AI News spoke with Sebastian Santibanez, Associate Director of the Advanced Technologies Group at SoftServe, about how the company is helping enterprises to successfully use AI in their digital transformations.
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.
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.