Business across all verticals, are grappling with the challenge of effectively leveraging their data. As I travel the globe working with organizations of varying sizes and industries, one phrase I hear repeatedly is, the AI revolution is here we will wait and “just let AI figure it out.” This well-intentioned but oversimplified approach often leads to disappointment and wasted resources.
My journey to understanding effective observability strategy didn’t begin in corporate boardrooms. Rather, it was shaped by unlikely beginnings—from teaching scuba diving to in South Africa to navigating dive boats through Scotland’s challenging waters. These experiences taught me valuable lessons about risk management, adaptability, and the importance of clear visibility into operations.
Today, as the leader of Full Stack Observability at HCLSoftware, I’ve applied these principles to develop frameworks that help organizations transform raw data into actionable intelligence. Let me share a structured approach that has proven effective across multiple industries.
The Three Phases of Observability Maturity
1. Specialization: Developing Domain-Specific Intelligence
The foundation of effective observability begins with specialization—understanding what truly matters to your specific business context. This requires:
- Starting with business outcomes, not technology solutions. Too often, organizations implement monitoring tools without clarity on the business problems they’re trying to solve.
- Taking a dual approach: Top-down by defining executive KPIs (typically rule-based aggregated metrics) and bottom-up by connecting operational IT metrics. This bidirectional approach ensures alignment between technical capabilities and business priorities.
- Value stream identification: Clearly understand your critical business processes first, then identify what “good” looks like. These become your critical control points.
During my time co-founding Alpha Insight (later acquired by HCL Technologies), we discovered that focusing on business process performance monitoring for the banking sector required deep domain expertise. We couldn’t simply deploy generic monitoring tools—we needed to understand banking operations intimately.
2. Differentiation: Transforming Data into Contextual Intelligence
The second phase focuses on differentiation—creating unique value from your observability data:
- Accelerating time-to-value by implementing quick wins across key value streams before expanding the solution. This builds stakeholder confidence and demonstrates tangible benefits.
- Connecting siloed data sources to create a unified control view. Most organizations struggle not from lack of data but from fragmented visibility.
- Enabling real-time risk scoring with impact analysis that executives can understand and act upon. Technical metrics must be translated into business impact terms.
My experience leading teams through complex re-platforming projects taught me that success hinges not on collecting more data, but on providing the right context. By focusing on meaningful connections between technical events and business outcomes, we’ve helped organizations reduce mean time to resolution by over 60%.
3. Institutionalization: Building Centres of Excellence
The final phase involves embedding observability into your organizational culture:
- Implementing a “crawl-walk-run” methodology that delivers value at each stage. Even just understanding the value stream end-to-end provides significant benefit before adding sophisticated monitoring.
- Stimulating feedback loops between business and IT to ensure focus remains on critical flows and controls.
- Making configuration accessible to business users without complicated IT dependencies.
- Beginning every initiative with a business outcome workshop, not a technical assessment.
At HCLSoftware, our IFSO HIVE platform embodies these principles, helping organizations institutionalize observability practices across departments and functions.
The Human Element in AI-Driven Observability
While technology plays a crucial role, my leadership journey has taught me that successful observability initiatives depend equally on people and culture. Building trust through clear communication, fostering continuous learning, and emphasizing teamwork have been cornerstones of my approach.
The most challenging period in my career occurred while running Alpha Insight, balancing the demands of a growing startup with family life. This experience reinforced that even the most sophisticated technologies require human guidance, perspective, and ethical considerations to deliver meaningful value.
Looking Forward: AI as a Tool, Not a Magic Solution
The most successful organizations don’t view AI as a magical solution but as a powerful tool within a well-structured observability framework. They understand that the quality of AI insights depends entirely on the quality of the underlying data strategy and business context.
By taking a dual approach of top-down strategic alignment and bottom-up technical integration, you ensure that every metric, every alert, and every dashboard directly supports your business objectives. Only then can AI drive deep insights using this framework.
Conclusion
The secret to successful observability isn’t implementing more technology—it’s ensuring you have a well-defined framework aligned with business outcomes, specifically key value streams. This fundamental shift in approach means you’re focused and spending your resources wisely, harnessing data with AI-assisted insights to drive innovation, manage risk, and accelerate growth.
As someone who has navigated from the cold waters of Scotland to leading technological innovation in global enterprises, I’ve learned that visibility—whether into dangerous underwater currents or complex business processes—is never something you can simply “let AI figure out.” It requires strategic thinking, domain expertise, and a clear understanding of what success looks like.
In observability, as in diving, ‘Plan the dive and dive the Plan’, the clearer your vision, the safer and more successful your journey will be.
About the Leader
Ian Philips is the General Manager for Full Stack Observability at HCLSoftware. His journey from diving instructor to technology executive exemplifies how unconventional paths can forge exceptional leaders in the technology sector. Under his leadership, HCLSoftware has earned numerous accolades, including Silver and Bronze Stevie Awards for innovation in business intelligence and compliance solutions.