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ZDNET Highlights
- Successful companies refine their strategies to guide AI explorations.
- You can start hundreds of projects, but quantity must become quality.
- Consider key elements such as architecture, redundancy, and goals.
As many business leaders struggle to create a competitive advantage from AI, your company will need a well-thought-out strategy to beat the rest.
For Art Hu, global CIO of tech giant Lenovo, there are no half measures. “We want AI to permeate all aspects of our business,” he told ZDNET in a one-to-one conversation in a London hotel, suggesting that his company has made a top-down and bottom-up commitment, where employees are encouraged to explore AI in a strictly governed and safe way.
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Key use cases include summarizing conversations to assist support specialists, applying agentic AI to enterprise-grade software engineering, and using Gen AI to create effective marketing collateral.
Hu and his team have laid out a careful strategy for shaping their AI explorations — and here are five ways you can do the same.
1. Take a portfolio approach
Hu said Lenovo takes a portfolio-based approach to emerging technology across the broader AI product lifecycle.
“This approach ranges from ‘I’ve heard about AI, and I’m thinking about it’ to ‘I’m playing with it in the sandbox’ to ‘I’ve deployed it in my department’ to ‘Hey, thousands of people at the company use this tool,'” he said.
This portfolio approach means the company has over 1,000 registered projects across all business sectors.
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Hu partnered with the firm’s chief security officer and chief AI officer to establish a policy for AI exploration that shifted from blooming thousands of flowers to providing careful guidance.
“We want people to blossom and explore, but we need to control the risk, because there’s quite a long tail of unexpected consequences, especially if you’re not careful,” he said.
“You really want to be in a situation where people are very excited, where they’re pushing you to approve more projects.”
2. Improve your operating model
Hu said the main implication of encouraging people to explore AI is that IT should be run differently.
“In the past, people would come into IT, and we ran a highly centralized architecture,” he said. “What this means for us in the age of AI is that we have to rethink how we work with business.”
Hu: “We want AI to penetrate all aspects of our business.”
Lenovo
Instead of his team taking the requirements of non-IT professionals and turning them into systems, whether through off-the-shelf or self-developed applications, Hu’s technology organization must now carefully manage a new area of demand: generative and agentic AI.
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“Instead of a small subset of employees working on IT transformation and building systems, the barrier is lower, and there is more potential for the entire company to contribute to digitalization and intelligent transformation,” he said.
“If the entire company wants to get involved in AI and transformation, how do we manage that? It’s a very different operating model to have guardrails and guidelines.”
3. Embrace excess capacity
Hu said it’s also important to consider the concept of redundancy in the age of AI. In the past, and perhaps most of the past two decades, business leaders prioritized globalization and centralization.
Now, volatility and uncertainty characterize modern economic and technological practices, and the new normal means CIOs must adopt a new approach.
“We are designing and tweaking our systems to be more regional, and that’s because of the increasing focus on data sovereignty and data privacy by key regulatory regimes from China to the EU and the US,” he said.
This new approach to enterprise architecture begins as a business strategy, with built-in shock absorbers that provide redundancies in regional variability.
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In short, excess capacity that once seemed wasteful is now a sensible strategy for companies operating in an AI-enabled economy.
“With redundancy, if something happens to one part, your entire system doesn’t become paralyzed or unable to function,” he said.
“Normally, in a hyper-efficient world, redundancies are bad. But I think through COVID and the current volatility, the natural response is, ‘Well, maybe buffers are good,’ because you don’t know what’s going to happen in the future.”
4. Create a scoreboard
Hu said another key element of Lenovo’s AI strategy is that each executive committee member has AI goals.
“So, we are all committed to adopting AI,” he said. “It has an interesting competition effect, because we put up a scoreboard: ‘How is everyone else’s zone going?’ And you don’t want to be slower than the person next to you on the executive staff.”
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Hu said the competition is healthy because the resulting dynamic is that professionals in functions like marketing, sales, or human resources are all thinking about how to use AI productively.
“We think about our business in a very structured, systematic and detailed way. We have very specific quantifiable targets for level one, level two and level three achievements,” he said.
“From a lifecycle and value-chain perspective, we’re trying to provide as much transparency as possible. And it’s fun if you spend time illuminating this map of the company and see how it’s going.”
5. Whitelist Great Tool
Of course, speed is important when it comes to developing AI initiatives, but quality is also important. Hu said Lenovo tries not to give much priority to quality at the beginning.
“I wouldn’t worry about small amounts of funding in the early stages because we want to build a funnel,” he said.
“In the beginning, the most important thing is, ‘Did we learn something?’ Quality matters a lot when you want to produce AI for hundreds or thousands of people. For five or 10 people, if it works and you learn something, that’s good.”
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Hu said the focus on quality requires clear direction on requirements and equipment. A critical element to success is to establish governance to reduce some of the enormous uncertainty and noise around AI tools.
“What that approach means for us is that we whitelist a set of tools to say, ‘We think these will meet 85% to 90% of your needs if you’re starting to explore AI,’” he said.
“But there’s no guarantee that these tools will do everything, so we also have an ongoing process for introducing tools. People can say, ‘Hey, I have a use case, and none of the tools you’re providing are a good fit. Can we whitelist this tool?’ And then we’ll do the appropriate review and evaluation to be able to offer that tool.”
