95% of AI pilots fail. What makes the 5% succeed?

General AI gives you broad capability and fast starts—great for experiments and narrow copilots. Proprietary intelligence goes further. It aligns models and agents to your domain, connects them to systems of record, and adds human‑in‑the‑loop oversight so outputs are trusted, explainable, and auditable.

Proprietary intelligence systems are:

• More accurate in your domain because they’re grounded in your data and ontology.
• Faster and lower cost because they’re right‑sized for your workflows.
• Context‑retaining with memory and feedback loops—so value compounds.
• Governed for privacy, IP, and compliance from day one.
• Model‑agnostic so you can swap components as the frontier evolves without rewrites.

This distinction mirrors how top performers execute: they use general tools to explore, then build proprietary systems where advantage compounds.

Systems that actually learn

Most pilots reset context every session, so they never improve. Turing Intelligence designs persistent memory and closed‑loop feedback into agentic workflows. Human corrections become training signals. We monitor business benchmarks—cycle time, backlog reduction, external spend—so improvement shows up where it matters.

We operationalize this with evaluation harnesses that run alongside production: regression tests for accuracy, safety, latency, and cost; drift monitoring across data slices; and routing that escalates edge cases to human review. Every exception is a chance to get smarter.

Building where ROI accrues

Enterprises often over‑invest in demos that don’t move the business. We help teams focus on “boring but valuable” workflows—such as finance, onboarding, claims, and compliance—where MIT finds the ROI is most visible. Each initiative is scoped to a single KPI per workflow and shipped in short cycles that demonstrate value every quarter.

A simple rule guides prioritization: if the work is frequent, rules‑heavy, and document‑centric, agents win early. If it is rare, subjective, or high‑risk, we sequence it later and keep a human primary with AI decision support.

Bridging internal build with frontier expertise

MIT notes that co‑developed systems scale more reliably than isolated internal efforts. Turing bridges your teams with frontier research and production patterns—model evaluation, post‑training alignment, orchestration, observability—so the architecture that ships is the one that scales.

We collaborate as a neutral partner. No proprietary lock‑in, no quota to push one stack. When the state of the art moves, your system can adopt the best component for the job.

No legacy baggage

Traditional vendors protect people‑powered revenue. We don’t run BPO operations or push a proprietary stack. Our incentives are clear: architect what today’s AI can safely do inside your environment—and measure it against your outcomes.

• Starting with a tool, not a workflow. Choose a P&L‑relevant process; then choose the tech.
• Measuring model scores, not business impact. Track cycle time, exception rate, and customer outcomes.
• Ignoring governance until late. Bake in audit trails, approvals, and data controls on day one.
• One‑off pilots with no path to scale. Design for integration, monitoring, and change management up front.
• Underfunding human intelligence. Use skilled reviewers and operators to train, supervise, and continuously improve the system.

Across industries, outcome‑led builds show a repeatable pattern: when systems learn, align to KPIs, and run in the business, results compound.

• Finance transformation: 30% faster client onboarding, 20% lower operational cost, 25% drop in complaints after modernizing finance operations with agentic document intelligence and workflow orchestration.
• Franchise growth: Admissions up 15% and onboarding 30% faster across 500+ locations after rebuilding customer‑facing flows on an event‑driven architecture with AI in the loop.
• B2B lending modernization: 45% faster loan processing, 20% growth in applications, and 30% lower cloud cost after moving from a monolith to modular architecture with AI‑assisted underwriting.
• Retail e‑commerce: 80% faster site performance, 200+ hours saved per pricing cycle, and 50% CSAT improvement with AI‑assisted merchandising and experimentation at scale.
• Biotech e‑commerce: Conversion up 50%, time‑to‑feature down 40%, and infrastructure cost down 20% after re‑platforming with an AI‑powered catalog and customer intelligence.

These outcomes aren’t one‑offs. They’re what happens when you replace tool‑thinking with proprietary intelligence that learns and scales.

1. Pick the workflow, not the model. Start where cycle time, risk exposure, or external spend is visible in the P&L.
2. Scope to one KPI. Make success legible—e.g., “reduce document cycle time by 30% in 60 days.”
3. Embed memory and feedback. Design human‑in‑the‑loop checkpoints so the system improves with use.
4. Prove in weeks, scale in quarters. Ship a thin vertical slice that runs in production, then expand by adjacency.
5. Measure business impact continuously. Track adoption, throughput, and exception rates—not just model metrics.

If you’re ready to move beyond pilots, let’s identify the two workflows that will change your P&L fastest—and build the systems to run them.

[Talk with a Turing Strategist →]