Nicholas Brackney, Senior Consultant in Product Marketing at Dell, discussed the company’s AI initiatives ahead of AI & Big Data Expo North America.

Dell’s AI strategy is structured around four core principles: AI-In, AI-On, AI-For, and AI-With:

1. “Embedding AI capabilities in our offerings and services drives speed, intelligence, and automation,” Brackney explained. This ensures that AI is a fundamental component of Dell’s offerings.

2. The company also enables customers to run powerful AI workloads on its comprehensive portfolio of solutions, from desktops to data centres, across clouds, and at the edge.

3. AI innovation and tooling are applied for Dell’s business to enhance operations and share best practices with customers.

4. Finally, Dell collaborates with strategic partners within an open AI ecosystem to simplify and enhance the AI experience.

Dell is well-positioned to help customers navigate AI workloads, emphasising choice and adaptability through the various evolutions of emerging technology. Brackney highlighted Dell’s commitment to serving customers from the early stages of AI adoption to achieving AI at scale.

“We’ve always believed in providing choice and have been doing it through the various evolutions of emerging technology, including AI, and understanding the challenges that come with them,” explained Brackney. “We fully leverage our unique operating model to serve customers in the early innings of AI to a future of AI at scale.”

Looking to the future, Dell is particularly excited about the potential of AI PCs.

“We know organisations and their knowledge workers are excited about AI, and they want to fit it into all their workflows,” Brackney said. Dell is focused on integrating AI into software and ensuring it runs efficiently on the right systems, enhancing end-to-end customer journeys in AI.

Ethical concerns in AI deployment are also a priority for Dell. Addressing issues such as deepfakes, transparency, and bias, Brackney emphasised the importance of a shared, secure, and sustainable approach to AI development.

“We believe in a shared, secure, and sustainable approach. By getting the foundations right at their core, we can eliminate some of the greatest risks associated with AI and work to ensure it acts as a force for good,” explains Brackney.

User data privacy in AI-driven products is another critical focus area. Brackney outlined Dell’s strategy of integrating AI with existing security investments without introducing new risks. Dell offers a suite of secure products, comprehensive data protection, advanced cybersecurity features, and global support services to safeguard user data.

On the topic of job displacement due to AI, Brackney underscored that Dell views AI as augmenting human potential rather than replacing it.

“The roles may change but the human element will always be key,” Brackney stated. “At Dell, we encourage our team members to understand, explore, and, where appropriate, use tools based on AI to learn, evolve, and enhance the overall work experience.”

Looking ahead, Brackney envisions a transformative role for AI within Dell and the tech industry. “We see customers in every industry wanting to become leaders in AI because it is critical to their organisation’s innovation, growth, and productivity,” he noted.

Dell aims to support this evolution by providing the necessary architectures, frameworks, and services to assist its customers on this transformative journey.

Dell is a key sponsor of this year’s AI & Big Data Expo. Check out Dell’s keynote presentation ‘From Data Novice to Data Champion – Cultivating Data Literacy Across the Organization’ and swing by Dell’s booth at stand #66 to hear about AI from the company’s experts.

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 Nicholas Brackney, Dell: How we leverage a four-pillar AI strategy appeared first on AI News.

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.

The post Chuck Ros, SoftServe: Delivering transformative AI solutions responsibly appeared first on AI News.

In an interview ahead of the AI & Big Data Expo North America, Igor Jablokov, CEO and founder of AI company Pryon, addressed these pressing issues head-on.

Critical ethical challenges in AI

“There’s not one, maybe there’s almost 20 plus of them,” Jablokov stated when asked about the most critical ethical challenges. He outlined a litany of potential pitfalls that must be carefully navigated—from AI hallucinations and emissions of falsehoods, to data privacy violations and intellectual property leaks from training on proprietary information.

Bias and adversarial content seeping into training data is another major worry, according to Jablokov. Security vulnerabilities like embedded agents and prompt injection attacks also rank highly on his list of concerns, as well as the extreme energy consumption and climate impact of large language models.

Pryon’s origins can be traced back to the earliest stirrings of modern AI over two decades ago. Jablokov previously led an advanced AI team at IBM where they designed a primitive version of what would later become Watson. “They didn’t greenlight it. And so, in my frustration, I departed, stood up our last company,” he recounted. That company, also called Pryon at the time, went on to become Amazon’s first AI-related acquisition, birthing what’s now Alexa.

The current incarnation of Pryon has aimed to confront AI’s ethical quandaries through responsible design focused on critical infrastructure and high-stakes use cases. “[We wanted to] create something purposely hardened for more critical infrastructure, essential workers, and more serious pursuits,” Jablokov explained.

A key element is offering enterprises flexibility and control over their data environments. “We give them choices in terms of how they’re consuming their platforms…from multi-tenant public cloud, to private cloud, to on-premises,” Jablokov said. This allows organisations to ring-fence highly sensitive data behind their own firewalls when needed.

Pryon also emphasises explainable AI and verifiable attribution of knowledge sources. “When our platform reveals an answer, you can tap it, and it always goes to the underlying page and highlights exactly where it learned a piece of information from,” Jablokov described. This allows human validation of the knowledge provenance.

In some realms like energy, manufacturing, and healthcare, Pryon has implemented human-in-the-loop oversight before AI-generated guidance goes to frontline workers. Jablokov pointed to one example where “supervisors can double-check the outcomes and essentially give it a badge of approval” before information reaches technicians.

Ensuring responsible AI development

Jablokov strongly advocates for new regulatory frameworks to ensure responsible AI development and deployment. While welcoming the White House’s recent executive order as a start, he expressed concerns about risks around generative AI like hallucinations, static training data, data leakage vulnerabilities, lack of access controls, copyright issues, and more.  

Pryon has been actively involved in these regulatory discussions. “We’re back-channelling to a mess of government agencies,” Jablokov said. “We’re taking an active hand in terms of contributing our perspectives on the regulatory environment as it rolls out…We’re showing up by expressing some of the risks associated with generative AI usage.”

On the potential for an uncontrolled, existential “AI risk” – as has been warned about by some AI leaders – Jablokov struck a relatively sanguine tone about Pryon’s governed approach: “We’ve always worked towards verifiable attribution…extracting out of enterprises’ own content so that they understand where the solutions are coming from, and then they decide whether they make a decision with it or not.”

The CEO firmly distanced Pryon’s mission from the emerging crop of open-ended conversational AI assistants, some of which have raised controversy around hallucinations and lacking ethical constraints.

“We’re not a clown college. Our stuff is designed to go into some of the more serious environments on planet Earth,” Jablokov stated bluntly. “I think none of you would feel comfortable ending up in an emergency room and having the medical practitioners there typing in queries into a ChatGPT, a Bing, a Bard…”

He emphasised the importance of subject matter expertise and emotional intelligence when it comes to high-stakes, real-world decision-making. “You want somebody that has hopefully many years of experience treating things similar to the ailment that you’re currently undergoing. And guess what? You like the fact that there is an emotional quality that they care about getting you better as well.”

At the upcoming AI & Big Data Expo, Pryon will unveil new enterprise use cases showcasing its platform across industries like energy, semiconductors, pharmaceuticals, and government. Jablokov teased that they will also reveal “different ways to consume the Pryon platform” beyond the end-to-end enterprise offering, including potentially lower-level access for developers.

As AI’s domain rapidly expands from narrow applications to more general capabilities, addressing the ethical risks will become only more critical. Pryon’s sustained focus on governance, verifiable knowledge sources, human oversight, and collaboration with regulators could offer a template for more responsible AI development across industries.

You can watch our full interview with Igor Jablokov below:

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.

The post Igor Jablokov, Pryon: Building a responsible AI future appeared first on AI News.

Paul O’Sullivan, Senior Vice President of Solution Engineering (UKI) at Salesforce, sheds light on the complexities of this transformative landscape, urging businesses to tread cautiously while embracing the potential of artificial intelligence.

Unprecedented opportunities

Generative AI has stormed the scene with remarkable speed. ChatGPT, for example, amassed 100 million users in a mere two months.

“If you put that into context, it took 10 years to reach 100 million users on Netflix,” says O’Sullivan.

This rapid adoption signals a seismic shift, promising substantial economic growth. O’Sullivan estimates that generative AI has the potential to contribute a staggering £3.5 trillion ($4.4 trillion) to the global economy.

“Again, if you put that into context, that’s about as much tax as the entire US takes in,” adds O’Sullivan.

One of its key advantages lies in driving automation, with the prospect of automating up to 40 percent of the average workday—leading to significant productivity gains for businesses.

The AI trust gap

However, amid the excitement, there looms a significant challenge: the AI trust gap. 

O’Sullivan acknowledges that despite being a top priority for C-suite executives, over half of customers remain sceptical about the safety and security of AI applications.

Addressing this gap will require a multi-faceted approach including grappling with issues related to data quality and ensuring that AI systems are built on reliable, unbiased, and representative datasets. 

“Companies have struggled with data quality and data hygiene. So that’s a key area of focus,” explains O’Sullivan.

Safeguarding data privacy is also paramount, with stringent measures needed to prevent the misuse of sensitive customer information.

“Both customers and businesses are worried about data privacy—we can’t let large language models store and learn from sensitive customer data,” says O’Sullivan. “Over half of customers and their customers don’t believe AI is safe and secure today.”

Ethical considerations

AI also prompts ethical considerations. Concerns about hallucinations – where AI systems generate inaccurate or misleading information – must be addressed meticulously.

Businesses must confront biases and toxicities embedded in AI algorithms, ensuring fairness and inclusivity. Striking a balance between innovation and ethical responsibility is pivotal to gaining customer trust.

“A trustworthy AI should consistently meet expectations, adhere to commitments, and create a sense of dependability within the organisation,” explains O’Sullivan. “It’s crucial to address the limitations and the potential risks. We’ve got to be open here and lead with integrity.”

As businesses embrace AI, upskilling the workforce will also be imperative.

O’Sullivan advocates for a proactive approach, encouraging employees to master the art of prompt writing. Crafting effective prompts is vital, enabling faster and more accurate interactions with AI systems and enhancing productivity across various tasks.

Moreover, understanding AI lingo is essential to foster open conversations and enable informed decision-making within organisations.

A collaborative future

Crucially, O’Sullivan emphasises a collaborative future where AI serves as a co-pilot rather than a replacement for human expertise.

“AI, for now, lacks cognitive capability like empathy, reasoning, emotional intelligence, and ethics—and these are absolutely critical business skills that humans need to bring to the table,” says O’Sullivan.

This collaboration fosters a sense of trust, as humans act as a check and balance to ensure the responsible use of AI technology.

By addressing the AI trust gap, upskilling the workforce, and fostering a harmonious collaboration between humans and AI, businesses can harness the full potential of generative AI while building trust and confidence among customers.

You can watch our full interview with Paul O’Sullivan below:

Looking to revamp your intelligent automation strategy? Learn more about theIntelligent Automation Event & Conference, to discover the latest insights surrounding unbiased algorithyms, future trends, RPA, Cognitive Automation and more!

The post Paul O’Sullivan, Salesforce: Transforming work in the GenAI era appeared first on AI News.

At its core, Predibase is a declarative machine learning platform that aims to streamline the process of developing and deploying machine learning models. The company is on a mission to simplify and democratise machine learning, making it accessible to both expert organisations and developers who are new to the field.

The platform empowers organisations with in-house experts, enabling them to supercharge their capabilities and reduce development times from months to just days. Additionally, it caters to developers who want to integrate machine learning into their products but lack the expertise.

By using Predibase, developers can avoid writing extensive lines of low-level machine learning code and instead work with a simple configuration file – known as a YAML file – which contains just 10 lines specifying the data schema.

Predibase reaches general availability

During the expo, Predibase announced the general availability of its platform.

One of the key features of the platform is its ability to abstract away the complexity of infrastructure provisioning. Users can seamlessly run training, deployment, and inference jobs on a single CPU machine or scale up to 1000 GPU machines with just a few clicks. The platform also facilitates easy integration with various data sources, including data warehouses, databases, and object stores, regardless of the data structure.

“The platform is designed for teams to collaborate on developing models, with each model represented as a configuration that can have multiple versions. You can analyse the differences and performance of the models,” explains Molino.

Once a model meets the required performance criteria, it can be deployed for real-time predictions as a REST endpoint or for batch predictions using SQL-like queries that include prediction capabilities.

Importance of low-code in machine learning

The conversation then shifted to the importance of low-code development in machine learning adoption. Molino emphasised that simplifying the process is essential for wider industry adoption and increased return on investment.

By reducing the development time from months to a matter of days, Predibase lowers the entry barrier for organisations to experiment with new use cases and potentially unlock significant value.

“If every project takes months or even years to develop, organisations won’t be incentivised to explore valuable use cases. Lowering the bar is crucial for experimentation, discovery, and increasing return on investment,” says Molino.

“With a low-code approach, development times are reduced to a couple of days, making it easier to try out different ideas and determine their value.”

Trends in LLMs

The discussion also touched on the rising interest in large language models. Molino acknowledged the tremendous power of these models and their ability to transform the way people think about AI and machine learning.

“These models are powerful and revolutionizing the way people think about AI and machine learning. Previously, collecting and labelling data was necessary before training a machine learning model. But now, with APIs, people can query the model directly and obtain predictions, opening up new possibilities,” explains Molino.

However, Molino highlighted some limitations, such as the cost and scalability of per-query pricing models, the relatively slow inference speeds, and concerns about data privacy when using third-party APIs.

In response to these challenges, Predibase is introducing a mechanism that allows customers to deploy their models in a virtual private cloud, ensuring data privacy and providing greater control over the deployment process.

Common mistakes

As more businesses venture into machine learning for the first time, Molino shared his insights into some of the common mistakes they make. He emphasised the importance of understanding the data, the use case, and the business context before diving headfirst into development. 

“One common mistake is having unrealistic expectations and a mismatch between what they expect and what is actually achievable. Some companies jump into machine learning without fully understanding the data or the use case, both technically and from a business perspective,” says Molino.

Predibase addresses this challenge by offering a platform that facilitates hypothesis testing, integrating data understanding and model training to validate the suitability of models for specific tasks. With guardrails in place, even users with less experience can engage in machine learning with confidence.

The general availability launch of Predibase’s platform marks an important milestone in their mission to democratise machine learning. By simplifying the development process, Predibase aims to unlock the full potential of machine learning for organisations and developers alike.

You can watch our full interview with Molino below:

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.

The post Piero Molino, Predibase: On low-code machine learning and LLMs appeared first on AI News.

AI News caught up with Vincent Chio, Data Science Lead at Shopify, to hear what the company is doing in the space and how AI is improving the end-to-end retail experience.

AI News: How has AI changed the shopping experience in recent years for both sellers and buyers?

Vincent Chio: From automated marketing, to smart fulfilment, and predictive analytics, AI has made it easier for retailers to manage and grow their business. But not every retailer has the resources to build and implement AI in their business.

That’s why at Shopify, we really focus on bringing the power of AI—which has historically been reserved for enterprise businesses—into the hands of our merchants, who are businesses of all sizes. To provide a few examples of what that looks like, we use machine learning in our fulfilment network to predict the closest fulfilment centres and optimal inventory quantities per location to ensure fast, low-cost delivery of our merchants’ products. Our business chat app, Shopify Inbox, is built on a natural language processing foundation that helps merchants convert conversations into sales. And, through state-of-the-art machine learning models that predict merchant business success, our product Shopify Capital automatically sends our merchants funding offers without them having to apply.

How this shows up for and benefits buyers is in the form of more accessible, seamless and personalized shopping experiences. From targeted product recommendations, to faster shipping and better ways to connect, buyers expect more from retailers, and AI is helping retailers meet those expectations. 

AN: What are some of the hurdles you’ve faced in bringing an AI model into production and how did you overcome them?

VC: When it comes to shipping anything, there are always hurdles you’re bound to face. At Shopify, we follow a few guiding principles for implementing and scaling AI that ensure easy adoption from the get-go. 

At Shopify, we take a merchant-first approach to identifying problem areas. So our first principle for implementing and scaling AI is to make sure that what we’re building is solving a merchant problem, and that we have enough data to create a solution. 

Second, we start simple. If a regression model will solve our merchant problem, that’s where we start. This doesn’t mean we avoid building complex models, it just means that we first prove that we can use AI/ML to solve the problem, and then we iterate by building complex models. 

These two steps are key for getting stakeholder buy-in which, if you don’t get, can stop your project before it even gets off the ground. We’ve got more principles and tips that you can check out here. 

AN: An increasing number of third-party integrations are available for Shopify that harness AI—what are some of the most unique and/or interesting ones in your view?

VC: We’ve got a ton of third-party developers creating innovative apps that help extend the capabilities of our merchants’ stores. Some of the most interesting ones, in my opinion, are the ones using AI to translate those authentic, in-person retail experiences to online stores. Like I mentioned earlier, AI is changing the shopping experience by using data to bring more personalization to retail.

Our third-party apps that use algorithms to help merchants optimize the full buyer journey, from marketing and conversational automation, to recommendations and cart abandonment, not only have the power to help merchants convert, but they’re creating better shopping experiences for buyers.

AN: Shopify introduced LinNét earlier this year, its new product categorisation model. How does the new model differ from its predecessor and why was it deemed necessary?

VC: Shopify has seen amazing merchant growth in the past 2 years, hitting over 1.7 million merchants across the world. New merchants means new products, so we decided to reevaluate our existing product categorization model. We wanted to evaluate our old model’s performance because it’s important that we understand what our merchants are selling, so we can build the best products that help power their business growth.

After evaluating key metrics like how often our predictions were correct and how often do we provide a prediction, while also taking a look at our product road map and how our model might support new products, we decided to build a new model to improve our performance. 

Compared to our old model, LinNét not only uses text features for prediction but also images. On top of this, LinNét has the ability to understand products in multiple languages. LinNét was also part of a larger effort to modernize our machine learning systems and we can now do things like real-time prediction, which we couldn’t do with the previous model.

With these new features, LinNét has increased our leaf precision by 8% while doubling our coverage. If you’re interested in learning more, read our blog!

AN: What’s next for AI at Shopify?

VC: We’re excited to further leverage the scale of our data to not only empower Shopify but to create new experiences for our merchants that are impossible without data.

Some of the ways we’re doing that is by exploring how to better support merchant workflows through product understanding and creating experiences for merchants that suggest best actions for their workflows, while foregrounding merchant autonomy.

How that will show up is through eliciting merchant feedback through accepting or rejecting our recommendations, and through education around our machine learning approaches to workflow optimization.

AN: Shopify is sponsoring, speaking, and exhibiting at this year’s AI & Big Data Expo Europe. What can attendees expect from your presence at the event?

VC: Attendees can expect the chance to really get to know the Shopify Data Science & Engineering team, and the kind of work we do. 

On day one of the expo, you’ll get to hear more from me! Diving into Shopify Inbox and the natural language processing foundation behind the product, I’ll illustrate how we accelerate product development with AI at Shopify, providing takeaways that can be used at any organization. I’ll also cover how to build a data foundation to establish trust and identify opportunities only AI can solve at scale.

Then, in the afternoon, you’ll have the chance to hear from Shopify’s Yizhar Toren, Senior Data Scientist. Yizhar will join a panel conversation on ramping up AI projects, discussing key tips like how to move your project from experimentation to production, and turning AI into ROI.

Attendees will also get the chance to meet our speakers and recruiters at our booth on the exhibition floor. Come say hi at booth #301!

(Photo by Mike Petrucci on Unsplash)

Shopify will be sharing its invaluable insights during this year’s AI & Big Data Expo Europe which runs from 23-24 November 2021. Shopify’s booth number is 301. Find out more about the event here.

The post Vincent Chio, Shopify: On using AI to revolutionise the retail industry appeared first on AI News.

AI plays a key role in making what BMC Software calls an ADE (Autonomous Digital Enterprise). These cutting-edge businesses know how to wield the latest technologies to minimise manual tasks and maximise the use of uniquely human talents.

AI News joined Ram Chakravarti, CTO of BMC Software, to discuss autonomous digital enterprises and the growing demand for AIOps.

AI News: BMC coined the phrase “Autonomous Digital Enterprise” (ADE). What makes an ADE and what competitive benefits does it present?

Ram Chakravarti: An Autonomous Digital Enterprise (ADE) is made up of intelligent, interconnected, tech-enabled, value-creating systems that minimise manual effort to capitalise on human creativity, skills, and intellect across the enterprise. Transitioning to an ADE presents a variety of benefits for organisations, enabling them to rapidly evolve, and ultimately stay competitive through streamlined agility, customer centricity, and actionable insights. 

One of the key benefits is automation, working as a business function alongside humans, to execute tasks faster and reduce the risk of errors. This means that employees are no longer burdened with manual tasks and can focus on higher-value activities. In addition, an ADE optimises costs, while significantly enhancing customer satisfaction. To support the evolution to an ADE, organisations need to embrace new approaches to talent management, evolve IT departments and optimise technology buying.

AN: Why do we need AIOps and what are its benefits?

RC: There are numerous benefits that come with implementing AIOps. For starters, AIOps provides organisations with insights into all layers of the IT environment. Monitoring and maintaining large complex systems is becoming too much for humans alone. As an example, the volume of operational metrics and log data being created is overwhelming, which can drastically slow repair times when a problem does arise. By applying machine learning techniques and training the models using historical data, AIOps can analyse reams of performance data to identify potential issues, allowing IT teams to take precautionary maintenance measures before a problem impacts customers. An intelligent AIOps solution essentially automates manual processes, meaning organisations will begin to see an increase in employee satisfaction, productivity and customer retention, while significantly saving time and resources.

AN: Around a decade ago, DevOps was a new and barely understood term that has since become somewhat critical to success. Do you think we’ll look back on AIOps in the same way by the end of this decade?

RC: By the end of the decade, we can expect to see the demand for AIOps continue to grow as intelligent automation is added to the mix to help fix problems without human involvement, which I refer to as actionability. Additionally, the intelligence and predictive analytics capabilities of AIOps can be applied beyond traditional IT data: Consider operational data that’s generated by wind turbines, cell towers, trucking fleets. All of that data can be analysed to help predict physical failures and improve maintenance schedules. Noisy datasets will be a barrier of the past as smarter strategies and centralised AIOps solutions help organisations improve the customer experience, deliver on modern application assurance and optimisation, and coupled with intelligent automation to help organisations thrive as ADEs.

AN: What data can be obtained from sources like IoT devices, customer engagement systems, and social media, and how can such huge amounts of data be converted into actionable insights?

RC: By 2025, we can expect data to be reaching unprecedented levels, meaning essentially every company will become a technology-driven business. As noted above, industrial IoT can be used for predictive maintenance and improve monitoring of physical systems. Customer engagement and social media data are all part of delivering a transcendent customer experience. The ability to quickly aggregate, scrub, and analyse your data is key. It paves the way for differentiated business data with key insights across your technology, tools, and employees which then make it possible to deliver a powerful, personalised customer experience.

AN: BMC is a sponsor of this year’s AI and Big Data Expo Global in London and will also be hosting a keynote at the event. What insights do you plan on sharing with the audience?

RC: At the AI and Big Data Expo our focus will be on how enterprises are continuing to amass data at exponential rates, why it’s becoming more imperative to unlock its value and how traditional approaches to data and analytics transformations have seen high failure rates. In my session, I look forward to sharing a recipe for success that rapidly turns new insights into fully operationalised production deliverables – all designed to unlock tangible business value from data.

(Photo by Bill Oxford on Unsplash)

Ram Chakravarti will be sharing his invaluable insights during a keynote on day two of AI and Big Data Expo Global, which runs from 6-7 September 2021. Find out more about the event and how to attend here.

The post Ram Chakravarti, CTO, BMC Software: On the benefits of AIOps and autonomous digital enterprises appeared first on AI News.