Skip to content
Tiatra, LLCTiatra, LLC
Tiatra, LLC
Information Technology Solutions for Washington, DC Government Agencies
  • Home
  • About Us
  • Services
    • IT Engineering and Support
    • Software Development
    • Information Assurance and Testing
    • Project and Program Management
  • Clients & Partners
  • Careers
  • News
  • Contact
 
  • Home
  • About Us
  • Services
    • IT Engineering and Support
    • Software Development
    • Information Assurance and Testing
    • Project and Program Management
  • Clients & Partners
  • Careers
  • News
  • Contact

CIOs still grapple with what gen AI can do for the enterprise

Most CIOs have begun exploring generative AI to make sure they stay relevant. But many are finding that the technology on the market doesn’t yet live up to the hype. “After experimenting with both GitHub copilot and ChatGPT for over six months, I’m amazed by the pace at which generative AI is evolving,” says Yves Caseau, global CIO of Michelin. “But in its current state, it’s just a toolbox.”

There’s indeed a lot of hype around the latest wave of large language models (LLM) and associated tools, yet beneath the noise, there’s a whisper about how the technology will one day become indispensable. “Once it’s matured, generative AI will perform many of our mundane tasks — and this will free us to focus on new things,” says Caseau.

Yves Caseau, global CIO, Michelin

Yves Caseau, global CIO, Michelin

Michelin

Some technology leaders, including Patrick Thompson, former chief information and digital transformation officer of Albemarle, go so far to say that generative AI will become the most disruptive technology in our lifetimes. “It will be more disruptive than what Apple did with the iPhone for consumers,” says Thompson. “And for business users, it will surpass what Microsoft did for workforce productivity.”

The big question is what to do with it now.

A boost to traditional AI

While generative AI is new, AI is not. One of the first use cases of artificial intelligence in many companies, including both Michelin and Albemarle, was predictive maintenance, which at its most basic level is an algorithm trained on data collected by sensors. Once trained, the model looks for indicators that have led to failures and alerts human operators, who can then prevent manufacturing outages.

One common shortcoming of the basic setup of predictive maintenance is that rare events are underrepresented in the training data. As a result, the algorithm might not learn enough about the patterns in sensor output that, while infrequent, may forebode failure. To fill the gap, many companies complement the real data with synthetic data.

AI is being used in other ways in the enterprise as well, to do things like improve the efficiency of the supply chain, facilitate customer interactions, and help employees perform office tasks. Albemarle has been using AI as a virtual assistant since the recent pandemic lockdowns. “We were a little ahead of the game, mainly out of necessity,” says Thompson. “The pandemic forced us to find ways of self-servicing 7,000 employees at home.”

The self-service chatbot developed at Albemarle evolved into a tool to help with other corporate functions, which then developed into a virtual personal assistant that manages federated workflows, making it easier for employees to work with several systems at once without having to log into all of them. An employee, for instance, can participate in workflows and make inquiries by just communicating with the bot using natural language, and the bot interfaces with the enterprise business systems.

Patrick Thompson, Albemarle

Patrick Thompson, former CIO and digital transformation officer at Albemarle

Albemarle

But in a few short months, generative AI is beginning to take traditional AI to another level for applications like predictive maintenance. “Interactions become more conversational so you can ask questions and get different insights about the state of equipment,” says Thompson. “It can be used to curate internal and external industry data that’s then used to train traditional algorithms to deliver agile results.”

Moreover, generative AI offers an entry point for companies in sectors yet to use traditional AI. Sectors, such as finance, where most companies began developing data platforms years ago to use with analytical tools, are now experimenting with the newest AI technology using the same platforms.

“Generative AI can also be used to parse publicly available data on markets and companies to help make investment decisions,” says Chris Herringshaw, global CIO of Janus Henderson, the British-American global asset management group. “Rather than spend a lot of time manually researching all of that information, we want to use generative AI to summarize what’s out there, tell us where the signal in the noise is, and suggest areas for us to look into.”

The challenges and rewards of early adoption

Aside from a lack of maturity of the underlying technology, several other obstacles need to be overcome before enterprises further embrace generative AI. The first challenge is the lack of skills both in-house and among vendors that sell traditional applications.

The lack of in-house expertise affects the build versus buy decision every IT leader has to make. “‘Buy’ certainly gets you up the curve faster,” says Herringshaw. “You don’t need to figure out how to productize it, scale it, and support the underlying infrastructure. And prices are so low now that it costs very little to do exploratory work.”

Vendors are keeping the prices down to encourage adoption. But over time, companies will start putting more data into the models, which locks them in with a vendor, and they’ll start creating offshoots that are specialized in certain areas. Instead of using the general version of ChatGPT, for example, they’ll use versions for specific industries, like financial services.

“Once you have different models tailored for different use cases, you wind up with several versions running at the same time, which multiplies the subscription price,” says Herringshaw. “Our hope is that business revenue will scale with the cost. If we really find a way to revolutionize our investment process, the return should more than outweigh the cost.”

Chris Herringshaw, global CIO, Janus Henderson

Chris Herringshaw, global CIO, Janus Henderson

Janus Henderson

For the short term, subscribing to cloud-based models is less expensive than building in-house — and that may even hold true over the long term. Another advantage of buying is it makes adoption quicker and easier. But over the long-term, building in-house may be the better option for organizations that need to tailor models to their industry, or those that want to push the AI out to the edge and run inference on devices that aren’t connected to cloud-based services.

For the time being, though, very few enterprises have the skilled staff to build an AI model or tweak an existing one. Most companies don’t even have the expertise to be good users. To get the most out of what you buy, you need to first curate your enterprise data to train the models, and then during the inference phase, ask it questions in the right way. Above all, you need to know when to doubt the model.

While generative AI will probably increase the value a company can extract from data, and ultimately change the way businesses are run, it’ll also increase the gap between the digital savvy companies and the digital laggards. So regardless of whether organizations choose to build or buy, they should start developing some level of in-house expertise. “We’re starting to put together formal training to improve how we use the technology,” adds Herringshaw. “The first thing we want to get better at is asking questions.”

Not only is the lack of skills affecting how people use the models, but it’s also affecting the quality of third-party products, which often claim to include AI algorithms. CIOs who are buying the latest version of an enterprise application should check this claim, because there’s still confusion among the traditional application vendors as to how to integrate generative AI.

“Traditional technology vendors are partnering with companies that develop generative AI to deliver virtual assistants that unlock the value of enterprise business systems,” says Thompson, who sits on the advisory boards of several application vendors. “They’re having to balance security and data privacy with speed of delivering on the generative AI value promise.”

While many of the organizations that are now experimenting with generative AI are large enough to have the resources to investigate new things, use of this technology doesn’t have to be limited to big enterprises.

“If you get your governance, security, and your data ingestion right, generative AI can help scale a small company into a big company — and a lean one,” says Thompson. “My prediction is generative AI will be the most disruptive innovation in business. It’ll help consolidate, optimize, and integrate industries, which will result in new industry performance benchmarks that raise the bar and create greater shareholder value. Companies that don’t embrace generative AI will become obsolete.”

Artificial Intelligence, Generative AI, IT Leadership, IT Skills, IT Strategy, Predictive Analytics, Supply Chain, Vendor Management
Read More from This Article: CIOs still grapple with what gen AI can do for the enterprise
Source: News

Category: NewsNovember 1, 2023
Tags: art

Post navigation

PreviousPrevious post:Fighting fire with…dataNextNext post:Navigating Cloud Cost Complexity and Security

Related posts

Barb Wixom and MIT CISR on managing data like a product
May 30, 2025
Avery Dennison takes culture-first approach to AI transformation
May 30, 2025
The agentic AI assist Stanford University cancer care staff needed
May 30, 2025
Los desafíos de la era de la ‘IA en todas partes’, a fondo en Data & AI Summit 2025
May 30, 2025
“AI 비서가 팀 단위로 지원하는 효과”···퍼플렉시티, AI 프로젝트 10분 완성 도구 ‘랩스’ 출시
May 30, 2025
“ROI는 어디에?” AI 도입을 재고하게 만드는 실패 사례
May 30, 2025
Recent Posts
  • Barb Wixom and MIT CISR on managing data like a product
  • Avery Dennison takes culture-first approach to AI transformation
  • The agentic AI assist Stanford University cancer care staff needed
  • Los desafíos de la era de la ‘IA en todas partes’, a fondo en Data & AI Summit 2025
  • “AI 비서가 팀 단위로 지원하는 효과”···퍼플렉시티, AI 프로젝트 10분 완성 도구 ‘랩스’ 출시
Recent Comments
    Archives
    • May 2025
    • April 2025
    • March 2025
    • February 2025
    • January 2025
    • December 2024
    • November 2024
    • October 2024
    • September 2024
    • August 2024
    • July 2024
    • June 2024
    • May 2024
    • April 2024
    • March 2024
    • February 2024
    • January 2024
    • December 2023
    • November 2023
    • October 2023
    • September 2023
    • August 2023
    • July 2023
    • June 2023
    • May 2023
    • April 2023
    • March 2023
    • February 2023
    • January 2023
    • December 2022
    • November 2022
    • October 2022
    • September 2022
    • August 2022
    • July 2022
    • June 2022
    • May 2022
    • April 2022
    • March 2022
    • February 2022
    • January 2022
    • December 2021
    • November 2021
    • October 2021
    • September 2021
    • August 2021
    • July 2021
    • June 2021
    • May 2021
    • April 2021
    • March 2021
    • February 2021
    • January 2021
    • December 2020
    • November 2020
    • October 2020
    • September 2020
    • August 2020
    • July 2020
    • June 2020
    • May 2020
    • April 2020
    • January 2020
    • December 2019
    • November 2019
    • October 2019
    • September 2019
    • August 2019
    • July 2019
    • June 2019
    • May 2019
    • April 2019
    • March 2019
    • February 2019
    • January 2019
    • December 2018
    • November 2018
    • October 2018
    • September 2018
    • August 2018
    • July 2018
    • June 2018
    • May 2018
    • April 2018
    • March 2018
    • February 2018
    • January 2018
    • December 2017
    • November 2017
    • October 2017
    • September 2017
    • August 2017
    • July 2017
    • June 2017
    • May 2017
    • April 2017
    • March 2017
    • February 2017
    • January 2017
    Categories
    • News
    Meta
    • Log in
    • Entries feed
    • Comments feed
    • WordPress.org
    Tiatra LLC.

    Tiatra, LLC, based in the Washington, DC metropolitan area, proudly serves federal government agencies, organizations that work with the government and other commercial businesses and organizations. Tiatra specializes in a broad range of information technology (IT) development and management services incorporating solid engineering, attention to client needs, and meeting or exceeding any security parameters required. Our small yet innovative company is structured with a full complement of the necessary technical experts, working with hands-on management, to provide a high level of service and competitive pricing for your systems and engineering requirements.

    Find us on:

    FacebookTwitterLinkedin

    Submitclear

    Tiatra, LLC
    Copyright 2016. All rights reserved.