For organizations to achieve AI automation success they need to apply three truths, according to Madhavan Vasudevan, the chief technology officer at IBM APAC.
A focus on collaboration, addressing data fabric and adopting AI and automation are the key ways businesses can enhance their AI automation success, he told delegates at IBM Think Sydney.
Vasudevan said, “You have to focus on collaboration and co-creation, you have to look at the data fabric as the solid foundation on which you can build your AI and automation layers. Finally, it’s about adopting AI and automation engineering principles in driving the adoption and the automation within the enterprise.
For organizations to focus on collaboration and co-creation, he uses the example of Formula 1 racing.
“In Formula 1 racing, it’s a team sport, the sponsor who’s typically paid for the car, registered the car, paid for the crew paid for the driver. You also have a constructor that builds the vehicle, owns the IP and intellectual rights to the vehicle itself. You have the performance engineering team to make sure it’s optimized for performance. It takes a very large group of people to make a Formula One charge go,” I explained.
Vasudevan said Formula 1 is not different from AI and automation as organizations have to embrace design thinking,
“Working with us, for example, through garage methodology that we use, whether it’s through our expert labs, or consulting, and then this approach can drive and deliver business outcomes, faster, better employee-customer engagement and experience, and, of course , simultaneously mitigating risk,” he said.
When it comes to building a solid data fabric, Shane Hill, Senior Research Analyst at Adapt said many organizations are halfway through the middle point of realizing these opportunities.
“Building the right data fabric requires more than the right platform, it involves having the right culture and engagement with business leaders, but also in the workforce. Curiosity is key, getting the right culture is a battle. But it’s worthwhile because it simplifies and improves agreement with your executive peers as you try to improve governance, funding, and also execution,” he explained.
Technology leaders have clear outcomes in mind when building this data fabric, he said.
“93 percent of organizations want this data to drive decisions, 78 percent to fuel business growth, and 70 percent to enhance customer experience. But progress is slower.
“53 percent had deployed robotic process automation, we’re seeing that 37 percent of organizations have deployed machine learning and artificial intelligence. These outcomes, these opportunities require us to have the right architecture with the right security controls, and the right technical provisions to deliver the outcomes that matter.”
Scaling AI operations, not only involve those IT operating models, but it’s also lead to data erosion as well, Hill explained.
“As we think about scaling these operating models, we need to underpin these with intelligent automation. 68 percent of technology leaders we’re speaking with are focusing on the scale of integration this year. Only 14 percent want to devote additional focus to DevOps ways of working,” he said.
Often the problem here is purely practical because organizations are still trying to fit these models into legacy governance funding project management structures, Hill said.
“The other side of this is data ownership, ownership influences culture and culture comes from understanding. 60 percent of organizations say that they have no consistent data culture in their organisation, and 40 percent are struggling with insufficient ownership of data that the business unit leaders they are speaking with.
“They’re finding it difficult to improve understanding and therefore to drive better engagement,” he added.
There are three ways organizations can apply to improve these situations, Hill said.
“One to improve the business acumen of IT and technology teams. Two, to shift accountability for prioritisation into the business. Three to leverage these two changes to create better business integration.
“Think about the metrics that will matter, design productivity found in wellness, calculate the value at risk of not making these changes and project your future value against the likely losses that will arise from not investing in these areas,” he ended.