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Focus on ‘The Business of AI’ to Move From Hype to ROI     

To kick off his new TechChannel series, "The Business of AI," sales and marketing leader Brian Silverman explains how companies can use the technology to drive growth

TechChannel AI

AI is gaining attention across industries as organizations explore its potential to reshape how we work and drive economic growth.

To understand the excitement surrounding the technology and its business impact, one must look no further than headlines like this one, from International Data Corporation (IDC): “Artificial Intelligence Will Contribute $19.9 Trillion to the Global Economy through 2030 and Drive 3.5% of Global GDP in 2030.”

Or, consider the observation Bill Gates made in a recent interview: “It’s the first technology that has no limit.” 

AI will transform industries, change how we work and create and redefine processes unlocking unprecedented innovation.

AI is not new. It has existed for centuries (A Very Short History Of Artificial Intelligence). The term was coined in a 1955 research paper. However, in the past, computing capabilities held back advancements in AI.

Recent advancements led by NVIDIA GPUs, deep learning and transformative training have significantly improved AI’s ability to process vast amounts of data and perform complex tasks. 

OpenAI’s release of ChatGPT 3.5 in November 2022 propelled AI into the mainstream. As the browser did for the internet, this milestone marked AI’s leap from labs and boardrooms to everyday accessibility.

Sam Altman, the CEO of OpenAI, recently shared at the NY Times Dealbook Summit that deep learning and Generative AI (GenAI) will become as pervasive as the transistor.

For AI to achieve these heights, organizations must move beyond hype to the real “Business of AI.” They need to focus on an AI strategy that aligns with their     objectives, deploying AI capabilities and solutions that accelerate innovation to realize the true value from their AI investments. 

TechChannel and I are launching a series of articles that will guide organizations to focus on “The Business of AI,” including utilizing the right AI technology for different use cases.

Different AI for Different Jobs

GenAI

GenAI leads the excitement and hype curve, as it leverages large data sets and deep learning to create new content, analysis, designs, insights and more. It offers value when creativity, personalization and rapid innovation are needed.  

Duolingo, for instance, leverages GenAI to create personalized educational experiences by analyzing user interactions, identifying areas of strength and weakness and adjusting lessons to make learning more efficient and engaging.

GenAI creates new content and can analyze large amounts of data, but it can be challenging, as responses can change from one prompt to the next. And without proper planning, it can hallucinate incorrect responses.

Traditional AI

AI requirements that involve accurate, repetitive processes may be better served by Traditional AI.   

Traditional AI relies on structured data, rule-based systems and machine learning focused on specific business use cases and tasks that require accuracy, such as fraud detection, preventive maintenance and demand forecasting.

For example, FICO Falcon Fraud Manager, powered by AI, leverages out-of-the-box fraud detection and prevention models based on data from more than 10,000 financial institutions worldwide. This helps organizations detect and prevent fraud quickly by monitoring transactions end-to-end.

Agentic AI

Recent advancements in AI include Agentic AI, which is building on GenAI and Deep Learning capabilities to move beyond assisting humans to actually performing work autonomously with minimal-to-no human input.

Amazon warehouse and fulfillment centers leverage agentic AI to manage half a million Amazon Robots navigating multi-floor warehouses. Autonomous agents and systems make decisions and rapidly adapt to complex and dynamic environments.

What’s in Store for ‘The Business of AI’

Join us over the next few weeks as we share observations, experiences and interviews with colleagues to address key questions that organizations should be asking to realize the actual value of “The Business of AI”:

  • What are the different types of AI, and which is better for various use cases and business opportunities? Just as important, when should AI not be used for a particular requirement?
  • What are the differences between Large Language Models (LLMs) and foundation models? How can you choose the right models to implement, develop      and train for the most value while containing the costs associated with AI?
  • How can you unlock the value of data and applications, which are the fuel for ensuring that your AI solutions are tailored to your organization?
  • How can enterprises’ legacy servers, applications and services be leveraged as accelerators and competitive advantages for your AI journey?
  • What is the right API strategy for AI? APIs are the wires connecting the AI world, requiring an API management strategy that includes security.
  • How do cybersecurity and protection differ in the AI universe? What new focus areas are required to ensure trust and transparency in AI implementations?
  • Looking for a roadmap for your AI Journey? Learn how to leverage our suggested AI Roadmap as a starting point for your organization to realize the “Business of AI.”

The next article, “Not All AI Is Created Equal: Differentiating AI Types and Their Value,” will focus on AI and its varying forms, including use cases and guidance on when to use them. 

Stay tuned, join us and share your comments and observations, as we discuss the true “Business of AI.”


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