We spoke to Kay to discuss the promise and pitfalls of generative AI, the future of the Metaverse, and how organisations can prepare for a decade of unprecedented digital transformation.

Generative AI has captured global attention, but there’s still a great deal of misunderstanding around what it actually is. Could you walk us through what defines generative AI, how it works, and why it’s considered such a transformative evolution of artificial intelligence?

It’s very exciting because it represents the next iteration of artificial intelligence. What generative AI allows you to do is ask questions of the world’s data simply by typing a prompt. If we think back to science fiction, that’s essentially what we’ve always dreamed of — just being able to ask a computer a question and have it draw on all its knowledge to provide an answer.

How does it do that? Well, it predicts which word is likely to come next in a sequence. It does this by accessing enormous volumes of data. We refer to these as large language models. Essentially, the machine ‘reads’ — or at least accesses — all the data available on the open web. In some cases, and this is an area of legal contention, it also accesses IP-protected and copyrighted material. We can expect a great deal of legal debate in this space.

Once the model has ingested all this data, it begins to predict what word naturally follows another, enabling it to construct highly complex and nuanced responses. Anyone who has experimented with it knows that it can return some surprisingly eloquent and insightful content simply through this predictive capability.

Of course, sometimes it gets things wrong. In the AI community, we call this ‘hallucination’ — essentially, the system fabricates information. That’s a serious issue because in order to rely on AI-generated outputs, we need to reach a point where we can trust the responses. The problem is, once a hallucination enters the data pool, it can be repeated and reinforced by the model.

While much has been said about generative AI’s technical potential, what do you see as the most meaningful societal and business benefits it offers? And what challenges must we address to ensure these advantages are equitably realised?

AI is now accessible to everyone, and that’s incredibly powerful. It’s a hugely democratising tool. It means that small and medium-sized enterprises, which previously couldn’t afford to leverage AI, now can.

However, we also need to be aware that most of the world’s data is created in the United States first, followed by Europe and China. There are clear challenges regarding the datasets these large language models are trained on. They’re not truly using ‘global’ data. They’re working with a limited subset. That has led to discussions around digital colonisation, where content generated from American and European data is projected onto the rest of the world, with an implicit expectation that others will adopt and use it.

Different cultures, of course, require different responses. So, while there are countless benefits to generative AI, there are also significant challenges that we must address if we want to ensure fair and inclusive outcomes.

The Metaverse has seen both hype and hesitation in recent years. From your perspective, what is the current trajectory of the Metaverse, and how do you see its role evolving within business environments over the next five years?

It’s interesting. We went through a phase of huge excitement around the Metaverse, where everyone wanted to be involved. But now we’ve entered more of a Metaverse winter, or perhaps autumn, as it’s become clear just how difficult it is to create compelling content for these immersive spaces.

We’re seeing strong use cases in industrial applications, but we’re still far from achieving that Ready Player One vision — where we live, shop, buy property, and fully interact in 3D virtual environments. That’s largely because the level of compute power and creative resources needed to build truly immersive experiences is enormous.

In five years’ time, I think we’ll start to see the Metaverse delivering on more of its promises for business. Customers may enjoy exceptional shopping experiences—entering virtual stores rather than simply browsing online, where they can ‘feel’ fabrics virtually and make informed decisions in real time.

We may also see remote working evolve, where employees collaborate inside the Metaverse as if they were in the same room. One study found that younger workers often lack adequate supervision when working remotely. In a Metaverse setting, you could offer genuine, interactive supervision and mentorship. It may also help with fostering colleague relationships that are often missed in remote work settings.

Ultimately, the Metaverse removes physical constraints and offers new ways of working and interacting—but we’ll need balance. Many people may not want to spend all their time in fully immersive environments.

Looking ahead, which emerging technologies and AI-driven trends do you anticipate will have the most profound global impact over the next decade. And how should we be preparing for their implications, both economically and ethically?

That’s a great question. It’s a bit like pulling out a crystal ball. But without doubt, generative AI is one of the most significant shifts we’re seeing today. As the technology becomes more refined, it will increasingly power new AI applications through natural language interactions.

Natural Language Processing (NLP) is the AI term for the machine’s ability to understand and interpret human language. In the near future, only elite developers will need to code manually. The rest of us will interact with machines by typing or speaking requests. These systems will not only provide answers, but also write code on our behalf. It’s incredibly powerful, transformative technology.

But there are downsides. One major concern is that AI sometimes fabricates information. And as generative AI becomes more prolific, it’s generating massive volumes of data 24/7. Over time, machine-generated data may outnumber human data, which could distort the digital landscape. We must ensure the AI doesn’t perpetuate falsehoods it has previously generated.

Looking further ahead, this shift raises deep questions about the future of human work. If AI systems can outperform humans in many tasks without fatigue, what becomes of our role? There may be cost savings, but also the very real risk of widespread unemployment.

AI also powers the Metaverse, so progress there is tied to improvements in AI capabilities. I’m also very excited about synthetic biology, which could see huge advancements driven by AI. There’s also likely to be significant interplay between quantum computing and AI, which could bring both benefits and serious challenges.

We’ll see more Internet of Things (IoT) devices as well—but that introduces new issues around security and data protection.

It’s a time of extraordinary opportunity, but also serious risks. Some worry about artificial general intelligence becoming sentient, but I don’t see that as likely just yet. Current models lack causal reasoning. They’re still predictive tools. We would need to add something fundamentally different to reach human-level intelligence. But make no mistake—we are entering an incredibly exciting era.

Adopting new technologies can be both an opportunity and a risk for businesses. In your view, how can organisations strike the right balance between embracing digital transformation and making strategic, informed decisions about AI adoption?

I think it’s vital to adopt the latest technologies, just as it would have been important for Kodak to see the shift coming in the photography industry. Businesses that fail to even explore digital transformation risk being left behind.

However, a word of caution: it’s easy to jump in too quickly and end up with the wrong AI solution — or the wrong systems entirely — for your business. So, I would advise approaching digital transformation with careful thought. Keep your eyes open, and treat each step as a deliberate, strategic business decision.

When you decide that you’re ready to adopt AI, it’s crucial to hold your suppliers to account. Ask the hard questions. Ask detailed questions. Make sure you have someone in-house, or bring in a consultant, who knows enough to help you interrogate the technology properly.

As we all know, one of the greatest wastes of money in digital transformation happens when the right questions aren’t asked up front. Getting it wrong can be incredibly costly, so take the time to get it right.

Photo by petr sidorov on Unsplash

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 Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.

The post Kay Firth-Butterfield, formerly WEF: The future of AI, the metaverse and digital transformation appeared first on AI News.

“2025 is shaping up to be a defining year for AI, as organisations transition from experimentation to scaling their AI capabilities,” said Gabie Boko, NetApp’s Chief Marketing Officer.

“Businesses are making significant investments to drive innovation and efficiency, but these efforts will succeed only if global tech executives can address the mounting challenges of data complexity, security, and sustainability.”

The findings of NetApp’s latest Data Complexity Report paints a detailed picture of where businesses currently stand on their AI journeys and the key trends that will shape the technology’s future.

Cost of transformation

Two-thirds of businesses worldwide claim their data is “fully or mostly optimised” for AI purposes, highlighting vast improvements in making data accessible, accurate, and well-documented. Yet, the study reveals that the journey towards AI maturity requires further significant investment.

A striking 40% of global technology executives anticipate “unprecedented investment” will be necessary in 2025 just to enhance AI and data management capabilities.

While considerable progress has been made, achieving impactful breakthroughs demands an even greater commitment in financial and infrastructural resources. Catching up with AI’s potential might not come cheap, but leaders prepared to invest could reap significant rewards in innovation and efficiency.

Data silos impede AI success

One of the principal barriers identified in the report is the fragmentation of data. An overwhelming 79% of global tech executives state that unifying their data, reducing silos and ensuring smooth interconnectedness, is key to unlocking AI’s full potential.

Companies that have embraced unified data storage are better placed to overcome this hurdle. By connecting data regardless of its type or location (across hybrid multi-cloud environments,) they ensure constant accessibility and minimise fragmentation.

The report indicates that organisations prioritising data unification are significantly more likely to meet their AI goals in 2025. Nearly one-third (30%) of businesses failing to prioritise unification foresee missing their targets, compared to just 23% for those placing this at the heart of their strategy.

Executives have doubled down on data management and infrastructure as top priorities, increasingly recognising that optimising their capacity to gather, store, and process information is essential for AI maturity. Companies refusing to tackle these data challenges risk falling behind in an intensely competitive global market.

Scaling risks of AI

As businesses accelerate their AI adoption, the associated risks – particularly around security – are becoming more acute. More than two-fifths (41%) of global tech executives predict a stark rise in security threats by 2025 as AI becomes integral to more facets of their operations.

AI’s rapid rise has expanded attack surfaces, exposing data sets to new vulnerabilities and creating unique challenges such as protecting sensitive AI models. Countries leading the AI race, including India, the US, and Japan, are nearly twice as likely to encounter escalating security concerns compared to less AI-advanced nations like Germany, France, and Spain.

Increased awareness of AI-driven security challenges is reflected in business priorities. Over half (59%) of global executives name cybersecurity as one of the top stressors confronting organisations today.

However, progress is being made. Despite elevated concerns, the report suggests that effective security measures are yielding results. Since 2023, the number of executives ranking cybersecurity and ransomware protection as their top priority has fallen by 17%, signalling optimism in combating these risks effectively.

Limiting AI’s environmental costs

Beyond security risks, AI’s growth is raising urgent questions of sustainability. Over one-third of global technology executives (34%) predict that AI advancements will drive significant changes to corporate sustainability practices. Meanwhile, 33% foresee new government policies and investments targeting energy usage.

The infrastructure powering AI and transforming raw data into business value demands significant energy, counteracting organisational sustainability targets. AI-heavy nations often feel the environmental impact more acutely than their less AI-focused counterparts.

While 72% of businesses still prioritise carbon footprint reduction, the report notes a decline from 84% in 2023, pointing to increasing tension between sustainability commitments and the relentless march of innovation. For organisations to scale AI without causing irreparable damage to the planet, maintaining environmental responsibility alongside technological growth will be paramount in coming years.

Krish Vitaldevara, SVP and GM at NetApp, commented: “The organisations leading in advanced analytics and AI are those that have unified and well-cataloged data, robust security and compliance for sensitive information, and a clear understanding of how data evolves.

“By tackling these challenges, they can drive innovation while ensuring resilience, responsibility, and timely insights in the new AI era.”

You can find a full copy of NetApp’s report here (PDF)

(Photo by Chunli Ju)

See also: New AI training techniques aim to overcome current challenges

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 Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.

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

The post Keys to AI success: Security, sustainability, and overcoming silos appeared first on AI News.

According to analytics firm GlobalData, Walmart is focusing on augmented reality and AI-enhanced store management. Amazon, meanwhile, is leading advancements in customer personalisation and autonomous systems.

Kiran Raj, Practice Head of Disruptive Tech at GlobalData, notes: “Walmart and Amazon are no longer competing for market share alone. Their AI strategies are reshaping the entire retail ecosystem—from Walmart’s blend of digital and physical shopping experiences to Amazon’s operational automation.”

GlobalData’s Disruptor Intelligence Center, utilising its Technology Foresights tool, has identified the strategic focus of these retail titans based on their patent filings.

Walmart has submitted over 3,000 AI-related patents, with 20% of these in the last three years, indicating a swift evolution in its AI capabilities. In contrast, Amazon boasts more than 9,000 patents; half of which were filed during the same timeframe, underpinning its leadership in AI-driven retail innovations.

AI-powered retail transformation

Walmart is deploying AI-driven solutions like in-store product recognition while making notable strides in AR applications, including virtual try-ons. The company’s progress in smart warehouses and image-based transactions denotes a shift towards fully automated retail, enhancing both speed and precision in customer service.

Amazon stands out with its extensive deployment of AI in customer personalisation and autonomous systems. By harnessing technologies such as Autonomous Network Virtualisation and Automated VNF Deployment, the company is advancing its operational infrastructure and aiming to set new standards in network efficiency and data management.

Walmart’s development of intelligent voice assistants and automated store surveillance emphasises its aim to provide a seamless and secure shopping experience. Concurrently, Amazon’s progress in AI for coding and surveillance is pushing the boundaries of enterprise AI applications and enhancing security capabilities.

“Walmart and Amazon’s aggressive innovation strategies not only strengthen their market positions but also set a blueprint for the future of the retail sector,” Raj explains.

“As these two giants continue to push the boundaries of retail AI, the broader industry can expect ripple effects in supply chain innovation, customer loyalty programmes, and operational scalability—setting the stage for a new era of consumer engagement.”

(Photo by Marques Thomas)

See also: Whitepaper dispels fears of AI-induced job losses

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 Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.

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

The post Walmart and Amazon drive retail transformation with AI appeared first on AI News.

From addressing the spectrum of use cases to navigating digital transformation, Shakir shed light on the challenges, ethical considerations, and the promising future of this groundbreaking technology.

Wide spectrum of use cases

Shakir highlighted the wide array of GenAI applications, ranging from productivity enhancements and research support to high-stakes areas such as strategic data mining and knowledge bots. She emphasised the transformational power of AI in understanding customer data, moving beyond simple sentiment analysis to providing actionable insights, thus elevating customer engagement strategies.

“GenAI now can take your customer insights to another level. It doesn’t just tell you whether something’s a positive or negative sentiment like old AI would do, it now says it’s positive or negative. It’s negative because X, Y, Z, and here’s the root cause for X, Y, Z,” explains Shakir.

Powering digital transformation

Gate One adopts an adaptive strategy approach, abandoning traditional five-year strategies for more agile, adaptable frameworks.

“We have a framework – our 5P model – where it’s: identify your people, identify the problem statement that you’re trying to solve for, appoint some partnerships, think about what’s the right capability mix that you have, think about the pathway through which you’re going to deliver, be use case or risk-led, and then proof of concept,” says Shakir.

By solving specific challenges and aligning strategies with business objectives, Gate One aims to drive meaningful digital transformation for its clients.

Assessing client readiness

Shakir discussed Gate One’s diagnostic tools, which blend technology maturity and operating model innovation questions to assess a client’s readiness to adopt GenAI successfully.

“We have a proprietary tool that we’ve built, a diagnostic tool where we look at blending tech maturity capability type questions with operating model innovation questions,” explains Shakir.

By categorising clients as “vanguard” or “safe” players, Gate One tailors their approach to meet individual readiness levels—ensuring a seamless integration of GenAI into the client’s operations.

Key challenges and ethical considerations

Shakir acknowledged the challenges associated with GenAI, especially concerning the quality of model outputs. She stressed the importance of addressing biases, amplifications, and ethical concerns, calling for a more meaningful and sustainable implementation of AI.

“Poor quality data or poorly trained models can create biases, racism, sexism… those are the things that worry me about the technology,” says Shakir.

Gate One is actively working on refining models and data inputs to mitigate such problems.

The future of GenAI

Looking ahead, Shakir predicted a demand for more ethical AI practices from consumers and increased pressure on developers to create representative and unbiased models.

Shakir also envisioned a shift in work dynamics where AI liberates humans from mundane tasks to allow them to focus on solving significant global challenges, particularly in the realm of sustainability.

Later this month, Gate One will be attending and sponsoring this year’s AI & Big Data Expo Global. During the event, Gate One aims to share its ethos of meaningful AI and emphasise ethical and sustainable approaches.

Gate One will also be sharing with attendees GenAI’s impact on marketing and experience design, offering valuable insights into the changing landscape of customer interactions and brand experiences.

As businesses navigate the evolving landscape of GenAI, Gate One stands at the forefront, advocating for responsible, ethical, and sustainable practices and ensuring a brighter, more impactful future for businesses and society.

Umbar Shakir and the Gate One team will be sharing their invaluable insights at this year’s AI & Big Data Expo Global. Find out more about Umbar Shakir’s day one keynote presentation here.

The post Umbar Shakir, Gate One: Unlocking the power of generative AI ethically appeared first on AI News.

Glean, a firm established by Google search engineers and other industry veterans, possesses considerable expertise in this area.

AI News caught up with Arvind Jain, CEO and Founder of Glean, to hear more about how the company is using AI to surface workplace knowledge and supercharge productivity.

AI News: Can you tell us about Glean and its goals?

Arvind Jain: Glean is solving perhaps the most urgent problem in today’s workplace: helping people find and access the information they need to do their best work. 

Over the past four years, we’ve built a search platform that leverages the latest advancements in machine learning and retrains deep learning language models on each company’s specific knowledge base. In this way, we develop a deep understanding of context, language, behaviour, and relationships with others that are uniquely tuned to your workplace and adheres to your data governance policies: a trusted knowledge model.  

Our trusted knowledge model enables us to give users the most relevant and personalised answers to their queries and empowers them to stay connected not only to company knowledge but also to one another.

Our mission is to bring everyone the knowledge they need to make a difference in the world.

AN: Has more remote working post-pandemic increased the need for knowledge-sharing solutions like Glean?

AJ: Absolutely, and this is compounded by the explosion of SaaS applications across the enterprise. Company knowledge has become fragmented and siloed, which presents huge challenges for companies who care about employee experience — who want to ensure that their employees can find answers to their questions, access the information they need, and feel connected.

A Forrester found that the top reason employees feel disengaged from work is that data and/or information is hard to find. Now more than ever it’s absolutely critical to ensure that you invest in good search and knowledge management tools.

AN: How do you ensure that potentially sensitive company data that not all team members should maybe have access to is kept safe?

AJ: Glean was built from the ground up to prioritize security and governance — we connect to all your enterprise knowledge and enforce the existing permissions of your data sources. This has been foundational to our success.

Glean’s governance engine ensures that users only see the information they are allowed to, based on their existing access permissions in the source systems that Glean searches. 

AN: After a reasonable time of integrating Glean, what is the success rate of receiving an answer that someone needs?

AJ: The average Glean user makes 20 searches/day and saves 2-3 hours/week — customer time savings and productivity gains are key success metrics for us.

AN: How does Glean differentiate from its competitors?

AJ: Over the past four years, we’ve built a search platform that leverages the latest advancements in machine learning and retrains deep learning language models on each company’s specific knowledge base.

In this way, we develop a deep understanding of context, lexicon, behaviour, and relationships with others that are uniquely tuned to your workplace and adheres to your data governance policies: a trusted knowledge model. Our trusted knowledge model enables us to provide users with the most relevant and personalised answers to their queries. 

I was also frustrated by how long it took other tools to get up and running. It was very important to me that our search solution should be fully customisable, but also should only need minimal operational overhead to set up – no third-party engagements or professional services.

AN: Amid global economic uncertainties, have you noticed an uptick in interest from enterprises seeking ways to lower their costs and improve operational efficiencies?

AJ: Yes, 100 percent! The economy has simultaneously seen a drop in productivity and in employee engagement, and many businesses are looking to improve efficiency and productivity. 

There are many studies, including one from McKinsey, that have found that almost 20 percent of the work week is spent looking for internal information or colleagues who can help – this is why it’s so vital to empower employees with good tools to connect to company knowledge and resources. It’s a critical investment right now.

AN: What can we expect from Glean over the coming year?

AJ: Generative AI has the potential to supercharge knowledge workers, and everyone wants to figure out how to bring it into the workplace, but GPT-4 and similar Generative AI models are simply not ready for the enterprise.  They need to be grounded in the right search technology. 

Our goal is to deliver a product that’s as useful for the enterprise as ChatGPT is for the web.

With that goal in mind, we’re going to expand the use of generative AI in our offerings and deliver new features, grounded in our trusted knowledge model, that will augment people’s potential at work.

The conversational interface is just one piece of that. Our mission is to bring people the knowledge they need to make a difference in the world. In the background, we’re also continually working to improve ranking, it’s foundational to what we offer.

Critical aspects of managing SaaS environments will be explored at the free webinar, Navigating SaaS Management: Enhancing Security and Operational Efficiency. Hear from Calero, Marsh McLennan and Merck Group. Register today.

The post Arvind Jain, Glean: On using AI to surface knowledge appeared first on AI News.

NVIDIA named QuEST an Elite Service Delivery Partner in the NVIDIA Partner Network (NPN) back in June.

Through NPN, QuEST has early access to NVIDIA platforms, software, solutions, workshops, and technology updates. The previous agreement only covered the USA but NVIDIA has now extended the collaboration to Japan.

Masataka Osaki, Japan Country Manager and Vice President of Corporate Sales at NVIDIA, said:

“We are pleased to welcome QuEST as an NPN Elite Partner not only in the US, but also in Japan.

The NPN Elite-level status is reserved for partners who demonstrate a history of expertise in the areas of artificial intelligence, machine learning, and deep learning.

We hope that QuEST’s solutions and services, based on NVIDIA’s AI technology, will further boost the Japanese industry.”

QuEST has wasted no time in taking advantage of the benefits of being an NPN member.

Using NVIDIA DGX systems, QuEST has trained custom vision AI models that are deployed for high-speed edge inference. Customers are able to begin enhancing their operations and decision-making through rapid proof-of-concept deployments. 

Rajeev Nair, Vice President and Head of Japan Business, QuEST Global, commented:

“We are extremely proud that our NPN Elite partner status has been extended to Japan. QuEST is already engaged with key Japanese customers in high-tech, medical devices, power, and automotive domains providing engineering and digital services. 

The NPN partnership will help us further our efforts and provide the best to our customers in Japan.” 

NVIDIA and QuEST have established a deep relationship over the years. QuEST has been part of NVIDIA’s Jetson Partner Ecosystem since 2018 and was one of the first companies to be selected for the NVIDIA Deep Learning Consulting Partnership Program.

In 2019, QuEST debuted a groundbreaking solution to detect lung cancer nodules from CT scans. The solution uses the NVIDIA Jetson platform for deep neural network training and validation to develop models that enhance the accuracy of CT image analysis compared to conventional image processing methods.

“QuEST’s collaboration with NVIDIA in Japan will help accelerate AI-based digital transformation across our customers,” added Nair. “We look forward to working with NVIDIA to spur technology-driven business innovation and growth for customers across industries.”

(Photo by Jase Bloor on Unsplash)

Find out more about Digital Transformation Week North America, taking place on 9-10 November 2021, a virtual event and conference exploring advanced DTX strategies for a ‘digital everything’ world.

The post QuEST partners with NVIDIA to deliver next-gen AI solutions in Japan appeared first on AI News.

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.

Catching problems during production enables intervention before materials are wasted—incurring direct and environmental costs. It also saves on the reputational damage and costs associated with returns/recalls after a defective product is shipped to customers.

The solution uses an AI model trained on images of products with abnormalities. These defects are simulated so images of actual products with issues pulled from a production line aren’t necessary.

Fujitsu tested its technology at its Nagano Plant, which manufactures electronic equipment, and noted a 25 percent reduction in the man-hours needed for inspecting printed circuit boards.

The AI is able to detect issues like frayed threads or defective wiring patterns – with “world-leading accuracy” – in products that are designed to vary individually; such as different colour carpets or electronics parts with different wiring shapes.

Fujitsu’s AI achieved an AUROC (Area Under the Receiver Operating Characteristics) score in excess of 98 percent when applied to products with variations to their normal appearance.

The Japanese tech giant aims to use its AI advancement for the company’s COLMINA (PDF) brand which aims to deliver digital transformation specifically for the manufacturing industry.

(Photo by Clayton Cardinalli on Unsplash)

Interested in hearing industry leaders discuss subjects like this? Attend the co-located 5G Expo, IoT Tech Expo, Blockchain Expo, AI & Big Data Expo, and Cyber Security & Cloud Expo World Series with upcoming events in Silicon Valley, London, and Amsterdam.

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