Deterministic vs Probabilistic AI: What Business Leaders Need to Know
KG Charles-Harris breaks down why deterministic AI — not probabilistic — is the foundation for mission-critical business decisions, and how Quarrio delivers the speed, accuracy, and auditability leaders need.
Deterministic AI isn’t just another buzzword in KG Charles-Harris’s world - it’s the foundation for how modern organizations should make decisions when real money, customers, and careers are on the line.
Why Quarrio Exists: Speed to Truth
KG starts from a simple but painful CEO confession: “I cannot get the information I need, when and how I need it,” even when surrounded by some of the smartest people in the building. Traditional reporting means waiting on harried IT and analyst teams to stitch together ad-hoc reports from hundreds of systems. In the average mid-size American enterprise, that cycle takes about 2.7 weeks for a single ad-hoc question; Quarrio’s deterministic AI platform is built so that the same answer arrives in about 2 seconds. Instead of learning query languages or BI tools, users ask questions in plain English, get auto-visualized answers, and can monitor processes and KPIs globally with threshold-based alerts.
Deterministic vs Probabilistic AI: Knowing When Accuracy is Non‑Negotiable
A core theme of the episode is the distinction between deterministic and probabilistic AI. Deterministic AI, as KG defines it, guarantees three things: accuracy, consistency, and auditability. The same question over the same data always yields the same answer, and every step is traceable. That’s essential in mission-critical domains - think autopilot systems in airplanes, power grids, healthcare, and supply chains - where “hallucinations” and inconsistent outputs are unacceptable. Probabilistic AI is powerful for creative and open-ended tasks, but KG argues that when decisions involve real stakes, you must “make decisions on factual data” and use AI that behaves more like an autopilot than a brainstorming partner. The real play, he suggests, is deterministic and probabilistic systems working in parallel: one to act safely and reliably, the other to explore, suggest, or generate.
Cycle Time as the True Competitive Moat
KG and Jeff frame organizations as “machines for making decisions and taking actions that have certain types of results.” In that machine, everything depends on cycle time: information → decision → action → results. Shortening cycle time to information shrinks each subsequent step. KG shares an example of a $60M margin leak that took two quarters to uncover in a traditional environment - something Quarrio could surface in seconds. If you imagine Goldman Sachs getting answers in 2 seconds while a competitor like Morgan Stanley waits weeks, the compounding advantage in market position, execution, and results becomes obvious. This is why Quarrio focuses on the structured systems where enterprises invest most - CRM, supply chain, finance platforms like Salesforce, SAP, Workday, ServiceNow, Oracle.
A Multi‑Agent Platform for Real‑Time Organizations
Under the hood, Quarrio is described as a three-agent platform. The “Ask” agent lets users query data in natural language and see visualizations instantly. The “Watchdog” agent continuously monitors business processes and KPIs, using configurable green/yellow/red thresholds to flag tactical versus strategic issues in real time. The “Workhorse” agent can then automate actions: for example, if margins drop below a defined threshold, it can automatically tighten sales discounts and notify the right teams in seconds instead of weeks.
All of this runs across an environment where a typical mid-size enterprise may have 300 structured data sources; Quarrio’s IP focuses on keeping natural language accuracy high and solving the “data heterogeneity” problem while preserving existing security and permissions so users only see what they’re allowed to.
Leading with Stewardship in an AI World
Beyond the tech, the episode digs into what it means to lead and build in this space. KG sees every organization as a decision-and-action machine, and insists that decision-making should be democratized: when you give accurate, trustworthy data to everyone - from receptionist to VP - you enable an organization that functions in near real time. He draws a sharp line between efficiency and effectiveness: efficiency optimizes for time and cost, but effectiveness is about actually achieving the goal, even if that means redundancy and extra resources to truly “enter and dominate a market.”
On the human side, KG’s leadership philosophy centers on people and stewardship: he emphasizes surrounding yourself with people of high quality, motivation, and experience, and stresses that honesty, dedication, and mutual loyalty matter more than anything, “If you don't have their back, you cannot expect them to have yours.” This same philosophy applies to Quarrio - for KG, stewardship in the context of data means giving people accurate, trusted access to the information they need so every level of the organization can make sound decisions and take effective action in near real time.
His message to founders and leaders is to ignore the hype, match the right kind of AI to the right problem, surround yourself with people who elevate your thinking and execution, and treat leadership as stewardship of your team, customers, partners, and family.
🎧 Watch Episode 359 – “Deterministic vs Probabilistic AI: What Business Leaders Need to Know” with KG Charles-Harris at https://saasfuel.com or on YouTube: https://youtu.be/_s2DlGTeiWY?si=JtXyu8jnU2znag9W