CNME Editor Mark Forker secured an exclusive interview with Sebastian Krause, Senior Vice-President and Chief Revenue Officer at IBM, in a bid to find out how the company is leveraging their AI and data platform watsonx in an effort to help reshape the landscape of enterprise AI, the impact generative AI will have in driving new productivity – and the use-cases driving its adoption. 
Sebastian Krause has enjoyed an incredible career at IBM, one that has spanned almost three decades.
Krause made a name for himself by leading IBM’s software group across the EMEA region from 1995 to 2011, before relocating to New York, to head up the company’s global storage organisation.
In 2015, he was appointed General Manager of IBM Cloud Europe, before heading back Stateside in 2020.
He is one of the most revered and respected executives within IBM, and is currently tasked with the responsibility of helping their customers modernise and transform their infrastructure and applications by leveraging hybrid cloud and AI technologies.
CNME spoke to Krause during a whistlestop tour of Dubai, and in a fascinating conversation he outlined IBM’s plans to help enterprises succeed in their digital transformation goals.
He kickstarted the discussion about IBMs strategic shift towards a hybrid cloud and AI model, and highlighted the acquisition of Red Hat in 2018, as significant.
“Our company’s strategy is hybrid cloud and AI. In 2020, we determined that this was the best path to take, and we started making significant investments in hybrid cloud and AI in a bid to drive new innovations for our customers. For years the debate had been public or private? However, it had become very clear by then that a hybrid cloud model offered businesses the chance to experience the best of both worlds. The acquisition of Red Hat that we completed in 2018, was essentially the cornerstone for us in terms of laying the foundations for our hybrid cloud world,” said Krause.
He highlighted how the capabilities of OpenShift really served the needs of enterprises from a flexibility and autonomy standpoint.
“OpenShift provides you with the ability to integrate multiple platforms, whether that is an on-prem platform, on the cloud, or a public and private cloud, you can seamlessly exchange workloads across multiple platforms. It has become evident that companies and customers today have multiple data sources, and the data sits in different environments, so you really need an infrastructure and information architecture that allows you to bring the information together, and AI is the key technology to drive that,” said Krause.
Many businesses have struggled with the demands of migrating their workloads, and Krause reinforced the importance of having a clear strategy when embarking on a cloud transformation journey.
“It’s all about the strategy, why are you going to the cloud, and where are you going to be placing your workloads? Over the course of time, I think many companies have come to the conclusion that the workload that they initially moved to the cloud was not meant for the cloud, and it’s better to have it in an on-prem environment. On the other hand, there might be workloads that can easily go into a public cloud environment for technical, or cost reasons, so the strategy might not have been thought through completely. There’s an evolution right now in regards to where data is residing and a realisation that you can bring your compute power to the data, and you don’t need to bring the data to the compute. That is a major reason in why cloud strategies are being revised, and specifically now with the capabilities of hybrid cloud computing, and that is what we are providing through our OpenShift platform. We are giving companies much greater flexibility to deploy workloads wherever they want,” said Krause.
Krause also outlined the role played by their consulting arm in terms of helping customers better understand where they need to place certain workloads.
“We have fully embraced the ecosystem, and we are working very closely with system integrators, software vendors, services partners and consulting companies. However, IBM Consulting is equally equipped to help clients to go on their cloud journey, and to help them understand what workloads they need to bring into a deployment that makes it stick to the strategy that the customer has chosen,” said Krause.
Generative AI is the talk of the IT and technology ecosystem globally, and there is no denying the endless opportunities presented by the new technology.
However, Krause warned how businesses must take into consideration what the best ethical practices are for adopting the technology, and cited how their watsonx platform is designed for generative AI.
“Generative AI came into the public domain through ChatGPT, and it has generated a lot of excitement in terms of what it can do, but there is also dangers around generative AI, especially when it comes to data privacy. That is something that businesses really need to consider when looking at generative AI use-cases and applications. I think the technology is now front and centre, and everyone is aware of it, however, there is a big difference between using generative AI in a consumer environment versus an enterprise and business environment. Our watsonx platform is designed as a generative AI offering for business and the enterprise. Our platform provides differentiated capabilities that are really suited for enterprises, because you need to be compliant to regulations that either exist today, or are currently being worked on,” said Krause.
Krause also stressed the need for enterprises to demonstrate greater transparency, and highlighted the governance pillar that is embedded in its watsonx platform.
“You need to be able to provide transparency in terms of where your data is coming from, and you need to have the capability of data lineage. One of the three pillars within our platform is called watsonx.governance, which provides the capabilities that I have highlighted that enterprises need in order to deliver the transparency in relation to where the data stems from, what has happened to the data, and how it has been applied to this specific use-case. Nobody has the capabilities that we have brought forward in terms of what is next with governance,” said Krause.
As Krause pointed out enterprises simply can’t afford to be sloppy when it comes to data and governance.
“Enterprises can’t afford to have data that is going to their customer, or is part of their value chain that eventually has inappropriate content. If the data has not been cleansed and you don’t know where the data is coming from, and who has been working on it, then that would be a significant problem in terms of reputational damage, let alone all the issues you would face with data privacy and copyright. That’s why it has to be 100% assured that the foundational model that you are using has cleansed data and can be followed back to the source, and that’s what the watsonx.governance is doing,” said Krause.
Krause then illustrated the capabilities provided within the two other key components of the watsonx platform.
“The other two pillars of the platform are watsonx.ai and watsonx.data. watsonx.ai is basically a studio in which you can test, validate and train foundational models for your own purpose and in your own environment. You can do prompt engineering, which allows you to really make sure that you are training your model in such a way that it does what you are expecting it to do. watsonx.data is suited specifically for the application of generative AI workloads, and it allows you to have the ability to ingest and store data that is coming from multiple sources, but is also tailored and optimised for the use-case of generative AI,” said Krause.
Krause then moved the dial of the conversation towards the use-cases that are actually driving the adoption of generative AI applications.
He pointed to three use-cases where he sees a lot of activity, but predicted many more will emerge as the technology matures.
“In terms of use-cases we are seeing a lot of traction in customer care. When you are enhancing the experience of a user, whether you are doing that through summarisation, or process optimisation that is something that generative AI can do to help. These user experiences are driving significant MPS improvements because it is faster, and you are getting much more accurate information than you would have done through a traditional contact centre. We’re also seeing a lot of customers doing modernisation in their environments, generative AI can helps developers change the code they are using in a much more seamless way, with a vast reduction in errors in comparison to if humans were doing it. There is also the digital labour use-cases, which is essentially providing automated processes where you augment the capabilities of humans to eliminate some of the mundane tasks employed are faced with. These three use-cases are lifting off quite significantly, but they are also multiple other use-cases that will come to fruition very soon,” said Krause.
Krause highlighted the investments IBM have made in what the company describes as client engineering capabilities, which he feels bring them closer to their customers.
“We have thousands of our consultants trained on generative AI and the watsonx platform and they are helping customers to transform by leveraging generative AI. We have also significantly invested in what we call client engineering capabilities. Our teams work with our customers very closely, and in a lot of instances on a much smaller scope. Ultimately, we want to showcase how our software capabilities will drive tangible outcomes for the clients. That’s why we have these client engineering teams that are highly trained experts to really help customers provide the technology for their specific strategy, and the objectives that they have articulated,” said Krause.
