Enterprise AI Priorities for 2026: Survey Results From Dan Herbatschek Point to Control, Cost, and Governance
Over a three-month period, Ramsey Theory Group CEO Dan Herbatschek and his team surveyed more than 100 enterprise leaders across healthcare, field service, logistics, and automotive. The signal is clear: enterprises are moving from experimentation to operational AI-governed, measurable, and accountable.
"AI has officially crossed the threshold from innovation initiative to core business infrastructure," said Dan Herbatschek. "In 2026, success won't be measured by how much AI an organization deploys, but by how well it controls, governs, and monetizes it."
Trend 1: Agentic AI at Scale-With Guardrails
Enterprises plan to embed agentic AI-systems that can execute multi-step tasks and workflows-directly into operations. The priority: controlled autonomy. That means business rules, approval flows, audit trails, and human-in-the-loop checkpoints by design.
"The future of agentic AI is not unchecked independence," said Dan Herbatschek. "Enterprises want AI systems that can act decisively and responsibly, with full traceability and human oversight."
- Define policy-based constraints for each agent: data access, decision rights, escalation paths, and rollback rules.
- Require verifiable logs for every action (inputs, outputs, prompts, tool calls, and approvals).
- Track operational KPIs: SLA adherence, exception rate, cycle time reduction, and internal control coverage.
- Start with contained use cases (e.g., service-ticket triage, claims prep, supplier onboarding) before touching customer-facing flows.
Trend 2: AI Cost Management and ROI Move to the Boardroom
As deployments grow, the true cost of AI is coming into focus: infrastructure, model training and inference, data pipelines, security, compliance, and ongoing governance. Spending will be treated as a long-term operational investment with strict scrutiny from executives and boards.
"AI costs don't end at deployment-they compound over time," said Dan Herbatschek. "Organizations that fail to measure AI ROI continuously will struggle to justify expansion."
- Stand up an AI FinOps practice: cost taxonomy, unit economics per workflow, and chargeback/showback to business units.
- Instrument every workload: track tokens, model mix, caching rates, latency, and failure modes.
- Adopt a model portfolio strategy: right-size models, use routing and distillation, and implement clear deprecation rules.
- Tie ROI to business metrics: revenue impact, cost-to-serve, risk reduction, and customer experience outcomes.
Trend 3: AI Governance Moves From Policy to Production
Principles are table stakes. In 2026, governance will be built into development, deployment, and monitoring pipelines-providing real-time proof of compliance, security, and alignment with business intent.
"Governance can no longer live in static documents," said Dan Herbatschek. "Regulators, customers, and boards will demand real-time proof that AI systems are compliant, secure, and aligned with business intent."
- Embed controls in CI/CD: dataset lineage, PII scanning, model cards, privacy and security checks, and red-team gates.
- Monitor continuously: drift, bias, toxicity, jailbreak attempts, and policy violations with automated alerts and auto-remediation options.
- Maintain immutable audit logs mapped to internal controls and regulatory requirements.
- Align with established frameworks such as the NIST AI Risk Management Framework and evolving regulations like the EU AI Act.
What This Means for Executives
- Appoint a single accountable owner for AI operations with clear decision rights and budget authority.
- Publish risk thresholds and approval policies for agent actions; require human oversight where impact or uncertainty is high.
- Build a portfolio view: all AI use cases, models, vendors, costs, SLAs, and risk ratings in one place.
- Partner with Finance on an AI cost dashboard tied to business outcomes; review monthly at the exec level.
- Select your instrumentation stack now-observability, security, governance-and standardize across teams.
- Launch 2-3 agentic pilots in back-office workflows with full guardrails and clear success metrics.
- Establish a quarterly governance review with board-ready reporting and audit artifacts.
A Turning Point for Enterprise AI
Together, these trends point to a more mature market-less hype, more accountability, and outcomes that stand up to scrutiny. "The leaders in 2026 will be the organizations that understand exactly who controls their AI, what it costs, and how it creates value," said Dan Herbatschek.
For more information, visit Ramsey Theory Group.
About Dan Herbatschek
Dan Herbatschek is an applied mathematician with a deep interest in the history and philosophy of science. He graduated Summa Cum Laude and Phi Beta Kappa from Columbia University, where his award-winning thesis explored mathematics, artificial languages, and the changing idea of time in the Scientific Revolution.
As Founder and CEO of Ramsey Theory Group, he connects business strategy to software execution across Python, JavaScript, data visualization, machine learning, and scalable data systems. His background includes roles as an Investment Consultant and Data Management Consultant in New York. Outside of work, he writes the "Open Mind" blog and enjoys boxing and time with his family.
About Ramsey Theory Group
Ramsey Theory Group is a diversified technology and digital services company operating across healthcare, field service, entertainment marketing, logistics, and automotive. The company applies expertise in AI, software engineering, IT, and cybersecurity to help organizations operationalize AI at scale. Its portfolio includes Erdos Technologies, Erdos Digital, Erdos Tracks, Erdos Logistics, Erdos Medical, and Eunifi.
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