For as long as artificial intelligence has existed as a concept, there have been fears about its capacity to surpass human capabilities and ultimately supplant humans altogether. Excitement over popular new generative solutions like, Chat-GPT, DALL-E 2 and Lensa’s Magic Avatar feature, for example, is tempered not only with privacy and copyright concerns, but protests from writers, artists, and all types of creators as well.
There’s no doubt the world is changing. The World Economic Forum estimates that AI will replace 85 million jobs by 2025. Focusing on that statistic would make anyone uneasy, but it only tells part of the story. In the same report, experts predict AI to create even more jobs over that same period – 97 million. Despite the misconceptions and alarmist headlines, new technologies should contribute to a surge in jobs by raising demand for new and evolving skill sets.
The enterprises who realize the potential of transformative solutions like computer vision and experience digital transformations will be those who recognize AI’s potential to augment their teams and take a human-centric approach. They’ll include end users as collaborators throughout the solution lifecycle, place an emphasis on trust and transparency, and identify opportunities to boost their team’s potential rather than just automating away manual processes or reducing headcount. Ultimately, they’ll arm their teams with solutions that are optimized to improve their decision making, address their most persistent challenges, and make the most of everything they bring to their jobs.
Across this three-part series, we’ll look at how human-centric approaches to computer vision are empowering workforces and elevating businesses across industries. First up: agriculture.
Seeing the Future of Agriculture with Human-Centric Computer Vision
This November, a study co-published by MIT Sloan Management Review and the Boston Consulting Group explored the relationship between AI’s organizational and individual value. It concluded that the two are inextricably linked and that businesses, across industries, are more likely to see a return on their AI investments when individual employees believe technology makes them more competent and autonomous while providing for stronger relationships with peers and customers. Effectively, they extolled the value of a more human-centric approach to AI.
MIT and BCG’s report opens with an exciting example of computer vision’s capabilities when enterprises take a human-centric approach to implementation and long-term management. The study’s authors point to the ways AI is making revolutionary advancements possible for Land O’Lakes farmers, changing their jobs and overall industry for the better. In decades past, innovations in traditional agricultural engineering have helped improve yields, fight pests, and contend with changing weather conditions. Those advancements, the authors suggest, took a long time and look relatively unimpressive compared to what AI-powered solutions are currently making possible today. The cooperative’s farmers can expect their yields – which have gone up 50 percent over the last 30 years – to triple by the end of this decade.
Among the solutions supporting these farmers are computer vision models to automate livestock counting and ensure consistent weight tracking across the herd. When they can count livestock faster and with greater accuracy, farmers can not only mount a more proactive response but also optimize feed strategies and maximize yield.
These models aren’t replacing farmers. Though they’re offering expert guidance, new solutions aren’t even telling farmers what to do and expecting them to blindly follow. Rather, they’re supplying farmers, agronomists, and other end users with vital data for generating better results while remaining independent and arriving at the strategies that work best for them. Human-centric AI is boosting autonomy in a two-fold way: automating key manual processes and making farmers more autonomous by giving them the ground truth data to confidently make informed decisions.
Human-Centric AgTech: Precision Livestock Counting
Our initiative to introduce a Precision Livestock Counting offers another case study in the immense potential of computer vision for agribusinesses as well as an example of how a human-centric approach to initiatives can help enterprises succeed. JBS, one of the world’s largest food producers, required a solution capable of automating its counting processes and achieving a higher level of accuracy than what was previously possible with manual counts alone.
With a single head of steer valued around $1200, even minute inconsistencies could mean potential hundreds of millions in losses on an annual basis. At this scale and in this context, counting was a complicated process – even for AI. In addition to the sheer number of near-identical animals moving quickly through close quarters, counts were made more challenging by environmental factors like steam, mud, shadows, rain, and fog.
Both changes to the data collection process and a new object detection model helped quickly exceed JBS’ accuracy goals and provide individual employees a greater sense of autonomy. With AI to support counting and tracking, they generate more useful insights, avoid costly errors, and save time to devote to more high-value strategic efforts. JBS is now poised to roll out additional solutions for augmenting their teams’ capabilities, including models for real-time health monitoring.
Empowering and Enabling Workers Across Industries
Despite the apocalyptic conversations about a robot-dominated future, a majority of respondents to MIT and BCG’s survey reported viewing AI as a co-worker as opposed to a threat. Enterprise leaders and managers have a valuable opportunity to benefit from widespread enthusiasm toward AI and put more power in their teams’ hands while seeing the impressive results they’ve expected for years. AI won’t eliminate roles or inspire discontent if it’s introduced with a strong emphasis on trust and awareness at the individual level and if everyone recognizes both its personal and organizational value.
In part 2, we’ll look at how computer vision is giving restaurants and their workforces the power to redefine service excellence.
