By The conversation around the integration of Artificial Intelligence (AI) in evaluation is rapidly gaining momentum. I find myself intrigued to explore how it can add value to each step of Outcome Harvesting, but my focus in this blog is specifically on how it enhances the harvesting of outcomes and refines the analysis and interpretation process.
AI is invaluable for document reviews in OH as it can swiftly sift through vast amounts of textual data, identifying potential outcomes much more quickly than manual methods. It’s like having a super-efficient assistant who never gets tired! Moreover, AI’s capability to summarize these documents ensures that external evaluators don’t overlook any contextual information crucial for the evaluation. AI can also analyze speech in interviews which can help harvesters in extracting outcomes from verbal communication.
However, what really excites me is AI’s potential in assisting sources to formulate well-crafted outcome statements. The iterative ping-pong process, essential in Outcome Harvesting for deriving smart, specific, and measurable outcomes, can also be time-consuming. AI can step in here, guiding sources through an interview process, essentially taking over the initial task of harvesters. Harvesters can then concentrate on fine-tuning the final outcome statements, checking for any nuances or errors AI might have missed. Alternatively, it can make the process of reviewing outcome statements for harvesters more efficient.
Furthermore, AI’s assistance extends to categorizing outcomes and playing a pivotal role during interpretation. By employing advanced data analysis techniques, AI can uncover deeper trends and patterns between harvested outcomes, possibly identifying insights that might be missed by human analysts. This can lead to a richer, more nuanced and unbiased understanding of the change that was achieved.
In terms of tools, while general AI applications can handle queries related to Outcome Harvesting, custom GPTs are notably more effective because they are programmed with an intrinsic understanding of the OH process. Driven by this potential, I developed two AI bots for Outcome Harvesting. ‘Harvest Helper’ assists in formulating outcome statements through targeted questions and the ‘Harvest Analyst’ aids in categorizing outcomes and detecting trends and patterns in the outcome dataset. These tools are available for everyone to use.
While AI offers considerable potential, it’s essential to understand that it functions as a supportive tool in Outcome Harvesting, enhancing our efficiency and refining the process, rather than serving as a substitute for our expertise as Outcome Harvesters. AI helps to save time and resources and it complements and augments our human expertise, but does not replace it. An expert in OH remains indispensable, particularly in verifying whether the outcomes identified by AI truly align with the Outcome Harvesting standards and in crafting the content for a comprehensive and high-quality Outcome Harvesting evaluation report.
Despite this caution, I’m genuinely enthusiastic about embracing AI as a partner in our OH journey. It promises to make our work faster, more accurate, and insightful.
I’m eager to hear your thoughts on this. How have you used AI for Outcome Harvesting? What potentials and pitfalls have you encountered?