How To Scale AI in Digital Commerce Effectively

Dana Gardner: Welcome to Don't Panic, It's Just Data, the podcast that explores how organizations turn data into a business advantage. I'm Dana Gardner, Principal Analyst at Interarbor Solutions, and I'll be your host for this discussion.

Joining me are Jürgen Obermann, Senior Go-To-Market Leader for EMEA at Vespa.ai, and Piotr Kobziakowski, Senior Principal Solutions Architect also at Vespa. They've worked with some of the world's largest brands to move from static search experiences to more powerful and dynamic personalized customer journeys.

In this episode, we'll tackle one of the biggest challenges facing digital commerce teams, how to unlock the full potential of AI-driven search ranking, and personalization. And we'll explore how technical leaders can design platforms that deliver meaningful impact for their customers. Jorgen and Pietro, welcome.

Jürgen Obermann: Thank you for having us.

Piotr Kobziakowski: Thank you very much.

Dana Gardner: Many teams see the potential of AI, but the practical blockers from data fragmentation to slow experimentation can make it difficult to turn ambition into real customer impact. Jorgen, let's start with a common challenge. Where do digital commerce companies most often struggle when adopting AI-driven search, ranking, and personalization?

[Listen to the discussion or watch it.]

Jürgen Obermann: We see three areas of problem areas with our e-commerce customers today. The first one is kind of an operational level. At the operational level all these e-commerce sites obviously have a history, they have a long development, they have fragmented environments, they all have architectures based on microservices, which was a good thing at the time. But today with AI and its performance needs for AI causes some performance problems, but it also causes flexibility problems. People talk to for the slightest changes. I have 90 to 180 day delivery times from the engineering teams because they have so many areas where they need to fine-tune things and if they touch one thing too much something else will break.

So, it's a very fragile infrastructure. That's challenge number one. Challenge number two is with regard to the customer experience and the search experience customers have on their websites. And that is really something where now with the newer AI technologies, people can do much more sophisticated, personalized search, particularly using our technology. And so that's where people really would like to see some improvements. And that's kind of the challenge they're facing that what they use today is not allowing them to do that.

The third area is the business area where they would love to run campaigns and check out if the campaigns actually have an impact. I like to use the example of the Netherlands where they have the king days and at king days everybody wears orange and somebody providing sneakers, hats and t-shirts should provide them in orange and should push these campaigns to the customers, which today takes three weeks, lots of involvement of data scientists in order to create a personalization and ranking to reflect it.

And what the people really want to do is to have basically more or less online within minutes and be able to configure this and then see the impact in A-B testing right away. And this is something where we get involved a lot with our customers because that's exactly where they want to go.

Dana Gardner: Are today's digital commerce search and recommendation stacks hitting a ceiling? When you look at a large e-commerce systems, what's the architectural bottleneck that you see most often?

Jürgen Obermann: It seems like that that's Lucene-based solutions and I used to work for Elastic for a long time so I know the environment pretty well. It seems like the Lucene-based kind of implementations hit the ceiling as soon as you start using vector operations.

I just talked to an analyst today and they told me that they feel like it's bolted on and therefore not really effective when it comes to be used in these environments. I'll give you an example. One of our customers, recently implemented our solution, was using one of the Lucene-based solutions and they had about four queries per second using vector operations in the background. We implemented our solution and we were using vectors and tensors to do this, not much different to what they were using before.

We could come up with 4,000 queries per second. So, you see that there's two orders of magnitude difference. And this is sort of the architectural bottleneck a lot of these people face besides being a bit too distributed. They have search and ranking personalization divided and have a network in between, which causes latency and so on and so forth. But Piotr, maybe you can give us from a technical perspective an additional view on this.

Piotr Kobziakowski: When you think actually about the multiple systems involved in an e-commerce operation, first of all, we're coming from the search, so we need to find items, that we need to rank them, we need to personalize them.

But then this requires a lot of other things as well, capturing the signals from the users, updating the feature stores, and then inferring on the information that are captured with different machine learning models in inference platforms, usually also outside of the main systems.

So those things basically need to be interconnected, right? So, the calls between platforms. That's one thing which is actually very limiting the speed and response because every connection, every call to the API causes the delay, right? But also if you have systems, let's say system for ranking, system for search, system for recommendation, they usually need to replicate the same catalog multiple times and then the information needs to be collected.

So not only you store data in multiple places, but also you slow down your actions. Then if you like to use all of these components into one single system to provide good answer to the end customer. This requires connecting calls across all of the systems. This data is not really available that fast as it will be in single system, right?

So that's definitely a big, big bottleneck in making the systems work fast. And also from the operational perspective, that's roadblock actually to update all of the systems. If you introduce just single field to add one more feature, that means that you need to update your APIs. You need to update all the things around all the system. So definitely this is really slowing down evolution and innovation.

Dana Gardner: Piotr, let's talk about what AI native search architectures look like. If a technical leader were designing a digital commerce search and ranking platform today -- with those vectors, the tensors, and the real-time inference -- what fundamental design principles should they prioritize? How should this be done properly?

Piotr Kobziakowski: From my perspective, we should look at all areas where the bottlenecks are. We already discussed that, right? We need to put a processing where the data is. So, to shorten the path and enable richer and better calculations on the all signals and data which we have. So effectively, when you look at the Vespa architecture, so that will be, from my perspective, really go to platform for e-commerce.

And then from simple perspective, because you can combine standard product search, Lexi, or semantic, you can basically implement signal handling, so feature store that you actually learn from interactions from user interactions. And then you can serve recommended elements or items very nicely and quickly because this is just another element in your rankings.

And obviously, I mentioned ranking and then what does it mean ranking? Ranking, real-time ranking means that you can perform all the calculations, not just how to order the elements based on your text input. but also include in this ranking business logic, which will be prioritizing, for example, items which are better for the revenue, but not losing the element from personalization, which is making customer feel that system understands them what they like and then what they actually see. So we have very good examples across many customers we are interacting that if users are looking for the cars or mobile phones or houses, if they have let's say,100,000s of offers for different things to actually navigate even through the search, keyword search, or let's say even the semantic search, it's very hard to find those exactly things they are looking for. Let's imagine that I would like to find the car, which is specific engine, specific, let's say color.

And things and then if you enter the website and you have that personalized you see cars you are normally interested how much faster you'll find when this is really precise car you are looking at and then instead of 3,000 you see only maybe 200 which are exactly what you need. So, your decision-making process will be much faster.

Another thing another topic is obviously that a platform which does all these calculations. Let's say it has to have really proper document store. It cannot be just for the search inverted index, which will be enabling us to do lexical search or just vector search, which is a vector store which enables us the vector search or just basically the feature store which will keep the values for example for personalization, let's say profiles for the users.

[Listen to the discussion or watch it.]

It has to be all combined because when you do ranking, you need to reach out to all of these data sets at once in a very fast way. Because if you have, let's say 10,000 queries per second, you can imagine that the load on the network will be extremely high if you need to make this cost to separate platforms. We are not talking about megabytes. We are talking about even gigabytes per second when we have combined systems, meaning distributed systems. So, the network is having extremely big importance. If you can put that next to the data, let's say this access enables you to do direct calls to your data, it's much, much faster. So again, many of these things are represented today in tensors.

We hear a lot about vector databases and vectors, but vectors are just small subset of tensors. Tensors can represent map of vectors, scalars, matrices, or maps. And then now when you think about personalization items, how you represent user, user can be represented as a no single vector, because if you have multiple categories, obviously we don't like the same cars as we like t-shirts.

So, we need to have multiple elements that represent different categories. And then we can put that in single tensor in Vespa and then use it in our ranking to calculate simple dot product operation and then really have accurate representation of what user likes. So again, now when we think about tensors, we should actually think a lot about machine learning models. So obviously inference and whether the inference is happening is also extremely important.

When you have access to all of this data, we have tensor representations, then it's natural to run different models like GPT models or any ONNX model, which you can download and you can experiment and use this data immediately in your ranking process.

I didn't mention yet about Vespa ranking. Vespa ranking is not as people understand traditional system where you do let's say, hybrid ranking, and then you'll find the documents which are lexical and semantics in combination. Vespa ranking is really the big, let's say, system which enables you to divide your calculations into three different phases per node and then per global cluster. You'll be able to execute any type of mathematical operation there.

Also, use any signals from typical lexical and semantic world, combine it with business logic with if conditions, let's say, full conditional structures, and then you can really build nicely all of the logic. Also, ranking enables you to expose all the calculated values, which can be used later on to optimize and then use it for training models which will, for example, fine tune weights for each segment of the ranking to be the most accurate possible.

Dana Gardner: Let's drill into the personalization a bit. I should think that the digital commerce systems of the future need to adapt. They can't be static and rule driven. They need to be more adaptive in real time.

So how do these systems, the personalization systems that rely on nightly batches and static segmentation, manual tuning, how do we move them now to a world where ranking adapts instantly to user behavior?

Piotr Kobziakowski: Yes, because of the topics we already discussed in the previous question, as we got that in separate systems, we needed to collect information. We've been not able to run quickly operations on, let's say, millions of the users easily, right? Because the system got their own limitations, and then it was pretty hard actually to do it.

When we move to Vespa, we can shift some of the operations from complex models into just tensor operations because it turns out that actually when we do tensor representation for profiles, we can basically update these models in the update the models based on the user signals and then updating in Vespa is possible. After all, Vespa is enabling a partial update.

So, you can update not just single fields in the document with really high efficiency, but also you can update even single cell in the tensors. So that enables you to manipulate every factor of the personalization and quickly do the dot product calculations. So that's similar to vector search, which enables you to find quickly documents, are, let's say the closest to what users like. And then when you have this ability, you can easily combine that in ranking with your text search.

We can actually start from the text search. You have some results. And then in the second stage, which I mentioned, you apply reordering based on the user preferences and then users will see what he was looking for but in his own preferred, let's say, colors, shapes and everything. So that also gives this feeling of a really good search because then we are not getting things which we don't like upfront, right?

Dana Gardner: And let's look at the impact on the resources needed as we make these advances. So from an engineering and operations perspective, what's the real cost of running search and vector and recommendation stacks as separate systems? What inefficiencies arise when organizations spread their search and vector retrieval inference across different services and databases?

Piotr Kobziakowski: When we have everything actually separate, even lexical semantics are separate, then another re-ranker which will be there. Then another ranking platform which will be putting business weights into every single document. And then you have recommender component that will be learning from user behaviors, doing the nightly batches or let's say hourly or any other period of time.

Then you will have feature store which will keep users. or let's say models, the model server and inference platform. When you think about that in many of these, many of these systems will have replicated data, right? So first of all, they might be actually data-driven inconsistency between these data sets because when we do ranking, we may update maybe later our catalog, not in every systems or something will happen that inconsistency will be there. So that's heavy risk of actually having broken results. Then obviously latency. So we already spoke about this latency and calls. We are not looking at single calls. We call, we look at the thousands of calls per second. So that generates a lot of a lot of data transfers across network. And then it's heavily impacting P95, P99 latency on the system, which is very important for the user experience. So platform complexity.

You already mentioned at the beginning that innovation is a key today because the world accelerated heavily, right? We see models every day. We see innovation every day. So, if we are not able to modify our path from to compete with other systems, which are now built in, as startups, that's really bad thing because we may lose a position from the leader and then be the last one if we'll be not competing.

We need to think about how to make this complex ecosystem much simpler to be able to introduce changes and modifications every day. So again, there is also aspect of testing. Testing is not trivial, right? You need to have really ability to run the, let's say one schema of ranking, how you'll be doing this and performing your operations. And then Vespa enables you also to run multiple different ways, how you will be running different ranking profiles just by setting the parameter so you can create almost unlimited number of ranking profiles and do the selection of the ranking profile to make it actually comparable across different sets of those.

Dana Gardner: It certainly sounds like the implications of AI-driven commerce is forcing a reckoning of search almost from top to bottom. Let's talk about the migration of how you get from current state to the next state. If you were leading a move from a legacy search stack to an AI native platform, how would you phase it? How would you go the crawl, walk, run in order to get there?

What do technical leaders need to modernize search and recommendation? You just can't rip and replace legacy systems. What are the practical steps to make this transition?

Piotr Kobziakowski: The biggest challenge is to provide the personalization to the category pages. When you visit your websites, you will see the products which will be, ordering will be driven by business and recommendation and personalization features, right? And this is usually not heavily implemented in e-commerce space. I would start from the personalization component.

This personalization component requires copy of the full catalog. as we already discussed, because these catalogs live in all of these components, right? So, you start doing the category pages, you start building the catalog, and you start at the same time thinking about, can I use the same catalog in the same platform for semantic and lexical search, right?

By successfully moving the personalization, you can realize that adding the search component to that is not really complex. It's a trivial task because you can build just new ranking profile, which will be responsible for search. And you already have the personalization component, which was built for the category page. If you fuse those two, you have now personalized search for the user.

So step by step, using this kind of approach, you'll have really easy move and of course when you do category pages you can start from single categories as well and then see how they perform measure the results and then add more categories once finalized then you move to a lexical and semantic search but then you don't start from the lexical and semantic search because you don't like to change things which work today well right you will move that later when you have implemented personalization components And then you will see that you can save on the same, reusing the same data in the same platform. You will not on that over the time, you will not need additional platforms to actually run around this.

Dana Gardner: Before we close out from a go-to-market perspective, Jürgen do you think some of the business leaders are underappreciating the impact on search that moves towards AI involved? What we've been hearing from Piotr sounds fairly involved. But do you think it's under-appreciated on the business side of what it takes to make search evolve along with AI?

Jürgen Obermann: Yes, we need to move to AI, but nobody really knows what the impact is of doing AI is because AI is a very, very wide kind of expression. What we see is that there is a push to do AI, but almost from a very high level management perspective for the sake of AI, not realizing what the impact is on the existing infrastructure.

For example, the effect of this by using AI technology for e-commerce is some of our customers in a one-on-one change of infrastructure from whatever they had before to Vespa, they had in certain categories up to 20-25 percent increase of revenue because of the better representation of their products, the easier access to their products and the more personalized delivery of the information to the customer.

The impact is profound. And I think where the gap is today is to understand from a business perspective. Yes, they want to do AI. But how do they get AI implemented in a way that is actually useful for the company? And I think this is the challenge today, where we also sometimes struggle because AI is such a wide experience that you really need to clarify that, but while it's done, I have not seen any manager or product owner who would not be excited to have this type of implementation.

Dana Gardner: Piotr, regardless of the level of AI adoption, it certainly sounds like the usability and detail availability for digital commerce is in itself a force to change and improve your search capabilities.

What advice would you suggest for technical leaders who want to deliver those usability and commerce benefits to the business? How should they start rethinking the digital commerce and architectures? How do you get the technical people to be able to deliver on these promises?

Piotr Kobziakowski: I advise that they look at the AI itself, right? AI is very broad topic. We have obviously large language models (LLMs) that everybody is evaluating now with ChatGPT, Perplexity and other systems, which do one thing. They understand and answer the questions.

But there is a hidden fact about those LLMs. For example, they can be extremely good at extracting features or information from the documents. So they can be used to improve the understanding by the system itself ,what the items are, and what they are for or how they can be represented.

When you have these representations, obviously those mostly will be generated into tensors. So you need to have platform which will handle that very well. The whole ecosystem which will be around those AI systems, LLMs, and other models. It has to be really cohesive and working in tandem with every required features together.

I suggest that they need to look in all of the parameters that we mentioned, such as latency, data availability, how updates can be done, and then how we can combine it all together into one single response to the user in the fastest possible way. They need to think about all of these aspects, and then when they look at Vespa they will realize and understand that Vespa is not the search engine, is not just vector database, but it's a platform that delivers all of these components into one single system.

Dana Gardner: Thank you so much, Jürgen and Pietra, for uncovering some of the details and complexity involved with transitioning digital commerce. It was a pleasure to have you on the show.

Jürgen Obermann: Thank you.

Piotr Kobziakowski: Thank you.

Dana Gardner: For our audience, if you enjoyed this discussion, we'll be back next week with another episode in our ongoing podcast series.

Until then, make sure you subscribe to this podcast and all major platforms, and follow the conversation on our social channels at EM360 Tech on X and LinkedIn. And for more insightful daily content, head over to em360tech.com. Thank you again.

(Vespa.ai supported the creation of this discussion).

[Listen to the discussion or watch it.]

How The Open Group Portfolio of Digital Open Standards supports your digital business transformation journey

The next BriefingsDirect enterprise architecture (EA) discussion explores how a comprehensive portfolio of open standards and associated best practices powerfully supports digital business transformation.

As companies chart a critical course to adopt agility using artificial intelligence (AI)-driven benefits, they need proven and actionable structure to help deepen customer relationships, improve internal processes, and heighten business value outcomes.

Accordingly, this latest BriefingsDirect interview explores how The Open Group Portfolio of Digital Open Standards instructs innovation internally to match the demands of a rapidly changing, increasingly competitive, and analytics-intensive global marketplace.

Listen to the podcast. Find it on iTunes. Read a full transcript or download a copy.

 

Joining us to explore how to strategically architect for ongoing disruption and innovation is our expert guest, Sonia Gonzalez, Digital Portfolio Product Manager at The Open Group. The discussion is moderated by Dana Gardner, Principal Analyst at Interarbor Solutions.

 

Dana Gardner: Sonia, what are the latest trends and catalysts propelling the need for a full portfolio of standards to better attain digital transformation?

Gonzalez

Sonia Gonzalez: Digital transformation is something that can completely change your company. It’s a process, a journey. But to do that, you need to start from the top. Meaning from a strategy; you need to have a digital strategy. Because you must change your business and operational models, you need to build new capabilities – and not just technical resources or technologies, but also the people. You need to train your people to pursue innovation and to create your business around customer centricity.

Of course, you need to take advantage of new technologies and trends, such as AI, the metaverse, cybersecurity and cybercrime, which are threats right now. You also must leverage the new power of computing in data processing, data analytics, and machine learning (ML).

 

If you don’t take advantage of all of that, then you’re going to be left behind. That’s why, a few years ago, we started this journey to conform the Portfolio of Digital Open Standards as a collection of practices that will allow any company to start the process, either if they are new to this (starters), or to maintain and especially sustain an already existing digital transformation process and effort.

That’s the reason we need the portfolio. We need different perspectives. It’s not only the technology, not only the people -- but it’s also everything that surrounds your company.

Gardner: Sure. And, I suppose, we used to look for silos within an organization with areas of expertise and would develop standards that might pertain specifically to them. But nowadays it’s important to have cross-pollination, if you will, and to look at standards not only in an isolated part of a company, but across them. We seek ways for them to better support each other.

 

Please explain why it is so important to seek a full portfolio of standards that are, if not integrated, complementary.

 

Digital change demands diversification

 

Gonzalez: Yes. The key words here are synergy, cross-collaboration, and consistency. All of our standards are very powerful on their own. For example, we have the TOGAF® Standard, which is one of the major recognized standards for Enterprise Architecture (EA). We have The Open Agile Architecture™, a Standard of The Open Group, for agility, you have the Digital Practitioner Body of Knowledge™ (DPBoK™) for digital; we have the ArchiMate® Standard for modeling in EA, we have theIT4IT™ standard for the IT processing and digital products.

 

But if all of them are used together in a consistent way, they become more powerful. They are greater than the sum of their parts. They provide a full and more systemic view of your issues.

 

For example, to make digital transformation possible, you need EA because you need to know and understand your capabilities, your landscape, and your actual measured capabilities to identify the gaps. But you also need to be able to manage digital products. And for that we have the IT4IT™ standard.

We need to pursue more agility, not only in the development or in the technical processes, but on the business as a whole. You need to become an agile enterprise.

We need to pursue more agility, not only in the development or in the technical processes, but in the business as a whole. So, you need to become an agile enterprise, and for that we need the Open Agile Architecture standard. Also, the TOGAF standard in which you will have guidance about agility.

 

If you want to have more customer-centricity and a learning progression toward digital, we have the DPBoK™ that precisely helps you understand how we can start very small and then continue evolving your capabilities, determine what you’re learning in the process, and how you can be evolving all those capabilities.

 

Those are the main aspects to understand and the benefit of having this portfolio. It’s not only that the content is in there in the same channel, which is completely HTML, you can cross navigate between the standards, you can make searches where you will find content from the different standards. We have also added some graphics icons in there, because as you know, people need very rapid solutions to the problems now.

 

Last April in Edinburgh as part of our summit, we released a new version of the Portfolio of  Digital Open Standards. If you click, for example, in agile practice, immediately you will see content from the Open Agile Architecture. But from there you can navigate to the TOGAF Standard, and you can navigate to the IT4IT Standard for example. So that’s a way that you can, as a user, you learn from different practices because at the end of the day, it doesn’t matter if you are using the TOGAF Standard or the Open Agile Architecture or the DPBoK™. What you want and what you need is a solution to your problem. And you find that through this synergy that is found in the portfolio.

 

Gardner: The Open Group is a long and venerated organization going back to the standardization around the UNIX® platform back in the day. We don’t need to go that far back, but I would like to put a little context around how the whole concept of a digital portfolio working as a concerted effort among different disparate parts came about. Can you give us some background and history as to how the digital portfolio came about and where it is now?

 

How we got here

 

Gonzalez: Actually, that’s an interesting story. I started back around 2015. I remember that I was at one of the events and I remember being in an internal meeting, where there were some people from a few member companies who started this activity. At the moment, I was the Architecture Forum Director but as you know, architecture is closely connected with digital. The first name of this workgroup was not Digital Portfolio Workgroup as it is right now, but it was something related, such as digital business and customer experience working group. It was a very long name, and the idea was to combine digital with customer centricity.

 

So, we started working on that. In the beginning it was a very close, small group of members. Then a decision was made to make it a work group in order to allow members from different Forums such as Architecture, ArchiMate, IT4IT to engage. We started to grow and grow. We delivered a couple of white papers that have been published and are very good. Eventually, we decided to change the name from Digital Practitioner Workgroup. At a certain point, it was also led by a subgroup of platinum members at the board level, that happened around four or five years ago. They said, okay, we need to move this forward. So besides having content that is related to digital, we need to start to provide this content as a digital product.

So, we started the process to deliver standards as code. That’s why this small group of members started this work initiative, like I mentioned--it was led by one of our staff members. The idea was to start taking all this content, put it in an open source platform called GitLab, and produce the output in another open source platform called Antora, that allows you to generate from different GitLab repositories using markup language (AsciiDoc) , HTML-based output, which is designed to cross navigate between content, which is one of the main principles of the portfolio. And they also started building the first graphics icons and the photographic interfaces.

At that moment, it was a work activity led by members. Eventually, around two years ago, it was decided that it become part of the staff activity, to be maintained by members in the Digital Practitioner Workgroup. I moved from Architecture Forum space, and I was named the Digital Product Manager for the portfolio. We decided to change the name to Digital Portfolio Workgroup because we didn’t maintain only the DPBoK™ Standard anymore. We maintained the entire environment. We pursued cross collaboration. We tried to engage members from different forums.

The idea was to start taking all this content and put it in an open source platform. ... designed to cross navigate to deliver operating standards.

The task now has three main components. One is the content, which is provided for our members. The second is the platform that we have in which we are producing or delivering our standards as digital products. And the third one is delivering standards as code using also GitLab and Antora, facilitating a more agile DevOps oriented way of delivering standards into the market, which I believe is something innovative. I don’t believe any other standard organization is going that deeply into standards as code. It might be the case, but we are the ones who are starting with this activity.

 

The story is that we went from being a very small work group that then became more active, and then it became something that was driven by staff and with the collaboration of members in the work group.

 

At the moment, we have already delivered four different releases. The first one was in Edinburgh, in October 2022. Since then, we have had several releases. We have been adding new standards into the portfolio. We have been adding new guides because the portfolio is meant to have standards, snapshots, and guides. We started improving the landing pages and creating new landing pages. For example, we added the ArchiMate specification along with the landing page in the release in October 2023 in Houston.

 

For the one that we had this year in Edinburgh, we improved the landing page for the IT4IT™ Standard, which is called the Life cycle, and it also has some links to the DPBoK™ We made some improvements to the general user interface and very importantly, we received some feedback from members saying we need case studies, we need more than how, we have guides, we have standards, but how other people have made this work in a practical way. So, we start talking with members of the Government EA Group and the ArchiMate Standard, because the ArchiMate standard they are the ones that have also several case studies.

 

So, we constructed another instance in GitLab and Antora called the case study collection. This case study collection is meant to have case studies from different verticals, and it is connected with the portfolio. So, in the most recent release, I gave a demo in which you can click on one of the icons in the portfolio and you will be directed to the second instance where the case studies live. From there, you can go to categories such as government, healthcare, or banking and finance.

Also, you can go to physical items such as mechanical constructions and things like that where we have related case studies available. At the moment, we have migrated five case studies, two case studies from ArchiMate®, the ArchiSurance and ArchiMetal, and the other case studies are from the Government EA Work Group. At the moment, we have a very long queue for requests for more content to be added, more guides, more case studies.

Also, at the moment, the connection with the TOGAF Standard is through the TOGAF Standard 10th Edition. We have started the migration to also add the whole set of the TOGAF Standard into the portfolio, which is the highest priority for the rest of the year. Our objective is to have an incremental, ongoing improvement of the portfolio. So that’s where we are right now.

 

Gardner: Wonderful. Thank you for that comprehensive overview. Let’s go up a few thousand feet and start to talk about why this is so important. Everybody agrees that digital transformation is essential and integral to their success, but not very many people agree on how to go about it. As organizations are facing the need to transform and consider more of the benefits from ML and other analytics technologies, what are the challenges? What prevents people from being able to transform and innovate in their organizations as quickly and as powerfully as they’d like?

 

Where to develop your digital strategy

 

Gonzalez: I think one of the main challenges is to know where you are, you know, that’s why EA is such an important pillar in this. If you don’t know what you have, it is impossible to have a feasible path ahead to becoming digital. You need to understand your current state. Do you have a digital strategy? Do you have a strategy at all? And is it clear? Has it been shared?  When you have your strategy, you start having a digital strategy. You start considering, okay, I already have these processes, I have this business lines, I have this product. It doesn’t matter if you are a company that is completely in the digital product business, it can be applicable to all companies. We had a very interesting case, last week in Edinburgh, about banking in Turkey. For that digital transformation, first they identified their current state, and then they identified again following the EA principles.  So, start small and grow incrementally

 

Identify an area that is less difficult to digitize and digitalize and transform and deliver the first outcome, and then you can continue to iterate incrementally. For example, if you are a company that is having issues with the supply chain, you go and make a value stream assessment and a value chain assessment and start identifying what you need to digitize in there. To digitize is to put your information in digital format.

 

And sometimes people, companies, they don’t even have that. They may have perhaps information in digital format, but it’s not consolidated. So, data analytics and data transformation are one of the steps. After you have digitized that, you need to start making the digital transformation with your processes and your capabilities. Meaning your people, your applications, your infrastructure. You need to make an assessment of that.

When you talk about digital transformation, it's not a one-time effort, it's a continuous and ongoing journey. You start doing it incrementally. 

For example, you need to ask yourself: Do I have the right capabilities for this? If I need to start having a manual process become completely automated and put it in one of our channels to become more customer facing, I need to improve the process. I need to train the people, probably hire new people. I need to automate, and I need to build a new channel for that, which of course comes with application and infrastructure layers. It’s a step-by-step process that starts with identifying where you are, where you want to be, and how to get there incrementally.

 

That’s why when you talk about digital transformation, it’s not a one-time effort, it’s a continuous and ongoing process. It’s a journey. You need to start doing it incrementally. In this case of the bank mentioned before, they started first by putting the data in order. They selected a critical process and improved it, and then they started improving the channels and then they started doing more things in digital and then started taking advantage of other technologies, such as AI, in their channels in the later stage. You need to start first with the simpler things that you need to do, because otherwise, if you start just changing things without connection and alignment without the impact assessment, the systemic view, you will be lost and the transformation effort will fail.

For example, people believe that, okay, there’s a new technology coming, I will implement it. They don’t take into consideration the current infrastructure, the current applications, the current capabilities of the people they have, whether they have legacy systems, if they have people trained. If they implement this new technology for the sake of implementing, it creates technical debt and unnecessary risks and even potential security breaches.

That’s why again, risk assessment is another key component of this. If you are transforming, but you are not aware of the risk, then you are creating another issue for your company. So, it’s a step-by-step process that you need to take. That’s why I always say that it’s very similar to EA, only in this case the customer is at the center and considering going digital. That’s the difference.

 

Gardner: Right. And certainly, undertakings to do digital transformation can swiftly become very complex and unwieldy. But when you apply structure, pragmatism, documentation, making sure that everyone is on the same page and collaborating accordingly, that complexity becomes much more managed. So as organizations use such things as the Open Group Digital Portfolio, what are some of the salient and most important benefits of that they’ll start to see?

 

Things that are perhaps a little intangible, difficult to measure, but nonetheless very important. What do you get when you do this right?

 

Measure your processes’ progress

 

Gonzalez: I think measurement is perhaps one of the more difficult things. For example, one thing that could be measurable is if you are delivering a product in a certain amount of time. You improve and digitize and digitalize to make your process better. You used to spend two hours on that; now you spend an hour and a half. That’s a metric. The other one is how efficient your people are becoming because one of the conditions for becoming digital is to transform your organizational structure.

 

If you have a structure that is too hierarchical with a lot of levels and people don’t have the freedom to act, it is very difficult to become agile or to become digital. But when you have autonomous teams, and each one of them has ownership of a specific part of the company or a product or a line of product and you have a cross-collaboration between them such as multidisciplinary autonomous teams and you give them ownership, you can start measuring what they are doing.

If you have a structure that is too hierarchical with a lot of levels and people don't have the freedom to act, it is difficult to become agile or digital. When you have autonomous teams, you can measure what they are doing.

Similar to the earlier scenario, you might have a one, two or three-day cycle and then you make a retrospective review and you re-measure, that’s what you need to do to start. For example, it would not be a good approach to make it three months and not measure what you’re doing. It should be shorter, smaller cycles. And again, you need to create these autonomous teams. In the O-AA™ Standard, there’s a lot of very good content about autonomous team and the fact that sometimes we organize our capabilities for physical or human resources following the same structure of the company.

 

So, they become very silo-oriented and very functional. The moment that you start seeing for example, how to deliver a new product into the market. Let’s say it’s a financial product, a new kind of loan or whatever. I need the person that manages loans. I need the person that manages legal aspects. I need the person that will handle the insurance policies that are related to that loan.

 

Of course, I need IT people, and I need data people. So, when I take this group of people together and they become autonomous, with a certain level of control of course, then they should be able to deliver this product to the final line easier and faster than if you have a very hierarchical structure in which you need permits and in which every area only owns and sees their own piece of work and not the whole thing. You need to own your process. You need to own your product if you really want to succeed at this.

 

Gardner: And of course, the proof of the pudding is in the eating, and you mentioned earlier that you’re now focusing on some case studies, which I imagine illustrate the important use cases. It’s another way of understanding the benefits of a digital portfolio and EA and a strategic approach to transformation. You don’t have to go into too much detail because I’m sure people will be able to access and review these case studies on their own, but maybe you could go through a few of the new case studies and why these use cases were so salient to begin with.

 

Case-by-case connections

 

Gonzalez: I think they are important. Some of them, especially the ones from the Government EA such address actions taken by the Indian government. They took on a project to improve certain areas in the government in order to offer better services to citizens. They started to improve the processes and applications around them. In the end, they started measuring the from the start, state A, and where they were at the end in terms of citizen satisfaction. There was a measure for that.

There was another very interesting one, but we haven’t migrated that yet. It is use of digitalization and digital information to improve the COVID vaccination process. At the beginning it was very messy. As you know, it was a health issue related to people’s lives. It was critical. It began with some initial automation. Then they created a portal in which people were able to create an appointment for their vaccinations with details such as how to get there, how to get their vaccine, how to access and update their medical records.

If they had COVID before, what were the symptoms? Which dose might they need? The process made it around different areas of the country by starting small and then growing and growing. In the end, even though the COVID emergency is somewhat lessened now, they decided to keep the project going in different stages and use it in other parts of the healthcare system, because healthcare is one of the sectors that needs this more.

 

You know, sometimes healthcare systems are inefficient because they are not connected. You don’t connect your medical supplies with your patients or with your healthcare centers. Sometimes some health centers will have medicine, but some others will not have it, and that’s also another case study that may be coming soon, probably in the next month, into the healthcare-related section of the portfolio.

 

As you know, we have a Healthcare Forum in The Open Group. They maintain a Reference Architecture which is into improvement, and we have a case study from a hospital. And they are working on how to better connect the different elements in their value chains, such as healthcare centers, providers either for medicine or medical equipment, patients, patients’ records, and all that. This content might be incorporated into the portfolio later this year.

Sometimes healthcare systems are inefficient because they are not connected. Now they are working to better connect different elements throughout their value chains. 

In another case study, they implemented an Enterprise Service Bus (ESB) to be able to improve their processes. They had a process that was not really reused in the services, so they improved that ESB to be able to provide better service to their citizenship.

 

As I said, all of the case studies are on our website, and I invite you to look it over. All of them are very interesting and they describe the benefits of using The Open Group standards. They also mention other practices because we cannot pretend like we, as The Open Group, hold all the responses. There are other standards in the market that are valuable and important. That’s why whenever we refer to a third party in our standards for publications, we always ask for legal permission. But those case studies also have that connection to external sources, and they are completely reachable through the case studies.

 

Gardner: Right. And so, looking to the future, digital transformation now is perhaps under pressure as organizations adjust to more AI and analytics, and data driven decision-making. It seems that there’s pressure on organizations to move quickly. If their competitors do AI perhaps better than they do, they could find themselves at a disadvantage.

 

As we look to the future of how digital transformation unfolds and how the digital portfolio can be very instrumental and accelerate their objectives and success, how is AI impacting this equation? What should we expect over the next several years in terms of how organizations can make AI a friend rather than a foe?

 

Put AI into the proper context

 

Gonzalez: Yes. That’s a very good question. Actually, that was one of the main themes at our event in Edinburgh. We had very good presentations there. I think AI has two different edges, like you said. It has the good one, which is that it could be very powerful if used properly but you need to be aware that it’s not magic. If you have acquired an AI tool or have partnered with someone that provided you with that tool, you need to be aware that first you need to have the right data for it, the right information to inject into that tool. You need to give that tool training because there are some patterns that they have. You need to do programming to be able to really have the AI give you the right response. The tool also needs the right context for the data you are feeding into it to receive a consistent output

 

You need to make a special request, and you need to give some training, invest some time, and feed data and training into it so that this AI activity can serve you for several purposes. It can be just for cognitive analysis which is useful on its own. It can be very helpful for giving you decision-making support and it can be the other side of that, which is generative AI where it is generating new content from your input.

 

So, you put that text or images or content in whatever form and it should be able to create a video, to create a story, to create a summary, to create a report, to create whatever kind of output that you need. But in order to do that, of course, there are some very critical capabilities that you need to build and that’s why again, if you are in the middle of your digital transformation and you want to include AI, you need to be sure that you have that capability.

 

For example, critical questions to ask are: Do I have the right data to feed into that AI tool? Do I have the right texts, images, and content because you need to give context. Even a simple ChatGPT that you use in your phone, if you ask a question and you don’t give the context, it will provide a very inadequate response. You need to give context. Sometimes the same AI will ask you “I need more context”. So, you need to provide the context.

You need to have an algorithm in that AI tool that is able to interpret the data you’re giving it to predict because they learn from experience and to act on that and you need to be able also to address your output.

The output could be a success, but it could be a failure. It’s not like because AI told me this, I’m going to use it. And also, you need to be adjusting the algorithm that it uses, the learning process, the outcomes, analytics.

 

The other thing that is very important is to understand that there are different components around AI, we have ML, we have a neural network, which is the way that this information is processed and when it is able to learn from the past experience.

 

Of course, there’s another one called natural language processing (NLP) and learning, which, by the way, is one of the more active working groups in The Open Group. Of course, they need cognitive computing, so we need to be aware that you need resources, infrastructure for this.

 

And now that we are thinking about sustainability, AI is going to become one of the threats in terms of sustainability.

 

We had another very good workshop in Edinburgh about the carbon footprint and even though AI is good, as all technology could be good, its footprint is actually going to become exponential because AI requires a lot of technical resources and power consumption.  It needs a lot of computing processes to be able to generate the output, especially if you’re going to generate complex things such as a video or song or any other kind of generative content.

We're already seeing evidence of jurisdictions looking at the increasing power and water demand ... so they can understand the implications on utilities and infrastructure of these new AI workloads.

And of course you need to have a vision. Why do I need AI in the first place? Is it really that I need it or because it’s in fashion, because everybody is talking about it. You need to have the right capabilities for that. For example, you have to have an AI policy. We already have an AI policy at The Open Group that we have shared with our members. For example, confidentiality is one factor. You should be very careful if you put some critical information in an AI tool. If that AI tool is behind a locked, closed door, where it’s not public, then probably you’re fine. But if you take a public tool and you put private data in there, then there can be cybersecurity threats. Cybercriminals are already using it.

 

Another important aspect for the AI policy is legal considerations. Legal needs to be involved in the policy. You need to also make a risk assessment before implementing it and of course you need to be sure that you have the technical critical capabilities.

 

And there’s another important one. You need to teach your people. I think everybody talks about AI and it’s an interesting topic, but we all need to learn more about it. I know people that are already experts, but we need to learn more about it before using it in the company because it can become a threat

 

You may have already heard in the news that there are already criminals using AI to generate fake calls. You receive a call and it’s your son’s voice telling you that he has been kidnapped. So how would you react to that? Or there are artists and singers that have been making demands and lawsuits because someone has imitated their voice. They’re singing using AI.

 

It can be used very well or very poorly depending on the programming which is also a little scary. For example, you can use, and it’s already being done, you can use drones or small robots in a war zone just to see if there are people that are alive who need to get medical care, but you can also use it to kill people.

 

It’s a double edge. I think it’s excellent to take advantage of what humans can do with technology, but it can be used -- it should be used very carefully, and it should be used knowing that you have the right capabilities and especially can respond to the critical questions--Why do I need AI? How am I going to implement and what am I going to do with it? That is my advice in that regard.

 

Gardner: Great. Well, it sounds like not only is the digital portfolio very important for making your journey to digital transformation smoother and faster, but it certainly also sounds like it’s very important to mitigate the risk when it comes to adopting AI and similar technology. So that’s very exciting for the future and I hope you get a chance to talk about that more.

 

Before we close out, Sonia, please help our readers and listeners understand how they can become more actively involved with The Open Group in terms of events, certifications, resources and specifically how they can start to avail themselves of the digital portfolio.

 

The Open Group support and opportunities

 

Gonzales: Okay, thank you for that. First of all, I invite our listeners to go to our website. On there, you will find important information about all of our standards. On the main page, you have to scroll down a little bit. You will find something called the Portfolio Digital Open Standards.

 

In there, you can read what it is. You can click and you will know more about it. There’s a video from our co-chair and you can click and actually you can experiment with the thing live. It’s completely live now and you can start navigating in there, trying it. You can make searches. You can go to the case study collection.

 

More importantly, please give us your feedback. There’s a small icon in the top right of the screen, which you can click on and send us your feedback. It’s completely private. That feedback is only seen by The Open Group staff. So don’t be shy about putting your email in there because it’s something that we treat very carefully.

Also, in our website, we have information about certification. Also in certification, we are going into a learning progression. Those of you that are familiar with the TOGAF certification program, it has gone now into this badge program in which you become certified in level 1 and 2 in the TOGAF standard.

But then you can become specialized in business architecture, risk and security, agile, digital. We had to couple that precisely to give this more agility and we are going to follow a similar one for the DPBoK™ Certification. There’s already plans to restructure the DPBoK and to also restructure their certification program.

 

More importantly, if you want to contribute with us besides giving us feedback, you can become a member of The Open Group. It has a lot of advantages. You can go to our website. You will find information how to become a member. You will see the cost for a silver, gold, or platinum member. You will see a little bit about the benefits. You can ask for more information, reach out to our business team.

 

Also, you can send me an email. Please contact me, drop me an email and I’ll be more than happy to help you through the portfolio. You don’t have to be a member to have some kind of an onboarding session with me. I can give you at any moment on onboarding session and explain to you what we are doing even if you want to just know the portfolio, or you want to become a member. We can also engage with our business team for them to give you more explanation about the process of becoming a member.

 

If you want to become certified and you’re not sure of the process, you can also reach out to me. If you are a tool vendor and you want to certify your tool, you can also go to The Open Group and you can reach me, and I can lead you to our certification team people and they should be able to serve you.

 

Also, please go to our social media. We have our YouTube channel in there. You will see a lot of videos, The Toolkit Tuesday, testimonials, The Open Group Blogs. We recently published a blog about the digital portfolio. We are soon going to publish a survey. It’s going to be sent to our social media probably LinkedIn, so be aware of that. Also, we have our podcast like this one, our blogs. Use our social media. You will find a lot of information in there.

And in terms of proceedings, especially if you attended the session, you could go to the proceeding and see what we discussed last week in our Edinburgh about AI. Ecosystem Architecture is another topic that we are taking very seriously at The Open Group. Sustainability is another one like I mentioned. Sometimes there’s a trade-off between technology and the environment which is becoming more and more relevant now.

Reach us through our social media, through email, or through our web page and we will be more than happy to give you more information.

 

Listen to the podcast. Find it on iTunes. Read a full transcript or download a copy. Sponsor: The Open Group.

 

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