ChatGPT is Hot—What’s Hotter is Pragmatic AI

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When your (non-technical) mother asks you what ChatGPT is, as mine did last week, you know that data science and analytics have transcended science fiction’s scope and are now part of widespread popular culture. Although Large Language Learning Models (LLMs) like ChatGPT and other forms of generative artificial intelligence are having their disruptive moment in the spotlight, various Gartner Hype Cycles point to other analytics and data science trends that bear watching. These include MxOps (ModelOps, MLOps), Prescriptive Analytics, Decision Intelligence, and more. 

Underlying these trends are perennial enterprise challenges—that the ongoing explosion of data volumes and variety yield more value for action and insights, especially in improving efficiency and resilience across the organization. What’s needed now are clear paths to apply cutting-edge trends and technologies in data science and analytics to real-world use cases.

3 Ways You Can Adopt Pragmatic Approaches to AI

There are use cases for pragmatic AI that span industries. Here are three use cases that can help you get value from AI-infused insights now.

1. Operational Efficiency for Resilience and Agility

Operational efficiency is not just for manufacturing or logistics. Agriculture benefits from the application of artificial intelligence as well. According to Michelle Lacy, biotechnology data engineering lead at Bayer Crop Sciences, “‘Traditionally, a farmer would plant a field, spray the entire field with fertilizer and pesticide, and use full-coverage irrigation. But fields are not uniform, and plants need different conditions depending on where they are in the field.…Using image analytics, you can look at your field as a whole and then overlay the different data layers: soil, irrigation, and fertilization—and say, ‘All right. I understand my field now.’”

Bayer uses drones to take high-definition pictures to monitor crops. Lacy continues, “And are you going to have someone look at each of these images? That just kills the ROI of that entire effort. With AI and algorithms, we can bring in these images that are huge and generate data that would be very difficult for a human to do by themselves. That is pretty powerful, and it’s the foundation of our image analytics platform.”

2. Customer Intimacy for Competitive Advantage

By using AI-infused processes to predict what your customers might find of most interest, you can increase offer acceptance and customer satisfaction. Bank of Montreal leveraged TIBCO to meet those use cases. “Instead of offers that are months old, they are now recent and could be based on interactions that occurred just minutes before—so they are very relevant for that customer,” says Gayle Ramsay, vice president of customer analytics. “We’ve seen positive acceptance triple from what it was four years ago. We’ve moved from a one-day response time to near real time.”

3. Dynamic Pricing for Revenue Enhancement 

AI can help provide the most optimal pricing for your products and services based on a wide range of dynamic inputs. TIBCO customer TUI Group has used this for both competitive advantage and to improve customer satisfaction. According to Heinz Kreuzer, CIO of TUI Central Region and CEO of TUI Infotec, “We use TIBCO Spotfire to analyze competitor pricing, and we can immediately change ours so we’re very competitive without giving up too much margin. As soon as we have the results, we can’t wait, we have to adjust prices immediately…. Customer feedback is definitely a very important measure for us, and we are quite proud that the feedback is really positive. Our net promoter score started at 41 and, in a very short time, went up.”

In short, pragmatic approaches to AI are proven and provide value NOW. You don’t need to chase hot AI trends to advance your business and exceed your goals.

Gain Insights with TIBCO

Data analytics practitioners and data scientists must learn from their best-in-class peers to spark fresh approaches where analytics insight and action can yield the highest value. They need to see and adopt real-world, pragmatic approaches to AI.

Every data analytics and data science practitioner knows that while hot trends and topics like ChatGPT are capturing the public’s attention, those trends are only part of the story of AI-infused value. What’s hotter is real value via innovative technologies like AI yielding data-driven insights and actions to help enterprises achieve and surpass their goals.