In this AI Inspired Story by AiThority.com, we had Bradley Jenkins, Intel’s EMEA lead for AI PC & ISV strategies participate to chat about the key benefits of Intel’s Core Ultra processor range and how modern enterprises can benefit from systems powered by it:

Key topics covered:

-> An overview of Intel Core Ultra Processors

-> How software optimization can impact the performance of Intel Core Ultra processors

-> Who benefits from laptops powered by Intel® Core™ Ultra Processors

-> A brief breakdown on the 3 AI engines driving this: CPU, GPU, NPU

Read more on what makes Intel vPro®: An Unrivaled Business PC Platform

 

Learn more about how Intel Core Ultra processors are built for business here

Vikram Murali, VP of Software Development, Application Modernization, and IT Automation at IBM, highlights more on the benefits of IBM Concert and how it empowers application teams, while taking us through the latest in GenAI and it’s impact on IT workflows:

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Hi Vikram, tell us about yourself and more about your role at IBM.

I’ve been part of the IBM family for almost two decades, wearing different hats and taking on leadership roles across various divisions. I have experience in working with various data warehousing appliances, database and information security products, data science and machine learning, next gen hybrid data stores, and Hadoop distributions. In my previous role, I lead development of watsonx.data lakehouse, part of the watsonx platform. In my current role as Vice President of Software Development, IBM Automation, I oversee the development of cutting-edge software solutions in the IT Automation and Network Automation Observability space, along with focus on driving app modernization and integration.

Also Read: Three Ways Generative AI Can Accelerate Knowledge Transfer Across An Organization

We’d love to know more about IBM Concert and how it enables end users?

Most application owners struggle with turning information from siloed data sources into actionable knowledge.  This challenge consumes an inordinate number of resources as application teams try to analyze, prioritize, and act based on the scattered data sources available to them.

IBM Concert provides a strategic view of applications across data sources, giving application teams the concrete recommendations they need to efficiently address the challenges their applications face. You can think of IBM Concert as the nerve center for your AI technology and operations.

IBM Concert uses AI to save application owners time and resources that usually go into analyzing and prioritizing hidden application data.  IBM Concert also produces more impactful outcomes by offering prioritized suggestions to guide the work of application teams.  Today, these outcomes focus on risk and compliance use cases, with additional use cases coming soon.

What are your predictions surrounding AI, GenAI and the future of business?

As we look forward, the initial allure and intrigue of early generative AI is transitioning into concrete business outcomes. This transformative technology is reshaping the way businesses boost productivity, encourage innovation, and spark creativity.

Open source pretrained AI models are expected to become increasingly popular, enabling businesses to boost growth by integrating these models with private or real-time data. This combination is set to improve productivity and cost-effectiveness.

We’ve also seen enterprises increasingly adopting customized generative AI applications that can meet their specific needs and provide more accurate responses based on proprietary data. This indicates a shift towards more tailored and efficient generative AI tools.

Also Read: AI For One and All

How can enhancements and innovations in GenAI specifically support IT teams in managing applications effectively across environments? What challenges do you still see IT teams struggle with as they implement AI powered tech to their daily functions?

GenAI can automate repetitive and mundane tasks, such as system monitoring, updates, and troubleshooting. IBM Concert specifically puts the user in the driver’s seat, providing application owners with a detailed understanding of their connected applications and toolsets. Concert can generate analyses, visualizations, and recommendations that enterprises can quickly turn into action. By helping organizations discover gaps, prioritize insights, and instrument changes, Concert helps reduce complexity and streamline operations to make their business more resilient, more innovative, and more cost-efficient.

The speed of application development and deployment is accelerating and will continue to do so in the coming years. This can overwhelm DevOps teams, particularly those who rely on manual efforts. To keep up with this rapid pace and advance their coding speeds, developers can use AI coding tools for generating code with greater efficiency and accuracy.

If you can also highlight how AI today enables IT teams to respect different compliance needs across regions, along with the biggest myths you feel IT teams still harbor when it comes to driving these kinds of processes with AI

AI-powered tools can help organizations fulfill compliance requirements by helping them effectively manage application performance and security certificates.

For example, IBM Concert’s automated visibility feature helps IT teams track certificate lifecycles, providing crucial insights into expiration dates and potential risks. This is particularly useful in managing compliance needs across different regions. IBM Concert also prioritizes certificates by their potential impact, allowing organizations to dedicate their time to renewals and minimize disruptions.

A common misconception among IT teams is that they don’t have the right skillset to drive these processes forward with AI. However, with the right educational resources, IT teams can quickly upskill themselves. Not only that, but the time saved from automating this process can also allow IT workers to focus on meaningful work or learning new skills.

Also Read: How the Art and Science of Data Resiliency Protects Businesses Against AI Threats

Five AI best practices you’d share with enterprise IT teams before we wrap up?

At IBM we believe that Artificial intelligence (AI) balanced with human oversight and accountability is crucial for AI lifecycle adoption. For this we recommend the following best practices:

1. Use AI to augment human intelligence, rather than operating independently of, or replacing it.
2. In a human-AI interaction, notify individuals that they are interacting with an AI system, and not a human being.
3. Design human-AI interactions to include and balance human oversight across the AI lifecycle. Address biases and promote human accountability and agency over outcomes by AI systems.
4. Develop policies and practices to foster inclusive and equitable access to AI technology, enabling a broad range of individuals to participate in the AI-driven economy.
5. Provide comprehensive employee training and reskilling programs to foster a diverse workforce that can adapt to the use of AI and share in the advantages of AI-driven innovations. Collaborate with HR to augment each employee’s scope of work.

For more on IBM recommendations and best practices; read here.

[To share your insights with us as part of editorial or sponsored content, please write to psen@itechseries.com]

Asaf has been at the helm of the tech, data, and AI scene for almost thirty years. He is the founder of Iguazio, an AI platform that enables enterprises worldwide to develop, deploy and manage their AI and Gen AI applications in live business environments. Get more insights from Asaf from the complete Q&A:

Hi Asaf tell us about yourself and more about Iguazio. What inspired the platform? Take us through the acquisition journey and what the road ahead will look like?

Hi, my name is Asaf Somekh, I’m the co-founder and CEO of Iguazio, an AI company that was acquired by McKinsey last year. I’ve been in the tech, data, and AI scene for almost thirty years. In 2014, after the successful exit of Voltaire (which floated on NASDAQ in 2007 and was later acquired by Mellanox), I climbed Mount Kilimanjaro with one of my Voltaire co-founders. Together we decided to found Iguazio to address the challenge of operationalizing machine learning, orchestrating, scaling, and accelerating AI delivery to impact. We built and scaled the company, focusing on enterprise AI projects. In 2022 gen AI burst into our lives with the release of ChatGPT, making the problems of getting AI from pilot to production even worse.  We see a great need from the market to streamline the development, deployment, and management of GenAI applications across the organization at scale.  In 2023 Iguazio was acquired by McKinsey, and today Iguazio is a part of QuantumBlack, McKinsey’s AI arm.

We are very excited about the road ahead – We’ve been working together with McKinsey on new client projects, including leading Fortune 500 companies. We see a lot of demand and special interest within the Financial Services industry, a highly regulated industry, a fact that poses additional challenges for deploying gen AI at scale.  We’re continuing to serve our original pre-acquisition clients as well.

Also Read: How the Art and Science of Data Resiliency Protects Businesses Against AI Threats

How did this acquisition specifically enhance McKinsey’s GenAI capabilities?

McKinsey acquired Iguazio in January 2023, in the context of scaling and operationalizing AI. McKinsey research shows that 90% of AI projects never make it to production, and enterprises are missing opportunities to generate real business impact through AI initiatives.  Now, the Iguazio AI platform is part of QuantumBlack, McKinsey’s AI arm, and its AI and machine learning innovation hub QuantumBlack Labs. Iguazio, along with Labs’ 30+ other products, enables teams to codify domain knowledge into reusable assets that can be deployed into client environments and unlock new scales of impact.

What are some of the biggest pain points enterprises face when scaling and deploying ML and GenAI across the enterprise?

Enterprises face two main challenges when advancing from gen AI proofs of concept to live implementations within business environments:

1. Gen AI Ops & Scaling – It is difficult to bring gen AI to production in an efficient and effective way.  The resources and infrastructure needed when you’re working in a live business environment, the level of complexity, the issues that arise – such as where do you deploy your application? How do you share resources across projects?  How do you build a feedback loop?  How do you centrally manage applications in production, to ensure company guidelines are being followed and that computing costs are being controlled? – All these are difficulties enterprises face every day when it comes to operationalizing and scaling gen AI.  LLM customization using sensitive data, GPU utilization, and hybrid deployments (including on-prem) are additional complexities in this category.
2. De-risking & Governance – Gen AI brings with it a host of risks for the enterprise, such as securing the company’s private data, mitigating AI hallucinations and biases, making sure to adhere to regulations and compliance standards, etc.  Implementing guardrails, governance, and monitoring mechanisms are essential when deploying GenAI in a live business environment. Governing gen AI within the enterprise is critical also in preparation for regulations that are soon to be imposed such as the EU AI Act.

Gen AI made it easier to build pilots, but much harder to move them to production, widening the gap between potential and actual business value.  Enterprises need to take these challenges seriously in order to start closing this gap and see real ROI from their investments.

Also Read: Real World Applications Of LLM

Can you talk about some of the biggest risks that are associated with GenAI and what enterprises should be more aware of today?

• Handling sensitive data – I think most enterprises today know better than to put their data into online gen AI tools, but most enterprises, especially in highly regulated environments, struggle with data privacy concerns every day.  It is important to be aware of PII removal techniques, options that exist to train your LLMs privately and deploy your Gen AI apps in your own environments, whether your virtual private cloud or even on-prem data center.
• AI hallucinations – It’s no secret that gen AI apps do not always provide the right answer, but it’s sometimes difficult to tell when they’re wrong if you’re not a subject matter expert, and to monitor them at all times.  Think about the car dealership whose virtual agent sold a car for a dollar or the chatbot that invented a travel policy that the airline needed to honor.  These are serious risks that need to be taken into account, and here too there are ways to mitigate them, for example by customizing your LLM or improving data quality before you begin training.
• Cost escalation – This may be one of the most overlooked risks.  As you scale gen AI, if your architecture is lacking, costs may skyrocket.  GPUs for example are often used inefficiently and are underutilized which result in the over-provisioning of these expensive computing resources.  It’s important to build a sound foundation and think about the right way to automate and scale your projects so that you don’t end up paying an arm and a leg for something that could have been done in a much more effective way.

What does the future of GenAI capabilities seem like for you: how will constant enhancements change the game across the tech sector?

I think we’re just scratching the surface of what gen AI will be able to do.

I believe we’ll see combinations of large foundational models with very task-specific ones – this combo will be able to improve accuracy on one hand while reducing risks and costs on the other.

I also believe that open-source models will play a key role in the ecosystem – we already see today that many applications, if built right, can achieve a high level of accuracy and impact just by using sets of open-source LLMs combined with classic machine learning and deep learning models.

Because of the great pace of change, we’ll see enterprises adopting open and flexible architectures, ones that enable them to swap components and weave in the latest technologies as they become available.

It’s an extremely exciting time to be a part of this industry, but also quite risky.  It is my hope that in the next couple of years, we’ll be able to look back at this time and be proud that we developed gen AI in a respectful, responsible way – for good.

Also Read: How Does Artificial Intelligence Drive Predictive Analytics Systems?

Before we wrap up, a few top myths you’d like to bust on GenAI?

Yes – Maybe a couple –

First of all, not everything needs to be solved with gen AI.  Sometimes there are simpler, more cost-effective ways of getting the same things done.

Sometimes, gen AI simply enables ‘developers to write bad code faster’ (and it’s not just developers, this can be applied to any practitioner using gen AI).  I would caution against using the technology blindly without establishing the right use case, guardrails, and standardized review processes.

Finally, just to reemphasize the importance of creating a sound backbone for your gen AI projects, with guardrails and central management.  This will make all the difference as you scale gen AI across the enterprise.

Thank you, Asaf, for your insights; we hope to see you back on AiThority.com soon.

[To share your insights with us as part of editorial or sponsored content, please write to psen@itechseries.com]