CIOs wish for simpler ways to wrangle data and experiment with business models – but change remains hard to scale. Also, it may be time to stop chasing “alignment.”
How we’re using AI: Bayer Crop Science CIO
AI is changing the rules of R&D, supply chain, and customer relationship management in our organization. Here’s a peek
At Bayer, AI is not some blurry point on the distant horizon. It’s already part of our core business operations, from how we conduct research and development to how we build stronger relationships with our customers.
Based on our effort and experience, I believe that AI is an enabler, not a replacer. I’ll share more about why I think this distinction is so important in a moment. First, let me share what this looks like today in three key aspects of our organization, and how, by embracing AI as an enabler and accelerator, we’re already achieving powerful results that are tightly aligned with our business strategy.
AI in R&D
Research is obviously of critical importance to Bayer, and AI is already helping us make better decisions that accelerate innovation through our Crop Science R&D pipeline.
Here’s a straightforward example: We used to plant every single seed in the ground to test its performance and determine, say, which conditions favor a particular type of seed. This was a necessary but resource-intensive process. We can now simulate a lot of that testing with AI and make smarter, faster decisions as a result.
That means we’re able to predict what genetics, or what seed or germplasm, will perform better than others on a computer instead of in the field. We might do a billion simulations before we put a single seed in the ground. This enables us to shave one to two years off our research cycle time. It gives us more accurate information, which in turn improves our decision-making. It uses fewer resources, so it’s a more sustainable approach. And by accelerating our time to market and simultaneously consuming fewer resources, we’re also reducing our costs.
I should note a key success factor here: We collect a lot of data throughout our pipeline, such as genetic information and performance data. This data is the fuel that helps us accelerate throughout our pipeline, and it’s something I’d advise my fellow IT leaders pay careful attention to in your own AI use cases: Make sure you have the data necessary to drive better business decisions and outcomes.
[ Is your data house in order? Read the related article: Getting started with AI in 2019: Prioritize data management. ]
AI in supply chain
Our global supply chain is another key area where we’re using AI today. We’re increasingly embedding machine learning into our shipping and logistics functions, for instance. We don’t do this to chase a trend; rather, it’s tightly woven with our business goals. As a result, we’re achieving bottom-line results while also reducing our footprint: We’re on track to deliver annual savings and cost avoidance of $14 million annually, while reducing 300,000 miles and 350 metric tons of C02 from how we deliver our products around the world.
Machine learning also is improving how we forecast demand and make better business decisions. We literally grow all our products, so accurate forecasting is vital to our success; we can’t just spin up a 24-7 factory if we underestimate demand.
Similarly, we can’t afford to waste products – not only from a financial standpoint, but also from a sustainability perspective. Machine learning is helping us boost yields and reduce obsolescence or waste; every percentage point that we can reduce obsolescence is worth millions of dollars.
[ Read also: AI vs. machine learning: What’s the difference? ]
AI in customer relationship management
In our commercial business, we’re always striving to better understand our customers and their needs so that we build strong, lasting relationships. AI and predictive analytics now enhance how we do that.
We can better understand our customers through their interactions with us so that we can better target how we communicate with them in the right way and at the right time. This is crucial for our customer loyalty and retention. We can better predict when a customer might leave us for a competitor and immediately work with that customer to try and retain them.
It costs on average $100,000 for us to reacquire a customer if they leave. If we can prevent a customer from leaving in the first place, we not only avoid that cost but build long-term loyalty in the process. Retention is important to all CRM efforts, regardless of your business or industry.