AI Fluency Jumpstart for Technology Leaders
Technology leaders are increasingly responsible for AI decisions that once had no clear owner: selecting the right application pattern, model, operating cost, governance approach, and determining whether an initiative will deliver meaningful return. These decisions require both strong technical judgment and AI-specific expertise. This program provides that expertise.
While technical, it is not a coding program. It teaches you to evaluate AI the same way you assess architecture, cost, and risk across your technology portfolio: matching solutions to problems, deciding when to build, buy, or partner, managing token and inference costs, embedding governance into systems, and translating business goals into technical designs with measurable outcomes. Equally valuable is the opportunity to learn alongside accomplished technology executives from diverse industries.
Through shared discussions and real-world problem-solving, you'll test your thinking against peers facing similar challenges in different environments. You leave better equipped to guide engineering teams, challenge vendors, and make the decisions that determine whether AI initiatives succeed or stall.
Program Details
Program Dates
October 8, 2026
Registration Deadline
September 24, 2026
To request additional program information, please fill out our interest form.
Cost
$700 if you register by August 27, 2026
$950 if you register between August 28-September 24, 2026
Delivery Method
One full day, on-campus
Prospective Participant Profile
This program is designed for technology leaders who are responsible for delivering AI, not personally building it, and who need enough technical depth to lead the people who do.
Typical participants include:
- Engineering managers, directors, and VPs of Engineering
- Software development and application delivery leaders
- Heads of data, platform, ML, or AI teams
- Technical program and delivery leaders with architecture responsibility
- CTOs, VPs of Technology, and technical founders at mid-market and enterprise organizations
The common thread: participants have real technical background and team ownership, and they are being asked to make AI decisions at the architecture, cost, and governance level rather than the code level.
Program Components
High-Level Program Topics
- AI application patterns and when each one fits: retrieval-augmented generation, single-agent and multi-agent orchestration, fine-tuning, and prompt-based approaches
- Model strategy and economics: build versus buy versus partner, foundation model selection, token and inference cost management, and the unit economics of an AI feature
- Governance and guardrails as engineering, not paperwork: technical enforcement of safety, security, and compliance
- Building AI that holds up at scale: evaluation, reliability, and quality
- Translating business vision into technical design, and technical results into business ROI
- Leading the team: setting standards, upskilling engineers, and making build decisions at the right altitude
Participants will learn to:
- Match an AI application pattern to a specific business problem, and recognize when a chosen pattern is the wrong one
- Make and defend a build-versus-buy-versus-partner decision for a given use case
- Estimate and control the cost of an AI system, including token consumption and inference optimization
- Design and technically enforce guardrails for safety, security, and regulatory compliance
- Diagnose why an AI system that works in testing becomes unreliable at scale, and what to require of a team to prevent it
- Translate an executive ambition into a technical roadmap, and translate technical delivery into an ROI story leadership will fund
At the conclusion of the program, you will be able to select the right architecture for an AI use case, make the model and cost decisions that keep it economically viable, enforce governance technically, lead a team through delivery at scale, and connect the whole effort to business value your organization can measure.
Organizations benefit when their technology leaders can move AI from experiment to durable capability. Outcomes may include: fewer stalled initiatives, more defensible spend on models and infrastructure, governance that holds up under audit, and AI work that produces measurable return rather than sunk cost.
Program Faculty
Ravi Tenneti, Adjunct Lecturer, Fisher College of Business, The Ohio State University
Business and technology executive with over 20 years of experience leading AI, digital, and business transformation initiatives across Fortune 100 and growth-stage companies. He serves on multiple industry councils and is an Adjunct Faculty member at The Ohio State University Fisher College of Business.