How AI Observability Turns Data Into Better Business Decisions

AI observability is quickly becoming one of the most consequential shifts in enterprise AI—not because it adds more dashboards, but because it exposes how AI systems actually behave inside real business workflows. For executives, that visibility is both a breakthrough and a burden. It reveals model performance, data quality, user interaction patterns and system drift in real time, yet it often arrives in a form that is fragmented, technical and difficult to translate into decisions that matter at the board level.

Organizations are rapidly scaling generative AI and machine learning systems across core operations, but many are struggling to operationalize oversight in a way that connects technical signals to measurable business outcomes. The result is a widening gap between AI capability and executive clarity—where systems are increasingly powerful, but not always understandable in business terms.

Members of the Senior Executive AI Think Tank—a curated group of leaders in machine learning, generative AI and enterprise transformation—argue that the issue is not a lack of data. It is a lack of translation. AI observability, they note, only becomes strategically meaningful when organizations move beyond monitoring and toward decision-making frameworks that connect model behavior, risk signals and user impact directly to business KPIs.

In the sections that follow, Think Tank members break down how organizations can close this gap in practice—from building operating models that turn observability into action, to identifying behavioral drift before it becomes business risk, to redefining governance so insights don’t remain trapped in technical teams. They also surface the most persistent obstacles executives face today—including signal overload, fragmented ownership and the absence of shared language between business and technical stakeholders—and offer concrete ways leaders can turn visibility into decisions that drive measurable value.

Build an Operating Model That Turns Signals Into Decisions

Organizations often believe observability begins and ends with monitoring dashboards. According to Gaurav Rastogi, Senior Director of Enterprise Data Analytics, Data Science and Strategic Insights at Hertz, that mindset leaves tremendous business value untapped.

Rastogi says AI observability only becomes meaningful when organizations establish a repeatable process for converting technical signals into operational decisions.

“AI observability gives organizations deep visibility into data pipelines, models and user interactions—but visibility alone does not create business value.”

Instead, he advocates for a structured operating model rather than another collection of monitoring tools.

“The five-point framework—prevention, detection, diagnosis, correction and learning—turns observability from passive monitoring into an action system,” he says.

That progression helps organizations move beyond simply identifying anomalies toward assigning ownership, correcting problems quickly and ensuring each incident improves future operations.

Rastogi believes many organizations still struggle because the underlying operating model hasn’t evolved alongside the technology.

“The biggest obstacles are signal overload, fragmented ownership across data, model and business teams, and lack of workflows that convert insight into action.”

Measure Behavioral Drift Instead of Chasing Every Signal

Executives frequently ask why an AI model behaves differently over time. Blake Crawford, Partner and CTO at Fusion Collective, believes many organizations ask the wrong question altogether.

Crawford argues that leaders often become consumed by granular technical metrics while overlooking broader behavioral trends that matter more strategically.

“Most people view observability at a far too granular level and miss the forest for the trees,” he says.

Instead of investigating every individual model decision, he recommends establishing a clear behavioral baseline that reveals how systems evolve over time.

“Model behaviors are guaranteed to drift,” he says. “Most organizations will benefit most from monitoring the behavioral envelope of a model to measure that drift over time.”

By understanding what constitutes normal behavior, executives gain a much stronger benchmark for evaluating future changes, replacing vendors or comparing new models before deployment.

“Understanding characteristic behavior provides a solid baseline against which comparisons can be made.”

As organizations increasingly deploy multiple large language models simultaneously, behavioral benchmarking provides leaders with more meaningful indicators of organizational risk and resilience than individual model outputs alone.

For Crawford, observability should ultimately provide confidence—not just diagnostics.

“It also gives you a leg up on switching models or model providers should you ever need to do so.”

“The biggest obstacle isn’t the technology or a lack of visibility; it’s a fundamental failure of imagination.”

– Geetha Kumari Kommepalli, Founder, CTO and Chief AI Officer at Thriven Advisory

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Build New Business Models Instead of Optimizing Old Ones

For Geetha Kumari Kommepalli, Founder, CTO and Chief AI Officer at Thriven Advisory, AI observability is not simply a governance capability—it is an opportunity to rethink how organizations operate. Drawing on more than two decades leading ERP implementations, M&A integrations, digital transformation initiatives and AI-enabled operating model redesigns, she argues that many executives are limiting AI’s potential by using it to improve legacy processes rather than reinvent them.

“The biggest obstacle isn’t the technology or a lack of visibility; it’s a fundamental failure of imagination,” Kommepalli says.

She believes many organizations continue to force AI into outdated operating models instead of redesigning business architecture around what AI can make possible.

“Executives are trapped trying to force highly capable AI into legacy, analog workflows,” she says. “True observability should be used to completely restructure the business architecture, yet leaders are stuck using it just to monitor old processes faster.”

According to McKinsey & Company, organizations generating the greatest value from AI are redesigning end-to-end workflows rather than automating isolated tasks, allowing AI insights to reshape how work moves across departments instead of merely accelerating existing processes.

For Kommepalli, the opportunity extends beyond better reporting or governance. It requires leadership teams to fundamentally rethink value creation.

“We are drowning in data but starving for vision,” she says. “Organizations will only turn visibility into actionable decisions when leadership stops looking at AI as a localized productivity tool and starts reimagining entire cross-functional value chains from scratch.”

Focus on Business Context Instead of Technical Metrics

Technical dashboards can confirm whether AI infrastructure is functioning properly, but Mohan Krishna Mannava, Data Analytics Leader at Texas Health, believes executives need a very different lens if they want observability to support strategic decision-making.

Rather than emphasizing uptime, latency or server utilization, Mannava recommends focusing on indicators that reveal whether AI continues to understand changing customer behavior: “Organizations must look past basic technical metrics and focus on ‘semantic drift.'”

He describes semantic drift as the point at which an AI model gradually loses alignment with real-world customer expectations, often long before traditional monitoring systems identify a problem.

“Most companies get stuck looking at uptime or server speeds, which are just technical vanity metrics that fail to show whether the AI is actually making good business decisions,” he says.

The challenge, Mannava says, is translating engineering observations into language executives can use to assess business risk.

“The biggest obstacle preventing executives from gaining meaningful insights is the ‘Context Gap.'”

That disconnect frequently leaves technical teams discussing infrastructure issues while executive leadership worries about customer satisfaction, regulatory exposure or revenue performance.

“Top leaders must act as Context Architects, translating raw data logs into clear business insights,” he says.

Ultimately, observability should help organizations anticipate financial consequences before customers experience them.

“True observability means knowing exactly how a tiny shift in an AI’s reward function will impact bottom-line revenue before it happens.”

“Like AI content, AI observability can create ‘slop’—verbose, inconsistent logs that overwhelm executives.”

– Manu Agrawal, Gen AI Leader and Architect at Oracle

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Cut Through the Noise With Standardized Signals

As enterprise AI systems become more sophisticated, organizations are collecting unprecedented volumes of operational data. According to Manu Agrawal, Gen AI Leader and Architect at Oracle, the challenge is no longer obtaining visibility—it’s identifying which signals actually matter. She says organizations should connect technical performance directly to measurable business outcomes.

“AI observability creates value only when visibility is translated into decisions. Organizations should connect model behavior, data quality, user interactions, latency, cost, accuracy, risk and workflow outcomes to business KPIs,” she says.

Instead of generating ever-larger collections of logs, observability should highlight where trust, compliance, customer experience or operational performance begin to deteriorate.

“The goal is not just more logs, but better signals,” she adds.

Agrawal warns that observability itself can become another source of information overload if organizations lack standardized governance: “Like AI content, AI observability can create ‘slop’—verbose, inconsistent logs that overwhelm executives.”

To combat that problem, she recommends standardized metrics spanning models, systems, workflows and business outcomes, paired with automated alerts, clear ownership and disciplined root-cause analysis.

“Leaders turn modern AI observability into strategic business decisions by embedding real-time model telemetry directly into corporate performance dashboards.”

– Uttam Kumar, Engineering Manager at American Eagle Outfitters

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Tie Observability Directly to Business Performance

AI observability earns executive attention when it demonstrates measurable commercial impact. Uttam Kumar, Engineering Manager at American Eagle Outfitters, says technical visibility becomes strategic only when it is expressed in business terms.

“Leaders turn modern AI observability into strategic business decisions by embedding real-time model telemetry directly into corporate performance dashboards.”

That integration transforms abstract technical metrics into outcomes that resonate with executive leadership, such as “increased customer lifetime value and reduced operational waste.”

Despite those advantages, Kumar sees many organizations struggling to articulate the financial case for AI observability.

“The major obstacle for executives remains hazy project objectives and an inability to accurately calculate the financial ROI of complex AI infrastructure,” he says.

Without clearly defined business metrics, observability initiatives risk being perceived as technical overhead instead of strategic investments. His advice is to establish measurable business objectives before implementing observability platforms. When technical metrics are tied to revenue growth, customer retention, operational efficiency or cost reduction, executive support becomes significantly easier to sustain.

“Clear business goals turn technical visibility into strategic value,” he says.

Turn Measurement Into a Competitive Advantage

For Brock Murray, Co-founder of seoplus+, AI observability is evolving from an operational necessity into a source of competitive differentiation. As organizations gain better visibility into AI performance, the companies that consistently outperform will be those that translate measurements into continuous business improvement.

“Visibility only matters if it drives decisions,” he says. “Real value comes from connecting observability data to business outcomes such as customer experience, operational efficiency, revenue growth, risk reduction and competitive positioning.”

He also sees rapid advances in benchmarking tools that enable organizations to compare AI performance across initiatives and identify where investments produce the strongest returns.

“Measurement is becoming a strategic advantage. Tools are emerging that can benchmark AI performance, identify opportunities and show what is actually driving results. This shift is moving AI from experimentation to informed decision-making,” he notes.

Still, Murray believes technology alone cannot solve the problem. Successful organizations establish ownership, invest in AI literacy and create processes that encourage continual improvement.

“The biggest obstacles are data overload, lack of expertise and unclear ownership.”

Without those foundations, even sophisticated observability platforms generate little practical value.

“An organizational commitment to continuous learning is critical for staying competitive as AI continues to evolve.”

Create a Shared Language Between Business and Technology

AI observability succeeds only when executives and technical teams interpret information through a common framework. According to Sabarinath Yada, Business Architect Associate Manager at Accenture, organizations frequently possess abundant operational data but lack the governance structures needed to translate it into confident business decisions.

“The real challenge is not collecting signals but translating them into timely, trusted and business-aligned decisions,” he says. “Organizations should establish unified metrics that map model indicators such as drift and accuracy to business KPIs, creating a shared language across stakeholders.”

Yada also believes executive education plays an increasingly important role as AI systems become more sophisticated.

“Equally critical is cross-functional fluency, where executives grasp key technical concepts and technologists clearly articulate business impact.”

This shared understanding reduces delays, improves governance and enables faster responses when AI performance changes.

“The future of observability lies in decision-centric platforms that enable organizations to act with clarity, confidence and accountability,” he says.

Translate Technical Data Into Executive Decisions

Organizations often assume the biggest barrier to AI observability is a lack of data. Goran Paun, Principal and Creative Director at ArtVersion, believes the opposite is true: “The biggest obstacle is usually not lack of data. It is too much data without useful interpretation.”

That disconnect becomes particularly apparent between technical and executive teams, where each group evaluates AI through a different lens.

“Technical teams may see logs, latency, rates, failures and usage patterns, while executives see only a high-level promise of efficiency.”

Paun says organizations need governance structures that clearly define ownership and accountability before observability can drive meaningful decisions.

“Someone has to decide which signals require intervention, who owns the response and how insights get fed back into product, operations, compliance and customer experience,” he says.

Without those mechanisms, even sophisticated observability platforms risk becoming little more than passive reporting tools.

“Observability becomes just another dashboard executives glance at but cannot act on.”

Design Governance Before AI Goes Live

As organizations move beyond single AI models toward autonomous agents and multi-agent ecosystems, observability itself must evolve. Pon Murugesh Devendren, SAP Enterprise AI Architect at Deloitte, believes many existing observability platforms were designed for a simpler generation of AI and are no longer sufficient.

“The shift is from monitoring single models to monitoring multi-agent systems, and most observability tools weren’t built for that,” he says. “A model dashboard shows accuracy and latency, not which business capability an agent supports, who owns it or whether it’s creating value or risk.”

Devendren argues that observability becomes strategically valuable only when technical telemetry is connected to enterprise architecture and financial performance.

“Once leaders see which capability an agent serves and what it’s worth in dollars, they can intervene in real time when behavior degrades, redirect investment toward agents proving ROI and spot duplication across the portfolio.”

The biggest challenge, he says, isn’t collecting telemetry. Organizations already possess enormous amounts of operational data.

“The telemetry exists; the governance to act on it often doesn’t,” he says.

By incorporating ownership, human oversight and business metrics during solution design—not after deployment—leaders can ensure observability becomes a management capability instead of another reporting exercise.

Practical Steps to Transform AI Visibility Into Value

• Create an operating model, not just an observability platform. Build structured processes for prevention, detection, diagnosis, correction and continuous learning so AI insights consistently lead to business action.
• Measure behavioral drift over time. Establish baseline model behavior and monitor deviations instead of focusing exclusively on individual technical anomalies.
• Use observability to redesign workflows. Look beyond optimizing existing processes and identify opportunities to reinvent cross-functional business operations around AI.
• Translate technical metrics into business context. Monitor indicators that reveal customer impact, financial risk and strategic performance rather than infrastructure metrics alone.
• Standardize the signals that matter. Connect model behavior, user interactions, costs, compliance and workflow performance to business KPIs while reducing unnecessary noise.
• Tie AI telemetry to executive dashboards. Express observability in terms of customer lifetime value, operational efficiency, revenue growth and ROI to strengthen executive decision-making.
• Treat measurement as a competitive advantage. Benchmark AI performance continuously and establish clear ownership so insights consistently drive improvement.
• Create a shared language across stakeholders. Align executives, business leaders and technical teams around unified metrics that connect AI performance with business outcomes.
• Define governance and accountability early. Assign ownership for interpreting signals and ensure insights feed directly into operational, compliance and customer experience improvements.
• Design governance before deployment. Build ownership, human oversight and business metrics into AI systems from the beginning, especially as organizations adopt multi-agent architectures.

Visibility Is Only the Beginning

AI observability is rapidly evolving from an engineering discipline into an executive capability. While modern platforms provide unprecedented visibility into models, data pipelines and user interactions, the members of the Senior Executive AI Think Tank consistently emphasize that technology alone does not create business value. Organizations realize the greatest return when they connect observability to governance, ownership, financial performance and continuous organizational learning.

As enterprises deploy increasingly sophisticated AI systems, executive success will depend less on collecting more information and more on designing better decision-making frameworks. Leaders who translate technical visibility into measurable business outcomes will be better positioned to build trustworthy AI systems, improve operational performance and adapt confidently as enterprise AI continues to mature.