Ahead of AI & Big Data Expo North America – where the company will showcase its expertise – Chuck Ros, Industry Success Director at SoftServe, provided valuable insights into the company’s AI initiatives, the challenges faced, and its future strategy for leveraging this powerful technology.

Highlighting a recent AI project that exemplifies SoftServe’s innovative approach, Ros discussed the company’s unique solution for a software company in the field service management industry. The vision was to create an easy-to-use, language model-enabled interface that would allow field technicians to access service histories, equipment documentation, and maintenance schedules seamlessly, enhancing productivity and operational efficiency.

“Our AI engineers built a prompt evaluation pipeline that seamlessly considers cost, processing time, semantic similarity, and the likelihood of hallucinations,” Ros explained. “It proved to be an extremely effective architecture that led to improved operational efficiencies for the customer, increased productivity for users in the field, competitive edge for the software company and for their clients, and—perhaps most importantly—a spark for additional innovation.”

While the potential of AI is undeniable, Ros acknowledged the key mistakes businesses often make when deploying AI solutions, emphasising the importance of having a robust data strategy, building adequate data pipelines, and thoroughly testing the models. He also cautioned against rushing to deploy generative AI solutions without properly assessing feasibility and business viability, stating, “We need to pay at least as much attention to whether it should be built as we do to whether it can be built.”

Recognising the critical concern of ethical AI development, Ros stressed the significance of human oversight throughout the entire process. “Managing dynamic data quality, testing and detecting for bias and inaccuracies, ensuring high standards of data privacy, and ethical use of AI systems all require human oversight,” he said. SoftServe’s approach to AI development involves structured engagements that evaluate data and algorithms for suitability, assess potential risks, and implement governance measures to ensure accountability and data traceability.

Looking ahead, Ros envisions AI playing an increasingly vital role in SoftServe’s business strategy, with ongoing refinements to AI-assisted software development lifecycles and the introduction of new tools and processes to boost productivity further. Softserve’s findings suggest that GenAI can accelerate programming productivity by as much as 40 percent.

“I see more models assisting us on a daily basis, helping us write emails and documentation and helping us more and more with the simple, time-consuming mundane tasks we still do,” Ros said. “In the next five years I see ongoing refinement of that view to AI in SDLCs and the regular introduction of new tools, new models, new processes that push that 40 percent productivity hike to 50 percent and 60 percent.”

When asked how SoftServe is leveraging AI for social good, Ros explained the company is delivering solutions ranging from machine learning models to help students discover their passions and aptitudes, enabling personalised learning experiences, to assisting teachers in their daily tasks and making their jobs easier.

“I love this question because one of SoftServe’s key strategic tenets is to power our social purpose and make the world a better place. It’s obviously an ambitious goal, but it’s important to our employees and it’s important to our clients,” explained Ros.

“It’s why we created the Open Eyes Foundation and have collected more than $15 million with the support of the public, our clients, our partners, and of course our employees. We naturally support the Open Eyes Foundation with all manner of technology needs, including AI.”

At the AI & Big Data Expo North America, SoftServe plans to host a keynote presentation titled “Revolutionizing Learning: Unleashing the Power of Generative AI in Education and Beyond,” which will explore the transformative impact of generative AI and large language models in the education sector.

“As we explore the mechanisms through which generative AI leverages data – including training methodologies like fine-tuning and Retrieval Augmented Generation (RAG) – we will pinpoint high-value, low-risk applications that promise to redefine the educational landscape,” said Ros.

“The journey from a nascent idea to a fully operational AI solution is fraught with challenges, including ethical considerations and risks inherent in deploying AI solutions. Through the lens of a success story at Mesquite ISD, where generative AI was leveraged to help students uncover their passions and aptitudes enabling the delivery of personalised learning experiences, this presentation will illustrate the practical benefits and transformative potential of generative AI in education.”

Additionally, the company will participate in panel discussions on topics such as “Getting to Production-Ready – Challenges and Best Practices for Deploying AI” and “Navigating the Data & AI Landscape – Ensuring Safety, Security, and Responsibility in Big Data and AI Systems.” These sessions will provide attendees with valuable insights from SoftServe’s experts on overcoming deployment challenges, ensuring data quality and user acceptance, and mitigating risks associated with AI implementation.

As a key sponsor of the event, SoftServe aims to contribute to the discourse surrounding the responsible and ethical development of AI solutions, while sharing its expertise and vision for leveraging this powerful technology to drive innovation, enhance productivity, and address global challenges. 

“We are, of course, always interested in both sharing and hearing about the diversity of business cases for applications in AI and big data: the concept of the rising tide lifting all boats is definitely relevant in AI and GenAI in particular, and we’re proud to be a part of the AI technology community,” Ros concludes.

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.

Explore other upcoming enterprise technology events and webinars powered by TechForge here.

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Sure. We’re a 30-year-old global IT services and professional services provider. We specialise in using emerging state-of-the-art technologies, such as artificial intelligence, big data and blockchain, to solve real business problems. We’re highly obsessed with our customers, about their problems – not about technologies – although we are technology experts. But we always try to find the best technology that will help our customers get to the point where they want to be. 

So we’ve been in the market for quite a while, having originated in Ukraine. But now we have offices all over the globe – US, Latin America, Singapore, Middle East, all over Europe – and we operate in multiple industries. We have some specialised leadership around specific industries, such as retail, financial services, healthcare, energy, oil and gas, and manufacturing. We also work with a lot of digital natives and independent software vendors, helping them adopt this technology in their products, so that they can better serve their customers.

What are the main trends you’ve noticed developing in AI and machine learning?

One of the biggest trends is that, while people used to question whether AI, machine learning and data science are the technologies of the future; that’s no longer the question. This technology is already everywhere. And the vast majority of the innovation that we see right now wouldn’t have been possible without these technologies. 

One of the main reasons is that this tech allows us to address and solve some of the problems that we used to consider intractable. Think of natural language, image recognition or code generation, which are not only hard to solve, they’re also hard to define. And approaching these types of problems with our traditional engineering mindset – where we essentially use programming languages – is just impossible. Instead, we leverage the knowledge stored in the vast amounts of data we collect, and use it to find solutions to the problems we care about. This approach is now called Machine Learning, and it is the most efficient way to address those types of problems nowadays.

But with the amount of data we can now collect, the compute power available in the cloud, the efficiency of training and the algorithms that we’ve developed, we are able to get to the stage where we can get superhuman performance with many tasks that we used to think only humans could perform. We must admit that human intelligence is limited in capacity and ability to process information. And machines can augment our intelligence and help us more efficiently solve problems that our brains were not designed for.

The overall trend that we see now is that machine learning and AI are essentially becoming the industry standard for solving complex problems that require knowledge, computation, perception, reasoning and decision-making. And we see that in many industries, including healthcare, finance and retail.

There are some more specific emerging trends. The topic of my TechEx North America keynote will be about generative AI, which many folk might think is something just recently invented, something new, or they may think of it as just ChatGPT. But these technologies have been evolving for a while. And we, as hands-on practitioners in the industry, have been working with this technology for quite a while. 

What has changed now is that, based on the knowledge and experience we’ve collected, we were able to get this tech to a stage where GenAI models are useful. We can use it to solve some real problems across different industries, from concise document summaries to advanced user experiences, logical reasoning and even the generation of unique knowledge. That said, there are still some challenges with reliability, and understanding the actual potential of these technologies.

How important are AI and machine learning with regards to product innovation?

AI and Machine Learning essentially allow us to address the set of problems that we can’t solve with traditional technology. If you want to innovate, if you want to get the most out of tech, you have to use them. There’s no other choice. It’s a powerful tool for product development, to introduce new features, for improving customer user experiences, for deriving some really deep actionable insights from the data. 

But, at the same time, it’s quite complex technology. There’s quite a lot of expertise involved in applying this tech, training these types of models, evaluating them, deciding what model architecture to use, etc. And, moreover, they’re highly experiment driven, meaning that in traditional software development we often know in advance what to achieve. So we set some specific requirements, and then we write a source code to meet those requirements. 

And that’s primarily because, in traditional engineering, it’s the source code that defines the behaviour of our system. With machine learning and artificial intelligence the behaviour is defined by the data, which means that we hardly ever know in advance what the quality of our data is. What’s the predictive power of our data? What kind of data do we need to use? Whether the data that we collected is enough, or whether we need to collect more data. That’s why we always need to experiment first. 

But I think, in some way, we got used to the uncertainty in the process and the outcomes of AI initiatives. The AI industry gave up on the idea that machine learning will be predictable at some point. Instead, we learned how to experiment efficiently, turning our ideas into hypotheses that we can quickly validate via experimentation and rapid prototyping, and evolving the most successful experiments into full-fledged products. That’s essentially what the modern lifecycle of AI/ML products looks like.

It also requires the product teams to adopt a different mindset of constant ideation and experimentation, though. It starts with selecting those ideas and use cases that have the highest potential, the most feasible ones that may have the biggest impact on the business and the product. From there, the team can ideate around potential solutions, quickly prototyping and selecting those that are most successful. That requires experience in identifying the problems that can benefit from AI/ML the most, and agile, iterative processes of validating and scaling the ideas.

How can businesses use that type of technology to improve personalisation?

That’s a good question because, again, there are some problems that are really hard to define. Personalisation is one of them. What makes me or you a person? What contributes to that? Whether it’s our preferences. How do we define our preferences? They might be stochastic, they might be contextual. It’s a highly multi dimensional problem. 

And, although you can try to approach it with a more traditional tech, you’ll still be limited in that capacity – depths of personalisation that you may get. The most efficient way is to learn those personal signals, preferences from the data, and use those insights to deliver personalised experiences, personalised marketing, and so on. 

Essentially, AI/ML acts as a sort of black box between the signal and the user and specific preferences, specific content that would resonate with that specific user. As of right now, that’s the most efficient way to achieve personalisation. 

One other benefit of modern AI/ML is that you can use various different types of data. You can combine clickstream data from your website, collecting information about how users behave on your website. You can collect text data from Twitter or any other sources. You can collect imagery data, and you can use all that information to derive the insights you care about. So the ability to analyse that heterogeneous set of data is another benefit that AI/ML brings into this game.

How do you think machine learning is impacting the metaverse and how are businesses benefiting from that?

There are two different aspects. ‘Metaverse’ is quite an abstract term, and we used to think of it from two different perspectives. One of them is that you want to replicate your physical assets – part of our physical world in the metaverse. And, of course, you can try to approach it from a traditional engineering standpoint, but many of the processes that we have are just too complex. It’s really hard to replicate them in a digital world. So think of a modern production line in manufacturing. In order for you to have a really precise, let’s call it a digital twin, of some physical assets, you have to be smart and use something that will allow you to get as close as possible in your metaverse to the physical world. And AI/ML is the way to go. It’s one of the most efficient ways to achieve that.

Another aspect of the metaverse is that since it’s digital, it’s unlimited. Thus, we may also want to have some specific types of assets that are purely digital, that don’t have any representation in the real world. And those assets should have similar qualities and behaviour as the real ones, handling a similar level of complexity. In order to program these smart, purely digital processes or assets, you need AI and ML to make them really intelligent.

Are there any examples of companies that you think have been utlising AI and machine learning well?

There are the three giants – Facebook, Google, Amazon. All of them are essentially a key driver behind the industry. And the vast majority of their products are, in some way, powered by AI/ML. Quite a lot has changed since I started my career but, even when I joined SoftServe around 10 years ago, there was a lot of research going on into AI/ML. 

There were some big players using the technology, but the vast majority of the market were just exploring this space. Most of our customers didn’t know anything about it. Some of the first questions they had were ‘can you educate us on this? What is AI/ML? How can we use it?’ 

What has changed now is that almost any company we interact with has already done some AI/ML work, whether they build something internally or they use some AI/ML products. So the perception has changed.

The overall adoption of this technology now is at the scale where you can find some aspects of AI/ML in almost any company.

You may see a company that does a lot of AI/ML on their, let’s say, marketing or distribution, but they have some old school legacy technologies in their production site or in their supply chain. The level of AI/ML adoption may differ across different lines of business. But I think almost everyone is using it now. Even your phone, it’s backed with AI/ML features. So it’s hard to think of a company that doesn’t use any AI/ML right now.

Do you think, in general, companies are using AI and machine learning well? What kind of challenges do they have when they implement it?

That’s a good question. The main challenge of applying these technologies today is not how to be successful with this tech, but rather how to be efficient. With the amount of data that we have now, and data that the companies are collecting, plus the amount of tech that is open source or publicly available – or available as managed services from AWS, from GCP – it’s easy to get some good results.

The question is, how do you decide where to apply this technology? How efficiently can you identify those opportunities, and find the ones that will bring the biggest impact, and can be implemented in the most time-efficient and cost-effective manner? 

Another aspect is how do you quickly turn those ideas into production-grade products? It’s a highly experiment-driven area, and there is a lot of science, but you still need to build reliable software on the research results. 

The key drivers for successful AI adoption are finding the right use cases where you can actually get the desired outcomes in the most efficient way, and turn ideas into full-fledged products. We’ve seen some really innovative companies that had brilliant ideas. They may have built some proof of concepts around their ideas, but they didn’t know how to evolve or how to build reliable products out of it. At the same time, there are some technically savvy and digitally native companies. They have tonnes of smart engineers, but they don’t have the right expertise and experience in AI/ML technologies. They don’t know how to apply this tech to real business problems, or what low-hanging fruits are available to them. They just struggle with finding the best way to leverage this tech.

What do you think the future holds for AI and machine learning?

I generally try to be more optimistic about the future because there are obviously a lot of fears around AI/ML. And I think that’s quite natural. If you look back in history, it was the same with electricity and any other innovative technologies.

One of the fears that I think does have some merit is that this technology may replace some real jobs. I think that’s a bit of a pessimistic view because history also teaches us that whatever technology we get, we still need that human aspect to it. 

Almost all the technology that we use right now augments our intelligence. It does not replace it. And I think that the future of AI will be used in a cooperative way. If you’ve seen products like GitHub Copilot, the purpose of this product is essentially to assist the developer in writing code. We still can’t use AI to write entire programs. We need a human to guide that AI to our desired outcome. What exactly do we want to achieve? What is our objective? What is our user expectation?

Similarly, maybe this technology will be applied to a broader set of use cases where AI will be assisting us, not replacing us. There is a quote that I wish was mine but I still think it’s a very good way of thinking about the role of AI: if you think that AI will replace you or your job, most likely you’re wrong. It’s the people who will be using AI who will replace you at your job. 

So I think one of the most important skills to learn right now is how to leverage this tech to make your work more efficient. And that should help many people get that competitive advantage in the future.

• Iurii Milovanov is the director of AI and data science at SoftServe, a technology company specialising in consultancy services and software development. 

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The event is co-located with Digital Transformation Week.

Explore other upcoming enterprise technology events and webinars powered by TechForge here.

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AI News: What work do you do in the artificial intelligence space? 

Sebastian Santibanez: We understand that the truly successful data-minded organizations are very fluid in their definition of AI and SoftServe has embraced this fluidity by thinking of AI organizations and solutions as those who touch, even transversally, on ML, big data, XR, IoT, robotics and many other advanced technologies. With that said, our AI work spans the full business cycle, from strategic digital consulting to solution design and build to maintenance. Depending on the maturity level of our clients we support them in different ways:

• Clients who are at the beginning of their digital journey get more value when we work together revealing the possibilities of technology. Especially now that a larger share of a business value is linked to certain AI initiatives, our clients trust us for designing a sound digital strategy around their AI-related goals, and conversely, ensuring that their AI dreams advance well anchored to a digital strategy. We see companies who what to start building AI projects before they have a sound strategy and sometimes help them step back and reframe their strategy before going forwards too quickly. Often, building PoCs are part of finding the strategy

• Clients who have taken their first steps already in their digital journey often see more value when SoftServe helps drive their transformation. In this area, we do a lot of work with our clients accelerating their innovation and IP generation; as well as developing their raw ideas into market-ready AI solutions. 

• Clients who are more digitally savvy often engage us to accelerate and optimize their AI-backed initiatives. We normally see extremely valuable market solutions that were created in a semi-artisanal fashion and of course are very hard to optimize and maintain effectively.  This is where our experience in Cloud-AI and XOPs really shines as we are able to transform good AI ideas into well-tuned production machines.

From a technical and organizational point of view, we support our clients with our Centers of Excellence in data science, big data analytics, IoT, XR, robotics, and cybersecurity, in addition to our in-house R&D department and our vast organizational experience in cloud, DevOps, and general software development. We’re prime partners of all major cloud providers and just got awarded Google Cloud Partner of the Year in Machine Learning, we have 10k associates around the world with a very well-established presence in Europe and North America, and a fast-growing presence in the Middle East, Latin America, and Asia.

Transversally, we are known in the market for our obsession with driving measurable solutions. Long ago, we collectively realized that many clients were really struggling with identifying the value potential of their current AI initiatives, or designing AI solutions that drove measurable value, which of course was hurting their stance in front of shareholders and leadership.

Our work in AI and associated technologies goes very deeply into identifying the actual business value of the solutions we design and find ways to effectively measure and communicate the outputs of our clients’ AI initiatives. Historically, clients have been tempted to measure the outcome of their AI solutions in terms of cost savings and revenue increase, which is of course important but certainly not the only metrics that matter.

AI has the tremendous potential of driving a competitive edge by accelerating speed to value when correctly aligned with an organization’s digital journey. We make sure that our clients develop their business with these goals in mind.

AN: What are the latest trends you’ve noticed developing in artificial intelligence and how do you think this will impact businesses and society in general? 

SS: Over the last few years, we have seen a transition in the market from a one-off experimental AI mindset to a more intentional, mature, and business-centred approach to data-backed solutions, which is likely fuelled by the availability of empirical data on what makes AI organizations successful.

Organizations are finding the right recipes to delight their customers and increase loyalty with AI and are investing in the right things: strengthening their data management tools and practices; improving (or even initiating) their data governance programs, better aligning AI initiatives to strategy, and creating a more AI-friendly culture.

We have no doubt that this paradigmatic shift is positive for businesses and for us and our communities. This mature, business-centred approach to AI means that a larger number of optimal solutions will reach the market and will positively affect the lives of billions.

As consumers, we will enjoy access to higher quality, cheaper goods and services which are optimized with AI, and as members of our communities we might see that our essential services such as public transportation, infrastructure or health also become more efficient and affordable for all which, of course, has the added value of reducing the negative impacts of our lifestyles in our planet.

From a more technical point of view, we’re seeing rising expectations of how AI and related technologies like robotics and XR can benefit organizations. Take the case of manufacturers as an example; more of them are accelerating their transition from reactive maintenance to predictive maintenance informed by IoT-Big Data-AI combination, and more of them are also evolving their sample-based quality controls to 100% sample methodologies assisted by computer vision, XR, edge computing, and other technologies.

These new expectations add a burden to AI-adjacent technologies, like IoT or MLOPs because they demand the enablement of heavy workloads at the edge and continuous development and management of algorithms to satisfy very fastly evolving needs, which in turn requires complex containerization and orchestration of physical resources and code across the globe. The industry is, in general, responding well to this challenge and we’re observing a mindset change from creating siloed solutions that are conceived with a focus on one part of the value chain, to a mindset that values convergence of technologies along the value chain.

Clients are also sunsetting their Hadoop clusters and switching back to SQL-based solutions like cloud-native warehouses and distributed query engines, which tremendously help to streamline the cloud-native AI lifecycle. We’re also constantly hearing about the desire to virtualize processes, which is something that Digital Twins, Simulations, reinforcement learning and other data science methods along with sensorization is enabling.

Organizations are using or gearing towards using this virtualization to analyse a variety of scenarios in the safety of the cloud and optimize their real-world operations; not only operations of their physical assets and systems of course, but also process optimization via process twinning, which helps organizations optimize their business workflows. Clients have seen the first wave of successful projects in these areas in the past years and are much more comfortable in investing in these solutions.

If this rising of expectations keeps coming informed by empirical evidence and within the goldilocks zone of the art of the possible, I think the implications for businesses and society in general are going to be very positive. The call to action however is to be very careful in identifying which expectations are rooted in solid evidence and which expectations need to be treated as pie in the sky. Both have their place and need to exist to have a healthy AI market but we can’t let the audience confuse both.

Another aspect we are also starting to see, even if just more recently and not forming a critical mass yet, is an increased awareness on security issues, fairness and explainability in AI.  Executives are starting to understand how fragile some AI solutions can be to attacks that manipulate data in order to change an AI result and are designing their solutions with that added layer of robustness in mind.

Curiously enough, this security awareness seems to have started unidirectionally, from the AI layer towards the data-generating layers, but it hasn’t yet reached the data-generating end of the AI lifecycle; there is still a lot of work to do in the industry so the numerous sensorization efforts are as secure as the cloud workloads.

On the fairness and explainable AI front, policymakers and technologists are coming to terms with some societal implications of trusting AI to make decisions that directly affect people. We are seeing more social actors asking the right questions of “what criteria is this algorithm using to decide on X or Y”, and at the same time, technologists are starting to promote more and more the use of explainable AI models.

As a matter of fact, only in the last year or so, the three largest cloud providers are joining the efforts initiated by IBM a few years back in promoting explainable and fair AI tools. Again, the business and societal implications of these aspects are in general very positive. Safer workloads and transparent analytics mean that life-impacting decisions can be well informed by AI, which is of course in everyone’s best interest. The big caveat will be making sure that technologists and policymakers work together in ensuring that we are able to secure the whole data pipeline, from collection to analytics

AN: The company recently became an advisor and technology partner on UNICEF Ukraine. What does this partnership entail and why did you choose to partner with UNICEF?

SS: We are expanding our strategic partnership with UNICEF Ukraine through 2023. SoftServe will now serve as an advisor and technology partner on UNICEF Ukraine’s projects working toward the goals of sustainable development for children. We have outlined opportunities for cooperation in software development and other activities to support UNICEF programs in Ukraine in education, health, child protection, social policy, communication for development, and others.

Our partnership with UNICEF Ukraine began in April 2020. To date, we have implemented numerous initiatives, including a platform for collecting and analyzing COVID-19 statistics in Ukraine, the launch of the country’s National Volunteer Platform, a web portal dedicated to reforming Ukraine’s school nutrition system, an infant care app for young parents, and an evidence-based medicine website. In 2021, SoftServe will also work on updating the national vaccination portal.

UNICEF’S projects in Ukraine systematically address social issues in child protection. The goal of these initiatives – to enable talented people to change the world – aligns perfectly with SoftServe’s mission.

AN: The company has also become an official member of the United Nations (UN) Global Compact. What do you hope to achieve as part of the Global Compact?

SS: It’s an opportunity for us to become part of the global movement of companies that are changing the world for the better and it’s a new step for us in creating a sustainable business. We are committed to the UN Global Compact initiative and its principles in the areas of human rights, labour, the environment, and anti-corruption. 

Our cooperation with the UN began in 2019. We participated in the ‘Hack for Locals’ hackathon that aimed to develop creative digital solutions to solve problems in local communities.

This year, we joined ‘Co-create with Locals’, the pilot program for the United Nations Development Programme (UNDP), which aims to engage activists in developing innovative solutions in public safety and social cohesion and will be implemented on SoftServe’s Innovation Platform.

AN: Finally, what other notable latest developments have there been recently at SoftServe?

SS: SoftServe surpassed ten thousand employees, a significant milestone, as of July 2021. Our headcount has grown by 26% since the beginning of the year thanks to the growing demand for digital services and an expanding customer base.

SoftServe also won the 2020 Google Cloud Global Specialization Partner of the Year – Machine Learning award.

Finally, SoftServe appointed Adriyan Pavlykevych as Chief Information Security Officer (CISO) as of June 2021. Pavlykevych has almost 20 years of experience with SoftServe. As CISO, he will be responsible for shaping and implementing SoftServe’s information governance and security strategy, including ensuring the secure delivery of the company’s engineering services and maintaining and developing its cyber defense capabilities.

(Photo by Cytonn Photography on Unsplash)

Santibanez will be sharing his invaluable insights during this year’s AI & Big Data Expo Global, which runs from 6-7 September 2021. Find out more about his sessions and how to attend here.

The post Sebastian Santibanez, SoftServe: On helping enterprises successfully use AI in their digital transformations appeared first on AI News.