Artificial Intelligence Failing to Generate Profit Returns - Three Proven Solutions
In the ever-evolving landscape of technology, the integration of Artificial Intelligence (AI) has become a cornerstone for many businesses. A major study by the National Bureau of Economic Research has shown a significant productivity improvement of 34% for new customer service workers when assisted by AI agents. This is just one example of the potential benefits that AI can bring to the table.
Three practical adoption paths for AI in the enterprise have emerged: Horizontal AI, Vertical AI, and Agentic AI.
Horizontal AI, which involves the broad distribution of general-purpose AI tools such as Microsoft 365 Copilot, ChatGPT Enterprise, or Google Gemini integrations in Workspace, can offer a wide range of benefits but may come with fuzzy returns on investment (ROI) and potential risks if guardrails aren't in place.
On the other hand, Vertical AI offers tighter control and more precise metrics. This approach focuses on targeted transformation by embedding AI where the business already knows how to measure impact, such as AI-driven financial forecasting, automated triage in customer support, or marketing content generation powered by internal brand guidelines and Large Language Models (LLMs).
The path with the highest ROI and the hardest lift is building AI into products, processes, and organizational design, often referred to as being AI-native. This path demands change at every level, including developer tooling, release velocity, security review, data governance, and product definition.
One example of a company that has successfully embraced this approach is Anysphere, the startup behind the AI-native development environment Cursor. Founded by Eric Steinberger and Sebastian De Ro, Anysphere has achieved over $500 million in annual recurring revenue (ARR) and a $9.9 billion valuation. Notably, Cursor did not wait for a steering committee to greenlight its transformation; it built it from day one.
However, for larger companies looking to catch up, the question is how to adopt AI in a way that leads to fundamental transformation, not just another pilot project. The disconnect between successful AI adoption and the struggles of Fortune 500 companies lies in the fact that the successful companies are not just "adopting" AI, but building with it.
Leaders such as chief AI officers, CISOs, and product heads are needed to challenge the status quo and drive the integration of AI. These leaders must focus on adoption and accountability, pairing it with clear usage policies, training, automated oversight, and tracking not just usage, but whether the tools are displacing time spent on repetitive tasks.
In conclusion, the path to AI adoption in the enterprise is not a one-size-fits-all solution. Understanding the differences between Horizontal, Vertical, and Agentic AI is crucial for businesses looking to leverage AI for growth and productivity improvements. With the right leadership and approach, businesses can successfully integrate AI into their workflows, functions, and products, leading to a fundamental transformation.
