AIPath.one Founder David Isaac Mathews Reveals How Decision Intelligence Is Replacing the Strategy Layer That Enterprise Software Never Built

SINGAPORE, July 03, 2026 — Eighty percent of software features built by engineering teams are rarely or never used — a waste that costs the average mid-market company $1 million per year in misallocated output. That single statistic sits at the heart of why David Isaac Mathews founded AIPath.one, a Singapore-based Decision Intelligence platform that is rapidly redefining how product, engineering, and go-to-market teams decide what to build next. The company has won the USD $1 million AI Agents Global Challenge, been selected for Cohort 1 of the HP Garage 2.0 AI Accelerator at HP's Singapore campus, and recorded month-on-month growth of over 500 percent, with more than 1,800 companies onboarded since its earliest launch phase.

What the Market Was Missing

For two decades, enterprise software excelled at describing what already happened — dashboards from Tableau, Snowflake, and Power BI — and executing what had already been decided, via tools like Jira, HubSpot, and Salesforce. The strategy layer, the point where organisations actually decide which bets to make, remained entirely manual. In January 2026, Gartner formally recognised the emerging category designed to fill that gap: Decision Intelligence Platforms. AIPath.one positions itself as the first platform built specifically to serve that layer for growth strategy, sitting upstream of the execution and analytics tools companies already pay for.

Isaac Mathews has a track record that maps directly to the problem he is solving. He previously served as ASEAN Head of Innovation for EY-Parthenon and as Chief Growth Officer for GrowthOps, a publicly listed company (ASX: TGO). He has led strategy engagements for Samsung's product-innovation team, driven ASEAN digital innovation at EY-Parthenon, and consulted for leaders across banking, fintech, e-commerce, and cybersecurity. He is also a member of SMU's pioneer graduating class and a Stanford GSB alumnus. That combination of top-tier consulting pedigree and operator experience informs the platform's architecture.

How AIPath.one Works

The platform combines internal product, customer, and deal data with public competitor signals and AI-generated digital twins to simulate markets, prescribe what to build, and validate those decisions through experiments — before a single line of code is written. Its AI engine is capable of generating and evaluating more than 3,000 growth hypotheses across five strategic dimensions, surfacing the product and go-to-market bets with the highest-leverage potential. One documented client outcome: customer acquisition cost reduced from $240 to $43 in the insurance category, achieved through AIPath's evidence-backed roadmap methodology.

The platform also automates the alignment problem that plagues cross-functional teams. Every strategy change is instantly reflected in go-to-market collateral — landing pages, ad creative, drip emails, and pitch decks — meaning product and marketing assets never fall out of sync. For organisations evaluating multiple strategic directions simultaneously, AIPath can run parallel digital-twin scenarios across dozens of niches and rank them by probability of achieving product-market fit, giving leadership a data-driven basis for budget allocation decisions.

Recognition and Accelerator Momentum

The $1 million win at the AI Agents Global Challenge, hosted by Agentplex, was a public validation of the platform's technical architecture and commercial thesis. Shortly after, AIPath.one was selected as one of just ten startups for Cohort 1 of HP Garage 2.0 — launched at HP's Singapore campus to coincide with the company's 55th anniversary in the country — with a mandate to advance AI-driven next-generation decision-making. The platform has also been featured on CNA and presented at the AWS x NVIDIA Gen AI BuildPad, the Silicon Valley Summit with Plug and Play and Enterprise Singapore, Singapore Design Week, and SGInnovate's AI Pathways programme. Over half of AIPath's client base is based in Palo Alto, California, a signal of traction well beyond Southeast Asia.

"Most organisations have spent years building the data layer and the execution layer, but nobody built the decision layer," said David Isaac Mathews, Founder and CEO of AIPath.one. "We built it. The companies winning right now are not the ones with the most data — they are the ones making faster, better-validated strategic choices before committing engineering resources to find out if they were right."

The Big Picture

The stakes are measurable. According to Pendo research cited by Isaac Mathews, the 80 percent feature-waste figure translates to $1 million per year in lost engineering output for a ten-person team — and hundreds of millions at enterprise scale. The average company already spends roughly $350,000 annually on tools that describe and execute. AIPath's argument is that none of those tools answer the foundational question: what should we build next, for whom, and why will it win?

With Gartner's formal recognition of the Decision Intelligence Platforms category, that question now has a named market segment behind it — and AIPath.one intends to define it. The platform's continuous learning model means it gets smarter about a client's market every week, compounding strategic memory in a way no consulting engagement can. For ambitious product and C-suite leaders navigating an era of constrained budgets and accelerating AI competition, the company's trajectory in 2026 signals that the strategy layer is no longer optional infrastructure — it is the next competitive frontier.