Following nearly two years of generative AI (gen AI) mania, businesses are putting the honeymoon phase1 behind them and focusing on the work that really counts: generating value from this alluring technology. There are great hopes.
According to a recent McKinsey Global Survey, twice as many businesses as the previous year—65% of businesses of all sizes, locations, and sectors—regularly use gen AI.
The perception that early gains made by top performers are a sign of future cost savings and profits has led to an increase in investment in general artificial intelligence (AI). However, the majority of businesses have not yet noticed a major impact from gen AI.
Most organizations’ data executives have developed generation AI strategies in order to stay up with the rapid pace of innovation. While not all businesses have advanced past the pilot phase, the majority have taken steps to incorporate AI into their tech stacks.
However, creating long-term value from gen AI requires more than just a technical integration model. To make sure that their technological investments produce quantifiable business outcomes, businesses must also develop next-generation AI operating models.
An operating model is a familiar structure in most large organizations. A company’s operating model is a plan that outlines how people, processes, and technology will be deployed to provide value to customers and stakeholders.
It can encompass financial structures, partnerships, and product road maps to meet the company’s long-term goals. When applied specifically to gen AI, an operating model includes every decision—from staffing and organizational structures to technology development and compliance—that guides how gen AI is used and measured throughout a company.
A well-defined gen AI operating model can help leaders successfully and securely scale gen AI across their organizations. Data is the backbone of a successful gen AI deployment, so chief data officers (CDOs) often lead the charge to create these models—bringing technology, people, and processes together to transform gen AI’s potential into real impact. Yet when creating gen AI operating models, data leaders commonly fall into two traps:
- Tech for tech: This approach involves allocating significant resources toward gen AI without a clear business purpose, leading to solutions disconnected from real-world impact. This can result in overspending on gen AI tools that are rarely used in daily workflows and create little business value.
- Trial and error: This approach entails experimenting with disparate gen AI projects, but not doing so in a coordinated manner. This presents a particular risk in sectors such as technology, retail, and banking, where gen AI has the potential to quickly increase productivity. Companies in industries where gen AI may take longer to have a significant effect on productivity, such as agriculture and manufacturing, could potentially afford to wait to deploy the technology.
