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Obstacles and Opportunities of Democratizing AI for Organizations

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Obstacles and Opportunities of Democratizing AI for Organizations

Enterprise deployment of artificial intelligence (AI) is positioned for tremendous growth.

Artificial intelligence is set to change the business world by improving predictive analytics, sales forecasting, customer needs, process automation and security systems.

Types_of_AI_Use_Cases.png

Source: Everest Group

IBM’s Global AI Adoption Index revealed that a third of those surveyed will be investing in AI skills and solutions over the next 12 months. 

More expansive use of AI democratizes AI, providing access to insights to more people – technologists and non-technologists alike. The latter group might include people in leadership, sales, finance, human resources and operations. This is where AI will shine, empowering business teams to make AI-driven decisions.

Imagine: business teams do not have to know how to code or be schooled in the intricacies of AI’s backend. Instead, they will use AI like you and I use a mobile phone for efficiency (if we’re running late, we merely send a text notifying the other person), access information faster (if we’re in the grocery store and need a recipe, we look it up), make better decisions (GPS gives us the fastest route).

Just as mobile technology works without us understanding complex circuitry, algorithms or software, the democratization of AI across enterprises will be integrated in much the same way.

So, what will hold AI back and how will AI help enterprise companies gain traction?

3 Obstacles and Opportunities Organizations Face by Implementing Artificial Intelligence

AI_Deployment.png

Artificial intelligence deployment approach | Source: IBM

Obstacle #1: Data in disarray. Data that does not provide a complete picture and single version of truth because of data silos and various data formats within an organization.

Opportunity: Employing a data fabric. Using a data fabric to help organizations use data more effectively and get the right data to users regardless of where it is stored. One significant advantage of a data fabric is that data governance rules may be automatically set for compliance.

Having one information structure to garner insights and analytics from, integrating security to protect sensitive data and establishing a framework for implementing trustworthy AI positions AI as part of the business strategy, not solely an IT strategy so that AI directly impacts business operations.

It all comes back to connecting data with business drivers and a data fabric helps accomplish this. It is what I call “point-to-point” thinking – knowing the business imperatives, business drivers, the different levels of raw data, who is consuming the data, who will have access to the data, and why the data is important in decision-making and then, the big payoff with AI, how it will elevate experiences: customer experience, workforce experience, supply chain experience, strategic partner experience, community experience. In “point-to-point” thinking we don’t hoard data, but share it – securely.

Obstacle #2: Varied skill levels. A lack of AI technical skills across the enterprise and a reliable, open platform to bring AI to more people.

Opportunity: Creating a bridge to AI for people within the enterprise. Palantir for IBM Cloud Pak for Data is one of the great innovations of our time because it doesn’t require coding skills. People in non-technical roles can go from raw data to data insights quickly using application templates (think of all the designs being produced with minimal design experience because of apps like Adobe Photoshop and Canva). This is truly the path to democratizing AI.

People can now use AI to make better decisions in real time and improve business outcomes. These teams include sales and marketing, manufacturing operations, campaign managers, branch managers, franchise operators, human resources, among others.

An example: a customer walks into their regional bank. The banking professional greets the customer, invites them to sit down and pulls up their profile. They see, not only account information, but a 360-degree view of the person sitting across from them. Through a data fabric, non-tabular visualizations gathered from previously siloed data originating from different systems provides an AI-infused perspective.

This might include two algorithmically recommended customer offers inspired by marketing analyst data and intelligent customer segmentation and campaign propensity scoring powered by Watson models.

Going further, feedback from the customer can then be entered and that data influences future offers because it goes right back into IBM’s data and AI platform. IBM Cloud Pak for Data, which helps to simplify data management and protect sensitive data by establishing a framework for implementing trustworthy AI.

Obstacle #3: Solving for the wrong “x.” In hundreds of conversations I’ve had with enterprise leaders over the years about AI, one common failure I see not identifying the right problem or identifying use cases that will yield high return from AI.

Opportunity: Clearly articulating the problem to be solved. With AI, we are talking about a machine making reasonable conclusions based on data. Better defining the problem is akin to asking better questions.

Imagine the difference if you were in a store and asked someone if they sold products. The question is too vague to expect a meaningful answer. Ask where the tomatoes are and you get a clear answer. Both are valid questions, but one is more focused. That’s how defining the problem should be (this is not just for AI purposes; I devote a lot of space in my book, Ascend Your Startup, to defining the customer problem because I believe building the wrong solutions plagues many companies)

Key Takeaways

How Artificial Intelligence Is Revolutionizing the Advertising Industry

In an interview with famed Mount Everest climber George Mallory, a reporter asked him why he wanted to climb the formidable mountain. His answer: “Because it’s there.” AI is very much the same thing. It has obstacles, yet it has the allure of opportunity and of making measurable progress.

Here are the big three takeaways for enterprise companies:

•  Use a data fabric. Information is powerful – and it exists! Don’t let siloed data and inconsistent data formats hold people back from making better decisions.

•  Give people what they need to succeed in their jobs. Tools such as low code/no code enable business users to rapidly leverage data and apply AI in their decision making. 

•  Go back to square one and define the problem. Solving for “x” without fully understanding “x” wastes precious time, causes unnecessary frustration and marginalizes the experience for everyone involved.

The Rise of AI

AI_in_Business_2022.png

Source: Statista

In a Forbes article on the topic of AI, author Manas Agrawal writes, “With rapid learning and adoption, AI is no longer a crystal ball technology but something that humans now interact with in nearly every sphere of life.”

In a very short time, we won’t be talking about AI adoption as people see it as part of doing business and part of making life more efficient. AI then will shift to being part of an enterprise’s business strategy, delivering value for non-technical people working in many different areas like customer experience, brand differentiation, HR, research and development, management and sales.

This is what the democratization of AI looks like at the crossroad of technology and humanity to improve outcomes for people leading successful enterprise businesses.


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TECHNOLOGY

Next-gen chips, Amazon Q, and speedy S3

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AWS re:Invent, which has been taking place from November 27 and runs to December 1, has had its usual plethora of announcements: a total of 21 at time of print.

Perhaps not surprisingly, given the huge potential impact of generative AI – ChatGPT officially turns one year old today – a lot of focus has been on the AI side for AWS’ announcements, including a major partnership inked with NVIDIA across infrastructure, software, and services.

Yet there has been plenty more announced at the Las Vegas jamboree besides. Here, CloudTech rounds up the best of the rest:

Next-generation chips

This was the other major AI-focused announcement at re:Invent: the launch of two new chips, AWS Graviton4 and AWS Trainium2, for training and running AI and machine learning (ML) models, among other customer workloads. Graviton4 shapes up against its predecessor with 30% better compute performance, 50% more cores and 75% more memory bandwidth, while Trainium2 delivers up to four times faster training than before and will be able to be deployed in EC2 UltraClusters of up to 100,000 chips.

The EC2 UltraClusters are designed to ‘deliver the highest performance, most energy efficient AI model training infrastructure in the cloud’, as AWS puts it. With it, customers will be able to train large language models in ‘a fraction of the time’, as well as double energy efficiency.

As ever, AWS offers customers who are already utilising these tools. Databricks, Epic and SAP are among the companies cited as using the new AWS-designed chips.

Zero-ETL integrations

AWS announced new Amazon Aurora PostgreSQL, Amazon DynamoDB, and Amazon Relational Database Services (Amazon RDS) for MySQL integrations with Amazon Redshift, AWS’ cloud data warehouse. The zero-ETL integrations – eliminating the need to build ETL (extract, transform, load) data pipelines – make it easier to connect and analyse transactional data across various relational and non-relational databases in Amazon Redshift.

A simple example of how zero-ETL functions can be seen is in a hypothetical company which stores transactional data – time of transaction, items bought, where the transaction occurred – in a relational database, but use another analytics tool to analyse data in a non-relational database. To connect it all up, companies would previously have to construct ETL data pipelines which are a time and money sink.

The latest integrations “build on AWS’s zero-ETL foundation… so customers can quickly and easily connect all of their data, no matter where it lives,” the company said.

Amazon S3 Express One Zone

AWS announced the general availability of Amazon S3 Express One Zone, a new storage class purpose-built for customers’ most frequently-accessed data. Data access speed is up to 10 times faster and request costs up to 50% lower than standard S3. Companies can also opt to collocate their Amazon S3 Express One Zone data in the same availability zone as their compute resources.  

Companies and partners who are using Amazon S3 Express One Zone include ChaosSearch, Cloudera, and Pinterest.

Amazon Q

A new product, and an interesting pivot, again with generative AI at its core. Amazon Q was announced as a ‘new type of generative AI-powered assistant’ which can be tailored to a customer’s business. “Customers can get fast, relevant answers to pressing questions, generate content, and take actions – all informed by a customer’s information repositories, code, and enterprise systems,” AWS added. The service also can assist companies building on AWS, as well as companies using AWS applications for business intelligence, contact centres, and supply chain management.

Customers cited as early adopters include Accenture, BMW and Wunderkind.

Want to learn more about cybersecurity and the cloud from industry leaders? Check out Cyber Security & Cloud Expo taking place in Amsterdam, California, and London. Explore other upcoming enterprise technology events and webinars powered by TechForge here.

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HCLTech and Cisco create collaborative hybrid workplaces

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Digital comms specialist Cisco and global tech firm HCLTech have teamed up to launch Meeting-Rooms-as-a-Service (MRaaS).

Available on a subscription model, this solution modernises legacy meeting rooms and enables users to join meetings from any meeting solution provider using Webex devices.

The MRaaS solution helps enterprises simplify the design, implementation and maintenance of integrated meeting rooms, enabling seamless collaboration for their globally distributed hybrid workforces.

Rakshit Ghura, senior VP and Global head of digital workplace services, HCLTech, said: “MRaaS combines our consulting and managed services expertise with Cisco’s proficiency in Webex devices to change the way employees conceptualise, organise and interact in a collaborative environment for a modern hybrid work model.

“The common vision of our partnership is to elevate the collaboration experience at work and drive productivity through modern meeting rooms.”

Alexandra Zagury, VP of partner managed and as-a-Service Sales at Cisco, said: “Our partnership with HCLTech helps our clients transform their offices through cost-effective managed services that support the ongoing evolution of workspaces.

“As we reimagine the modern office, we are making it easier to support collaboration and productivity among workers, whether they are in the office or elsewhere.”

Cisco’s Webex collaboration devices harness the power of artificial intelligence to offer intuitive, seamless collaboration experiences, enabling meeting rooms with smart features such as meeting zones, intelligent people framing, optimised attendee audio and background noise removal, among others.

Want to learn more about cybersecurity and the cloud from industry leaders? Check out Cyber Security & Cloud Expo taking place in Amsterdam, California, and London. Explore other upcoming enterprise technology events and webinars powered by TechForge here.

Tags: Cisco, collaboration, HCLTech, Hybrid, meetings

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Canonical releases low-touch private cloud MicroCloud

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Canonical has announced the general availability of MicroCloud, a low-touch, open source cloud solution. MicroCloud is part of Canonical’s growing cloud infrastructure portfolio.

It is purpose-built for scalable clusters and edge deployments for all types of enterprises. It is designed with simplicity, security and automation in mind, minimising the time and effort to both deploy and maintain it. Conveniently, enterprise support for MicroCloud is offered as part of Canonical’s Ubuntu Pro subscription, with several support tiers available, and priced per node.

MicroClouds are optimised for repeatable and reliable remote deployments. A single command initiates the orchestration and clustering of various components with minimal involvement by the user, resulting in a fully functional cloud within minutes. This simplified deployment process significantly reduces the barrier to entry, putting a production-grade cloud at everyone’s fingertips.

Juan Manuel Ventura, head of architectures & technologies at Spindox, said: “Cloud computing is not only about technology, it’s the beating heart of any modern industrial transformation, driving agility and innovation. Our mission is to provide our customers with the most effective ways to innovate and bring value; having a complexity-free cloud infrastructure is one important piece of that puzzle. With MicroCloud, the focus shifts away from struggling with cloud operations to solving real business challenges” says

In addition to seamless deployment, MicroCloud prioritises security and ease of maintenance. All MicroCloud components are built with strict confinement for increased security, with over-the-air transactional updates that preserve data and roll back on errors automatically. Upgrades to newer versions are handled automatically and without downtime, with the mechanisms to hold or schedule them as needed.

With this approach, MicroCloud caters to both on-premise clouds but also edge deployments at remote locations, allowing organisations to use the same infrastructure primitives and services wherever they are needed. It is suitable for business-in-branch office locations or industrial use inside a factory, as well as distributed locations where the focus is on replicability and unattended operations.

Cedric Gegout, VP of product at Canonical, said: “As data becomes more distributed, the infrastructure has to follow. Cloud computing is now distributed, spanning across data centres, far and near edge computing appliances. MicroCloud is our answer to that.

“By packaging known infrastructure primitives in a portable and unattended way, we are delivering a simpler, more prescriptive cloud experience that makes zero-ops a reality for many Industries.“

MicroCloud’s lightweight architecture makes it usable on both commodity and high-end hardware, with several ways to further reduce its footprint depending on your workload needs. In addition to the standard Ubuntu Server or Desktop, MicroClouds can be run on Ubuntu Core – a lightweight OS optimised for the edge. With Ubuntu Core, MicroClouds are a perfect solution for far-edge locations with limited computing capabilities. Users can choose to run their workloads using Kubernetes or via system containers. System containers based on LXD behave similarly to traditional VMs but consume fewer resources while providing bare-metal performance.

Coupled with Canonical’s Ubuntu Pro + Support subscription, MicroCloud users can benefit from an enterprise-grade open source cloud solution that is fully supported and with better economics. An Ubuntu Pro subscription offers security maintenance for the broadest collection of open-source software available from a single vendor today. It covers over 30k packages with a consistent security maintenance commitment, and additional features such as kernel livepatch, systems management at scale, certified compliance and hardening profiles enabling easy adoption for enterprises. With per-node pricing and no hidden fees, customers can rest assured that their environment is secure and supported without the expensive price tag typically associated with cloud solutions.

Want to learn more about cybersecurity and the cloud from industry leaders? Check out Cyber Security & Cloud Expo taking place in Amsterdam, California, and London. Explore other upcoming enterprise technology events and webinars powered by TechForge here.

Tags: automation, Canonical, MicroCloud, private cloud

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