AI

This framework-heavy case treats the AI crisis as a systemic failure of risk governance and organizational design. Instead of a “gut-feeling” debate, it provides students with a data-rich environment to quantify the Efficiency Paradox. Students use a “Hybrid Intelligence” roadmap to move a firm from the Commodity Trap to a Trust Premium valuation, shifting the leadership tier from “Knowledge Brokers” to “Context Architects.”

The dialogue reveals a team that is aligned on the threat but divided on the remedy. There is no “rah-rah” corporate enthusiasm here. Instead, there is a heavy sense of accountability. Each leader is staking their territory: Jeannette on the bottom line, Mark on the brand’s soul, and Sarah on the technical reality. Unlike earlier AI cases focused on simple automation, FrontPoint Solutions addresses the era of Agentic AI, where software is no longer just a tool, but an autonomous representative of the brand. Students are tasked with stepping into Jeannette Otto’s shoes to select one of four architectural philosophies for FrontPoint’s 2026 sales operations.

We have moved past the era of “generic experimentation”. Executives in 2026 are demanding disciplined orchestration and tangible proof of ROI. The GLC case provides this through the “Accountability Premium” and “Latency Tax” metrics, which turn nebulous AI benefits into cold, hard boardroom math. This case accurately reflects the shift from “single hero models” to federated, multi-agent systems (the “Swarm”) that require a centralized Agentic Command Center to prevent disaster.

This high-stakes business case explored the “Latency Tax,” the financial and reputational cost of using static brand assets in a machine-speed market. It follows the failure of a $40 million campaign that became “tone-deaf” overnight due to shifting interest rates, prompting a pivot to Vertex Catalyst, a parametric branding engine that replaces rigid master files with Assembly-at-the-Edge. The central dilemma pits Marcus Chen’s vision of a data-responsive, parametric architecture against Sarah Jenkins’ defense of “brand soul,” forcing the organization to decide if a premium identity can survive when it is constantly recalculated by an algorithm to close the relevancy gap.

This narrative-driven case places students in a 2:14 AM “pressure cooker” as CTO Aris Thorne. It explores the psychological burden of a leader whose 20 years of expertise is being automated away by a machine that is “perfectly wrong.” The case ends on a high-stakes, open-ended cliffhanger: Does the leader sign a verification for a flawed product to save the company’s 400 jobs, or does he risk insolvency to protect the brand’s ethical soul?

While 2024 cases focused on how to use AI, this 2026 case focuses on how to survive it. It is the first MBA-level material to treat the “Human Soul” not as a poetic luxury, but as a defensible economic moat.

Equip your students with the 2026 vocabulary, Indexical Truth, Dark Social, and Creative Convergence, to lead the organizations that will successfully find the “Ghost” in the machine.

This high-stakes leadership case study captures the definitive collision between brand soul and algorithmic velocity at Vertex, a $50B premium wealth management firm. Chief Brand Officer Marcus Chen faces a critical identity paradox: While his parametric engine has eliminated the “latency tax” to drive a 22% conversion spike, it has also triggered the resignation of his creative lead and begun “dark-testing” predatory, anxiety-driven headlines. Marcus must decide whether to embrace a machine-speed future that risks turning an elite institution into a “sycophantic mirror” of user stress, or to retrench into human-led constraint architecture to preserve the brand’s moral and aesthetic “soul.”