Key Takeaway
The rush to adopt agentic AI has left many enterprises vulnerable, as platforms may compromise long-term competitiveness. Kanishk Mehta from Quantiphi Analytics warns of a data security crisis, with nearly 40% of employees sharing sensitive information with public AI tools. Quantiphi’s baioniq platform operates within a company’s existing infrastructure, ensuring data sovereignty and protecting proprietary assets. It features specialized agents for various industries and has shown significant efficiency gains. Kanishk emphasizes that enterprises must invest in their AI capabilities to remain competitive in an evolving landscape, highlighting the risks of relying on external AI services.
The surge in agentic AI has left many enterprises facing an unsettling reality: the platforms they hastily adopted may jeopardize their long-term competitiveness.
This concern is echoed by Kanishk Mehta, a product leader at AI consulting firm Quantiphi Analytics, who has observed companies struggling with the unintended consequences of their AI adoption strategies.
Since 2013, Quantiphi has been providing AI and data science consulting services and has developed baioniq, an agentic AI platform designed to function within customer infrastructure rather than relying on external cloud environments.
Kanishk’s warning stems from an emerging data security crisis, where nearly 40% of employees share company secrets with public AI tools without authorization, a figure that rises to 46% among younger workers.
“This isn’t theoretical; it’s happening right now in organizations across every industry,” he states.
“When employees use public AI tools with company data, they’re essentially broadcasting your competitive intelligence to the world.”
How data sovereignty drives baioniq development
This issue has become more pressing as AI companies encounter business continuity challenges while customers express concerns about data being locked into external platforms.
Traditional cloud-based AI services require organizations to send sensitive information to third-party providers, creating vulnerabilities that many enterprises are only beginning to grasp.
Recent surveys indicate that employees frequently share sensitive company data with consumer AI tools, putting organizations at risk of data leaks.
When companies utilize external AI services, their prompts, training data, and model improvements often become shared resources instead of proprietary assets.
How Baioniq addresses enterprise control requirements
Quantiphi’s baioniq operates within existing virtual private cloud infrastructure—the secure computing environments that companies use to run applications and store data.
This architecture sets it apart from cloud-based AI services that process customer data on external systems.
“The fundamental differentiator is deployment architecture,” Kanishk clarifies.
“The platform is deployed within your existing cloud infrastructure—your VPC, behind your firewall—ensuring complete data sovereignty.”
The platform connects to enterprise data through 37 connectors, creating intelligent retrieval systems that comprehend context and intent rather than merely matching keywords.
With baioniq, these assets “remain exclusively yours, creating intellectual property that appreciates over time,” Kanishk asserts.
The platform also includes pre-built agents for specific industries, such as pharmacovigilance systems for life sciences companies monitoring adverse drug events and underwriting agents for insurance risk assessment.
“These aren’t generic chatbots; they are specialized AI systems designed to tackle complex industry challenges,” Kanishk explains.
The three phases shaping the AI adoption evolution
Kanishk, who has dedicated over six years at Quantiphi to developing enterprise AI solutions, identifies three phases of adoption.
The initial phase focuses on democratizing AI, making it accessible beyond IT departments.
The second phase involves developing AI that understands business contexts.
The final phase envisions autonomous AI systems managing complex processes.
Quantiphi recognizes what Kanishk describes as the reality that “most enterprises will operate in a multi-vendor AI environment” rather than relying on a single provider.
The company also reports measurable improvements from baioniq implementation: a 50% increase in knowledge worker efficiency, a 60% acceleration in task automation, and an 80% reduction in content summarization time.
Quantiphi employs baioniq internally, providing validation while informing development based on actual usage patterns.
“Every day you delay is a day your competitors are building advantages that will define the next decade,” Kanishk concludes.
“This is a long-term investment that positions your enterprise to compete in an AI-native economy.
“The question isn’t whether you can afford to own your AI stack—it’s whether you can afford not to.”
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