Bryan Harris: Building Trust in Workforce Is the First Step to Building Trust in AI

Why It Matters

Building internal trust removes the biggest barrier to scalable AI, while deterministic analytics and emerging quantum capabilities give SAS a defensible edge in high‑stakes, regulated markets.

Key Takeaways

• Trust between leadership and workforce drives AI adoption
• SAS generates 11‑15 M AI‑written code lines monthly
• Deterministic analytics meet regulated industry demands
• Pune R&D expands 20‑30% annually, becoming strategic
• Quantum computing could cut AI training time dramatically

Pulse Analysis

Leadership‑driven trust is emerging as the linchpin of AI rollout in enterprises. Harris argued that when executives frame AI as a cost‑cutting weapon, employees resist, causing pilots to languish. By fostering a culture where AI tools are seen as collaborators rather than job‑threats, companies can accelerate on‑premise and hybrid deployments—critical in markets like India where cloud‑scale inference costs remain prohibitive. This people‑first approach not only smooths adoption but also unlocks the full potential of AI across banking, insurance, life sciences and the public sector.

SAS’s deterministic analytics platform differentiates it from the wave of flashy LLM demos flooding the market. In regulated domains—clinical trials, fraud detection, underwriting—outputs must be reproducible, auditable and defensible before regulators or courts. SAS’s Viya engine delivers exactly that, orchestrating agentic workflows while preserving deterministic results. This focus on precision and compliance resonates with customers handling billions of transactions daily, where a 30‑millisecond decision window leaves no room for probabilistic language models. The company’s claim of generating 11‑15 million lines of AI‑produced code each month underscores its commitment to scaling rigorous, validated AI pipelines.

Looking ahead, SAS is positioning itself at the intersection of quantum computing and AI. By abstracting quantum processing units behind familiar SAS or Python interfaces, the firm aims to slash model‑training times from weeks to hours once quantum hardware matures. Coupled with its rapid code‑generation engine—projected to exceed 100 million lines by the end of 2026—SAS is building an ecosystem that can quickly iterate, test, and validate AI solutions. The strategic expansion of its Pune R&D centre, growing 20‑30% annually, provides a global talent pool to accelerate these innovations, ensuring SAS remains a trusted partner for enterprises that cannot afford to compromise on speed, accuracy, or consistency.