In today’s enterprise technology landscape, vast investments are being made in artificial intelligence, data infrastructure, and digital collaboration tools. Yet many organisations continue to struggle with a simple question: why does the return on these investments often fall short of expectations?
For Sandy Hardikar, Co-Founder and CEO of Network Science, the answer lies in a structural gap within enterprise technology architecture — the absence of what he calls a “System of Context.”
With years of experience working at the intersection of enterprise data, decision systems, and organisational intelligence, Hardikar has built his perspective around a problem he believes most enterprises underestimate: while companies have systems that store data and systems that help teams communicate, they lack systems that preserve the context behind decisions.
Understanding the Missing Layer in Enterprise Technology
According to Hardikar, enterprise technology historically evolved through two major layers.
The first layer, known as Systems of Record, focuses on capturing operational data and transactions. These include enterprise platforms such as ERP systems, CRM platforms, and internal databases that store structured business information.
The second layer emerged with Systems of Engagement, enabling teams to collaborate through communication platforms, emails, messaging tools, and workflow applications.
While these two layers transformed the way organisations operate, Hardikar believes they left a crucial gap unaddressed.
“Enterprises today generate enormous volumes of information,” Hardikar explains. “But the challenge isn’t access to data. The real problem is the lack of context that explains why decisions were made and how insights evolved.”
Why Information Without Context Slows Enterprises Down
From Hardikar’s perspective, most organisations are not suffering from a shortage of information. Instead, they struggle with fragmented access to knowledge spread across multiple systems.
Financial insights may reside inside ERP platforms, customer intelligence inside CRM tools, and operational metrics inside analytics dashboards. Yet employees often need to navigate across multiple tools to assemble a full picture before making a decision.
In Hardikar’s view, this constant reconstruction of context slows decision-making and reduces organisational efficiency.
Employees spend time piecing together insights rather than acting on them.
The Problem of Institutional Memory
Hardikar also points to another hidden issue: the disappearance of institutional knowledge.
In most organisations, the reasoning behind major decisions rarely gets captured in structured systems. Instead, it lives inside presentations, meeting notes, internal discussions, and scattered communication threads.
When teams change roles or projects evolve, this reasoning often disappears.
“The numbers remain, but the thinking behind them is lost,” Hardikar notes.
This results in repeated analysis, slower onboarding of new team members, and decision cycles that take longer than necessary.
Why AI Investments Often Fail to Deliver ROI
Despite significant spending on artificial intelligence, Hardikar observes that many enterprises still struggle to demonstrate meaningful returns.
While AI tools are widely adopted for tasks such as summarising documents or generating content, their deeper potential — supporting strategic decisions and improving outcomes — often remains untapped.
Hardikar believes this happens because most AI systems operate without sufficient organisational context.
Without understanding enterprise workflows, past decisions, and institutional knowledge, AI tools can generate outputs but cannot reliably influence outcomes.
“This is why many leadership teams struggle to demonstrate ROI from AI initiatives,” he explains.
Introducing the System of Context
Through his work at Network Science, Hardikar has been advocating for the creation of a third architectural layer in enterprise systems: the System of Context.
Unlike traditional systems that focus solely on storing data or enabling communication, the System of Context focuses on capturing how decisions evolve, how teams interact with knowledge, and how workflows ultimately produce outcomes.
Over time, this layer builds what Hardikar describes as a persistent intelligence network across the organisation.
Employees no longer begin with raw data. Instead, they begin with questions — and the system responds with insights grounded in the organisation’s collective knowledge.
Turning Intelligence Into Measurable Outcomes
For Hardikar, the real value of contextual intelligence lies in measurable outcomes.
Tasks that previously required hours of analysis can be completed in minutes. Strategy presentations can be generated faster. Complex documents can be summarised and understood instantly. Decisions can be supported by insights drawn from multiple sources simultaneously.
When outcomes improve, the return on technology investments becomes clearer.
And according to Hardikar, this is where enterprises will begin to see the real promise of artificial intelligence.
“In the future, the most successful organisations won’t just have more data or better AI tools,” he says. “They will have systems that understand the context behind how their organisations think, decide, and operate.”
The above information is the author's own; Outlook India is not involved in the creation of this article.
