The Productivity J-Curve best explains AI’s productivity paradox. The investments across different categories beyond technology initially depress measurable output. Rather than making the necessary investments across these categories, CEOs of legacy corporations often fall into the Delegation Trap, treating AI as a tactical IT upgrade. This misallocation triggers the AI
General-Purpose Technologies, such as AI, must cause the enterprise to rethink and redesign its workflows, organizational structures, and business models. Most information technologies targeting the enterprise, e.g., business intelligence, refine and optimize existing processes rather than forcing their complete redesign. Failing to distinguish between a true General-Purpose Technology and a
Incumbent corporations are investing in and incorporating AI, yet most fail to fundamentally alter their operating models or achieve strong ROI as a result. The root of this failure is not technological. It is macroeconomic and organizational. US enterprises are attempting to execute a paradigm-shifting technological transition within a market
As 2025 drew to a close, the narrative surrounding enterprise AI began to shift. We moved from 2025 being the “Year of Experimentation” to 2026 shaping as the “Year of Deployment.” Under such a mandate, enterprises must develop and deploy their AI applications scalably, efficiently, and economically. For the large
Enterprises have accelerated their efforts to integrate AI into operations. More recently, many have launched pilot projects to develop agent-based systems across various domains. Most of these agents rely heavily on the capabilities of conversation-centric foundation models such as GPT or Gemini. But as we deploy more capable agent-based intelligent
The automotive industry is undergoing a major transformation. This transformation aims to change not only how we move and transport goods, but also how we interact with the vehicles that move us. The Software-Defined Vehicle’s customer experience must cover both the pre- and after-sales portions of the customer journey. The Flagship Experience discusses how the Software-Defined Vehicle, data, and AI can be used seamlessly to create a new end-to-end customer experience that encompasses all mobility-related activities inside and outside the vehicle, delights the traveler, and enables the OEM to monetize the relationship at the appropriate times. Software-Defined Vehicles will center the customer experience around the customer, not the vehicle. Combining AI with these vehicles’ big data generation, configurability, and updatability provides automakers with recurring customer-monetization opportunities. It impacts how automakers design, make, sell, and service vehicles; how transportation and logistics providers maximize their vehicles’ utilization and improve the return on their investment; and how customers (both consumers and businesses) interact with the vehicle, the automaker, and numerous service providers. To succeed in making this shift, organizations will need to undertake important transformations. The Flagship Experience analyzes the approaches already taken and outlines additional ones that will be necessary.
