Artificial intelligence promises to revolutionize the way we work, offering efficiency gains and new capabilities. Yet, adopting AI is not without its challenges. One prudent approach is to integrate AI into existing workflows in parallel with human processes. This strategy minimizes risk, builds confidence, and allows organizations to understand where AI excels and where it stumbles before fully committing. I have described the problem of AI output validation before; it is a serious impediment to AI integration. Here is how to solve it.
Consider a professor grading student essays. Traditionally, this is a manual task that relies on the educator's expertise. Introducing AI into this process does not mean handing over the red pen entirely. Instead, the professor continues grading as usual but also runs the essays through an AI system. Comparing results highlights discrepancies and agreements, offering insights into the AI's reliability. Over time, the professor may find that the AI is adept at spotting grammatical errors but less so at evaluating nuanced arguments.
In human resources, screening job applications is a time-consuming task. An HR professional might continue their usual screening while also employing an AI tool to assess the same applications. This dual approach ensures that no suitable candidate is overlooked due to an AI's potential bias or error. It also helps the HR team understand how the AI makes decisions, which is crucial for transparency and fairness.
Accountants auditing receipts can apply the same method. They perform their standard checks while an AI system does the same in the background. Any discrepancies can be investigated, and patterns emerge over time about where the AI is most and least effective.
This strategy aligns with the concept of "double-loop learning" from organizational theory, introduced by Chris Argyris. Double-loop learning involves not just correcting errors but examining and adjusting the underlying processes that lead to those errors. By running human and AI processes in parallel, organizations engage in a form of double-loop learning—continually refining both human and AI methods. Note, it is not only about catching and understanding AI errors; the parallel process will also find human errors through the use of AI. The overall error level will decrease.
Yes, running parallel processes takes some extra time and resources. However, this investment is modest compared to the potential costs of errors, compliance issues, or damaged reputation from an AI mishap. People need to trust technology they use, and bulding such trust takes time.
The medical field offers a pertinent analogy. Doctors do not immediately rely on AI diagnoses without validation. They might consult AI as a second opinion, especially in complex cases. This practice enhances diagnostic accuracy while maintaining professional responsibility. Similarly, in business processes, AI can serve as a valuable second set of eyes.
As confidence in the AI system grows, organizations can adjust the role of human workers. Humans might shift from doing the task to verifying AI results, focusing their expertise where it's most needed. This gradual transition helps maintain quality and trust, both internally and with clients or stakeholders.
In short, parallel integration of AI into work processes is a sensible path that balances innovation with caution. It allows organizations to harness the benefits of AI while managing risks effectively. By building confidence through experience and evidence, businesses can make informed decisions about when and how to rely more heavily on AI.
