AI Appreciation Day is observed every year on July 16 to recognise the innovation, potential, and ever-expanding role of artificial intelligence around the world. In 2026, it stands as a reminder to use AI in secure, robust and responsible ways, to reflect on how far it has come, and to make the most of its benefits for the good of humanity and our future. 

We speak with leading B2B innovators and how they are paving the way for AI to help organisations strengthen the trust, security, privacy and governance of their customer data and infrastructure.

Enterprise customer experiences powered by AI and trusted data

As enterprise AI adoption accelerates, organisations are discovering that AI is only as effective as the customer context it can trust and act on. According to a recent IDC MarketScape report, the role of the modern customer data platform (CDP) extends beyond data unification. 

CDPs need to take on an expanded definition and serve as the context layer by exposing customer context, organisational context and decision context – That humans and AI agents can act on.”

It cited Amperity, the AI-powered Customer Data Cloud as a leading example of enterprise AI adoption, a solution particularly relevant for enterprise B2C organisations where “durable identity, data quality, marketer usability, and cross-functional operationalisation matter as much as profile unification itself.”

“Enterprise AI is only as effective as the customer context behind it. As organisations move beyond experimentation and into production, they need data they can trust and context that’s current enough for AI to make confident decisions,” said Derek Slager, co-founder & co-CEO at Amperity. 

‘Shadow AI’: managing the scale, risk and consequences of unmanaged AI

With generative AI adoption now outpacing the governance around its use, the risk horizon with respect to sovereign data and the emigration of confidential information is growing rapidly.

Compounding this problem is professionals are exposing sensitive company data to AI platforms, most of it through personal accounts on tools their employer cannot see or control, creating a new ‘shadow AI’ epidemic. 

To navigate the new age of shadow AI in enterprises, startup ORCA Opti recently released Opti Assist Free, a no-cost, sovereign AI governance assistant built for regulated organisations. The solution directly addresses the widening gap between how employees are using AI and what regulators now expect from their employers.

The solution does not send user inputs to third-party AI providers, and does not train on customer data. Organisations sign up with a Microsoft 365 work or school email account, with no credit card, no procurement approval, no trial period. 

LtoR: Derek Slager, Co-founder & Co-CEO at Amperity | Kathryn Giudes, Founder and Managing Director of ORCA Opti | and Ned Shawa, Vice President & Field CTO, Solutions Engineering – APJ at Kong

“Banning ChatGPT did not work for Samsung, JPMorgan or Apple, and it will not work for a local council, hospital or defence supplier either,” said Kathryn Giudes, Founder and Managing Director of ORCA Opti.
“The lesson was never ‘ban AI’. The lesson was ‘ungoverned AI is the risk.’ Regulators have accepted that AI is inevitable. What they will not accept is that organisations can no longer say where their data went, who used it, or which foreign model is now trained on it. That is the visibility gap.
“Opti Assist Free is how we close it, not by banning AI, but by giving people a version of it they can safely say yes to.”

Rethinking agent-scale traffic in enterprise infrastructure

Enterprises also need to reconsider secure and robust API infrastructure capabilities as agentic AI becomes more embedded in workflows.

According to Ned Shawa, Vice President & Field CTO, Solutions Engineering – APJ, Kong, if you’re running technology for an AI-powered enterprise today, the question isn’t whether to build APIs. The question is whether your APIs are ready to be the primary interface to your business and whether they are ready to be exposed to the wild and handle exponential agent clicks.

“That means rethinking rate limiting for agent-scale traffic, context mesh and MCP autobuilds, prompt injection protection, guardrails and PII sanitisation if we are exposing AI,” he said. “It means authentication flows that work for machines, not just humans. It means metering that tracks API consumption as a revenue metric, not just an infrastructure metric, although chargebacks and showbacks are as important. And it means pricing models that align with how agents consume your services — per-call, per-transaction, per-outcome.”

Kong’s latest raft of product announcements are centred on helping enterprises build and govern AI applications at scale. Recent launches include Insomnia’s integration with Kong Konnect, Context Mesh, the MCP Registry within Kong Konnect for discovering and governing AI agent connectivity, and an enhanced Kong AI Gateway with new Agent Gateway capabilities.