The ongoing impact of inflation on the economy is a persistent news headline. Organizations around the world are exploring how data and AI can help lower costs and improve efficiency.
Georgia-Pacific, one of the world’s largest manufacturers of pulp and paper products, is ahead of the curve. They are poised to unlock an astounding $100 million in potential value with data and AI.
Steve Bakalar, VP of IT Digital Transformation, has been at the helm of technological innovation at Georgia-Pacific for nearly 30 years. Alongside Roshan Shah, VP of AI & Products, they are driving significant value and change within the business. Their passion for emerging technologies has positioned Georgia-Pacific as a pioneer in adopting cloud and AI solutions.
In a recent conversation, I sat down with Steve and Roshan to share experiences and advice that will help other organizations achieve similar outcomes with data and AI.
Bryan Harris: Georgia-Pacific is a great example of a company that has fully embraced technology like AI and become data-driven across its divisions. I talk with a lot of leaders who are laser-focused on accelerating their AI deployment strategy. As an early adopter, what advice would you give to companies that may be behind the curve?
Steve Bakalar: I can tell you that a big part of success, especially in the early days, is around driving awareness at the highest level of the organization. It’s so important that senior leadership understands what AI can do and how it can augment a workforce. AI has the power to turn trillions of signals a day into real-time insights – insights a leader can use to help forge a business model or insights a floor operator can use to prevent a disruption in operations.
The key here is appreciating the analytical power of a good algorithm. It’s not something a human can match. But when you combine those data-driven insights with the problem-solving abilities of people, you unlock incredible business value.
The most effective way to drive awareness is having the ability to approach leaders with concrete use cases attached to clear KPIs. We’re able to do this because we’ve put in the foundational work to get our data estate in order. Currently, we've got 300 data pipelines and 30,000 models running.
What does that mean? It means our data scientists can quickly look at sample data and get a sense of how that data can be used. This informs the kind of algorithms they can leverage and the type of use cases they can pursue. Getting your data estate in order doesn’t happen overnight. But believe me, this investment pays off in dividends. We’re expecting a nine-figure return from our current investments in AI, generative AI and machine learning.
Bryan: You mentioned driving awareness about AI capabilities. Roshan, how does your approach change as generative AI (GenAI) becomes mainstream?
Roshan Shah: The simple fact is we – along with the global business community – are all learning as we go. There’s no GenAI for Dummies book that outlines all the ways the technology can drive growth and increase operational efficiencies.
The bottom line is this: surround yourself with strong technology partners. More than that, look for partners who bring a strong ecosystem of innovation players with them. That is how you can really stay abreast and up to speed – by being part of a broader community of people and organizations focused on forging innovation.
Bryan: As businesses look to prioritize deployment use cases, do you have any learnings you can share?
Steve: Obviously, begin with a tangible business problem or opportunity. Make sure you can define a measurable business outcome. I always think it’s a good idea to start small. Expect to make mistakes and then learn from those mistakes.
Here’s the thing, though: yes, start small – but be aspirational. Right from the jump of any potential initiative, think about its scalability. Effectively using AI and becoming data-led offers the opportunity for exponential growth. Setting your sights on incremental growth suggests you’re aiming way too low.
Getting your data estate in order doesn’t happen overnight. But believe me, this investment pays off in dividends. We’re expecting a nine-figure return from our current investments in AI, generative AI and machine learning. Steve Bakalar
Let’s say you want to optimize manufacturing facility productivity. Georgia-Pacific has 120 facilities, so we don't take on opportunities that only address one facility, right? We might trial it at one or two locations, but the end goal is to scale. From a technology infrastructure perspective, ensuring you have the right platforms is key. But you must think about it right from the early days.
Bryan: Roshan, how should businesses think about effectively integrating AI into production in their business?
Roshan: You can build the best models and devise the most compelling use cases – but if people don’t embrace those tools and if they don’t embrace new ways of working, you’re not going to see the ROI you want.
You’ve got to strategize and plan to drive adoption. You’ve got to be very deliberate.
Going back to our example about digitizing manufacturing facilities, it’s always a good idea to start small. Trial deployment in one or two facilities. Make mistakes. That’s okay. Learn and adapt. Build confidence among your teams and then you’re in a strong place to scale. When you roll out to new geographies or divisions, people are going to see value faster. The new tools will more quickly become embedded in their daily workflows.
Also, as you prioritize use case scenarios, think about the impact on employees. We were dealing with a talent drain in our facilities. So, that helped us narrow our focus: what use cases will allow workers to develop and take on higher-value, more rewarding work? We’re now deploying GenAI technology that not only helps our floor operators spot potential issues but also offers step-by-step remediation guidance. This boosts operators’ efficacy and also helps these employees upskill. There’s no better way to drive employee engagement and help ensure we’re cultivating talent.
Make people feel like part of the journey – and don’t be afraid to market your success. We deployed an AI initiative focused on optimizing maintenance across 120 sites. It was super important to share that downtime results were then reduced by 30%. That should be seen as a shared success.
Bryan: Let’s finish by talking about senior leaders. Steve, what’s the biggest learning you want to share with your peers?
Steve: For me, the biggest learning is around how we make decisions. So, AI’s ability to augment decision making is clear and compelling.
We’re now deploying GenAI technology that not only helps our floor operators spot potential issues, but also offers step-by-step remediation guidance. This boosts operators’ efficacy and also helps these employees upskill. There’s no better way to drive employee engagement and help ensure we’re cultivating talent. Roshan Shah
Now, I’m going to offer up an idea that might seem paradoxical: precision in decision making is not very profitable. The fact is, if you can get directionally accurate information that helps you make the right decision nine times out of 10, you're likely moving your business in the right direction. For that one in 10 failure, it's so important to learn from it – not run from it. That's not easy, and that's where resiliency as a leader really comes into play.
When it comes to innovation: be action-oriented, be committed to learning and don’t let the pursuit of perfection get in the way of progress.
