Achieving Individual — and Organizational — Value With AI

Introduction

At Land O’Lakes, a member-owned cooperative agribusiness, farmers are using data and artificial intelligence to make smarter decisions. Over the past 30 years, corn farmers have used advances in bioengineering, chemicals, and analytics to boost their average yields by 50%, from 120 to 180 bushels per acre. Those advances pale in contrast to future corn yields that will be made possible using data and AI: Demonstrations promise to triple that average — to 540 bushels per acre — by the end of this decade. Farmers don’t have to wait that long to see some of those benefits, however. Through extensive experimentation and complex algorithms, Land O’Lakes is already providing AI-driven recommendations to help individual farmers become more productive.

But AI systems aren’t telling farmers exactly what to do: Every piece of land differs; every local market differs. Instead, the cooperative offers detailed recommendations to improve the decisions individual farmers make. These include:

Placing the right seed in the right soil at the right density while incorporating long-term weather forecasts.
Applying nitrogen at the appropriate rate and at the most optimal time frame, saving costs and increasing plant uptake.
Implementing the right farming practices (e.g., tilling, cover crops) to increase soil health, sequester carbon, and drive equal or higher yields compared to prior outputs.
Using computer vision to identify weeds and diseases and to apply the right amount of crop protection in the required spots.
The research and analysis for this report was conducted under the direction of the authors as part of an MIT Sloan Management Review research initiative in collaboration with and sponsored by Boston Consulting Group.

Teddy Bekele, CTO of Land O’Lakes, says that farmers are “making decisions rather than following a recipe.” That is, they are no longer adhering to age-old family traditions about how to cultivate the land, nor are they blindly following AI recommendations. Farmers are forging their own paths — better informed, but still independent.

At the same time, many farmers in the co-op are building stronger connections with other farmers, agronomists, and nutritionists as they learn from one another. When Land O’Lakes runs large grower events that showcase new technologies, participating farmers “want to spend half of their time talking to other farmers to understand what they’re doing,” Bekele says. “There’s a big interaction among these farmers. Working with one another is a big part of what the cooperative is all about.”

Greater competency, increased autonomy, stronger relationships — these are all hallmarks of self-determination, which is a foundation of human motivation and people’s innate growth tendencies.1 The social psychology concept of self-determination holds that people have three basic psychological needs: the need to feel competent, the need to feel autonomous, and the need to feel related to others or things beyond themselves. Our research, based on a global survey of 1,741 managers and interviews with 17 executives, finds that individuals derive personal value — what we refer to as individual value — from AI when using the technology improves their self-determination, which encompasses their competency, autonomy, and relatedness.

Across industries, we find employees using AI and then feeling more competent in their roles, more autonomous in their actions, and more connected to their work, colleagues, partners, and customers.

The Land O’Lakes story illustrates a broader phenomenon. Across industries, we find employees using AI and then feeling more competent in their roles, more autonomous in their actions, and more connected to their work, colleagues, partners, and customers. Understanding this phenomenon, however, depends on understanding how individuals use AI — which we discovered is incredibly difficult to specify. Many people use the technology without realizing it. For example, users aren’t always aware that products have AI embedded in them. When individuals use those products to generate benefits for their companies, do they personally get value from AI? Does it matter if they aren’t aware that the products incorporate AI?

Our research also suggests that a popular belief — that organizations achieve value with AI at the expense of individuals — does not represent the lived experience of most employees. Organizations with employees who personally derive value from AI are 5.9 times as likely to get significant financial benefits from AI compared with organizations where employees do not get value from AI. Only 8% of our global survey respondents are less satisfied with their jobs because of AI. Therefore, understanding AI’s contributions to individual and organizational value demands greater awareness of AI use, its variety, and its effects on both individual and organizational value creation.

This report offers three key insights:

Individual value from AI is critical for organizations to obtain value from AI.
Individuals benefit from AI when using the technology enhances their competence, autonomy, and/or relationships.
Managers can encourage AI use and catalyze value creation at the individual level by cultivating trust, understanding, agency, and awareness.
What Does “Using AI” Really Mean?

Managers often say “using AI” as if they were referring to using a tool, like a stapler. But while people know when they are using a stapler, that is not always the case with AI. Consider a general business product like Salesforce’s customer relationship management software, Einstein. It incorporates AI, using machine learning (ML), natural language processing, and computer vision to, for example, predict customer behavior, understand customer sentiment, and automate client services. However, end users might not be aware, or care, that AI is behind the product’s performance. What’s more, individuals often consider AI solutions akin to coworkers, which is rarely the case with a stapler. In addition, when using specialized AI solutions that govern (or are a part of) a business process, users may need to know how an algorithm arrived at its recommendation; in other instances, users need to train the algorithm in order to use it. Understanding the inner workings of a stapler is not necessarily needed in order to use it. Ultimately, the concept of “using AI” covers a broad range of applications in which AI may be a more or less prominent component. It’s not just that using AI is different from using other tools; using AI itself is not a monolithic activity with a consistent meaning in different contexts. Understanding how organizations create value with AI requires a more foundational understanding of how individual workers use AI and how AI use contributes to personal value.