There are few debates that AI will revolutionize work practices, but there is less agreement on the best way to exploit this transformation.
While 90% of CIOs pilot AI or invest in small or large -scale developments, more than two -thirds (67%) have not seen a measurable return on investment, according to the recently published Nash Squared / Harvey Nash Digital Leadership Report.
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“Managers know the technology, but they have trouble with its application in the company to create value,” said Zdnet Nash Squared Cio Ankur Anand in a conversation on key points emerging from the leadership survey.
So how can business leaders overcome this struggle? Four business leaders provide their advice on best practices for using AI to resolve the commercial problems of large companies.
1. Create a list of the first 10
Joe DEPA, Director of EY global innovation, said your user cases should be aligned with your highest commercial priorities.
He told ZDNET that this alignment should be a continuous work in progress. Business leaders should continue to refresh their approach to focus on the fields that matter.
“I often use a list of the first 10, just to stay simple,” he said. “Here are the 10 best use cases on which we will focus.
DEPA said that the priority revision of the first 10 list with other senior executives required a firm strategic hand.
“If people want to add things, I will say,” What will we make the list remove? “Because when you add something to the list, you have to withdraw something,” he said. “This approach helps keep people focused.
DEPA said that this careful strategy helps companies avoid spending money on AI solutions that do not hit their return on investment measures.
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“It is generally when you do not have a clear use or commercial value case, but you have a nice problem that you want to solve. These are the ones you should say:” Hold a second. I know it’s a nice problem, but what is the analysis of consciousness? “” He said.
“This is where you can get a rut a little if you take a path to try to solve certain problems with AI, without having a clear profitability analysis for its application.”
2. Raise hackathon sessions
Adobe Cio Cindy Stoddard said that his IT team had used AI in many areas and works with the rest of the company to identify other use cases.
His team used AI to explore past computer requirements and create recommendations so that commercial analysts and product managers know what will probably be necessary when users are asking for a new service.
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The IT team also used AI in tests to create scripts that can be reused to automate repetitive processes.
For new use cases, the team manages hackathons that help surface applications for emerging technology within industry and business.
“People submit different ideas about what they think they can change,” she told Zdnet. “We each encourage to subject improvement areas around what they see in the ground.”
The Stoddard team then works with commercial peers and external partners to select the best projects.
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“People who submit ideas have implemented teams. We will also bring some of our main supplier partners to help train staff on technologies,” she said. “Then we will go through a development and judgment process to see if the ideas offer value. Many ideas we offer are found in our production systems.”
3. Learn by failure
Caroline Carruthers, CEO of the Carruthers and Jackson consultant, five ways told Zdnet to prepare an organization for an AI transformation.
However, she also mentioned something crucial to identify the right use cases – adopting innovation.
Carruthers said there were a lot of emerging technologies to test, large -language models at Causal AI.
“You don’t know how this technology will be integrated into your organization. Waiting for things to be perfect with AI will not work,” she said. “You have to experience. You have to cut a safe sandbox, something with which you can start and play to understand how your organization can get the best of AI.”
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Like other business leaders, Carruthers said it is important that the projects you support focus on the right areas. This targeting can mean learning by failure, as long as it is not too expensive.
“If you are going to really experience with AI, then you must almost get into the celebration of failure,” she said. “Conducting an experience and discovering the experience did not work, but learning something new, is a valid use of an organization. Do not spend much money to do so.”
Carruthers said that each AI initiative should be part of a larger project aimed at overcoming major organizational challenges.
“Innovation consists in doing part of the company’s AI, but it does it small, iteratively and safely. When we talk about experiences, and especially when we talk about data, because it can solve some of the biggest problems in the world, our brains tend to go,” Oh, look at everything we can do, “she said.
“But if we try to tackle this problem, it’s big, difficult, and we will get bored, and we will not deliver things in time. While if you solve a little problem, it’s like” Oh, it’s good “, and then you solve other problems.”
4. Educate your employees
Tobias Sammereyer, team engineering team team at XXXLUTZ, said many people were rocked in a false feeling of security, thinking that easy -to -use tools like Chatgpt can be applied to any profitability analysis.
“We have to educate our guys how to use AI stuff correctly and how to be precise with their invites to get what they want,” he said.
Sammereyer told ZDNET that business and digital leaders should help their people understand the advantages and limitations of AI before applying technology to use cases.
“Try to tell them what is possible, but also what is not possible, because there are two types of people – we think that AI is just a media threshing and the other believes that they can do anything with it. And the two are incorrect.”
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Sammereyer said the key to success was to find the common ground.
“Educate people, then you can use AI, especially with a generative AI,” he said. “Just know that AI can make mistakes in the same way as a human being can make mistakes. Check the results, then you are ready to leave.”
He said this process is based on the fact that your AI systems are fed enough reliable data.
“AI is like a pleasure.” So you have to be critical in your reflection and see if the AI system has enough data and is equipped to give you the right answer. Remember that she will give you an answer, but not necessarily the right one. “
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