management to address this through appropriate training.
As experts advise, boards should exercise an “inquisitive mindset; digging deeper, challenging assumptions, and encouraging open communication. All before adverse events materialize.”
In essence, bridging the gap requires aligning these perspectives. When governance and implementation are in sync, Boards can anticipate issues and support management in addressing them proactively, rather than cleaning up surprises after the fact.
The Strategic Role of Independent Directors in Risk Oversight
Independent directors are critical for objective oversight, challenging assumptions and fostering a risk-aware culture. Independent directors bridge the gap by:
Asking Tough Questions: Free from management ties, they probe operational realities (e.g., “Are cybersecurity resources adequate?”).
Bringing Expertise: Directors with cyber or compliance backgrounds enhance oversight, reducing financial irregularities (per governance surveys).
Setting Tone: By engaging risk managers directly and rewarding candor, they encourage issue escalation.
Leadership in Crisis: As seen in BharatPe (2022), independent director can direct investigation of misconduct, thus protecting stakeholder interests.
In summary, Independent Directors also play a strategic role as risk sentinels and governance champions. They must use their position to ensure the board isn’t operating with blind spots. As one LinkedIn corporate governance commentary put it, independent directors act as “ethical custodians, guardians of shareholder interests, and champions of accountability,” reinforcing structures that mitigate risk.
Conclusion: Strengthening the Board’s Risk Guardianship
We close this article with 10 sharp questions that we believe the board members & independent directors must ask in order to obtain comfort in the risk / governance framework within mid-sized enterprises. Obtaining comfort on these areas will naturally cascade into the direction and investments that need to be made towards better risk management.
As businesses globally navigate an increasingly volatile world; from cyber threats and supply chain disruptions to regulatory shifts and beyond, closing the board-operational reality gap will distinguish the resilient companies from the rest. With boards committing to the sanctity of their risk oversight role, mid-sized enterprises can confidently stride forward.
Sources:
AuditBoard Blog – “The Business Resilience Gap: A Tipping Point” (EY Global Board Risk Survey findings) auditboard.comauditboard.com.
Harvard Law School Forum (Glass Lewis post) – “Corporate Governance, Board Oversight & the 2023 Banking Crisis” (Analysis of SVB, Signature, First Republic failures) corpgov.law.harvard.educorpgov.law.harvard.edu.
Economic Times (India) – “What’s behind the CEO resignations in India’s private sector banks?” (Governance lapses in mid-tier banks) m.economictimes.com.
Business Today (India) – “How Zilingo’s Troubles Bring to the Fore Governance Issues at Start-ups” (Start-up governance lapses, Zilingo and BharatPe) businesstoday.inbusinesstoday.in.
Reuters – “Investors of India’s GoMechanic seek audit into ‘inflated’ financials” (GoMechanic startup financial fraud admission) reuters.comreuters.com.
ForensicRisk Alliance – “Navigating the Storm: learning from past corporate failures in the GCC” (Gulf corporate governance failures and lessons) forensicrisk.com.
dss+ Consulting – “When Boards Miss the Warning Signs: Elevating Operational Risk Oversight” (Operational risk oversight challenges and recommendations) consultdss.comconsultdss.com.
LinkedIn Pulse – “Independent Directors: Navigating Corporate Governance” (Role of independent directors in risk oversight and culture) linkedin.com.
BusinessToday (India) – “YES Bank independent director…resignation letter” (Yes Bank governance failure, independent director protest) businesstoday.inbusinesstoday.in.