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10 Things Microsoft has Learnt from Customers about Using AI to Improve their Organizations

Posted by Marbenz Antonio on August 3, 2022

10 examples of AI in customer service

There is no longer any question that artificial intelligence may significantly impact an organization, whether it is through fixing significant issues or entirely altering a business model. Microsoft established AI Business School two years ago to help important decision-makers across sectors by offering advice on strategy, culture, responsibility, and other important themes.

It’s difficult to implement AI completely and properly, and we certainly don’t have all the solutions. In contrast, we have observed select businesses that raise the standard for what an AI-powered organization can be as a result of our work with business leaders through AI Business School and in customer engagements.

To showcase customers who have demonstrated what it looks like to have a thorough strategy and execute across the organization to create business value and momentum with digital transformation, we launched Best of Business AI 2021. The frameworks from AI Business School are the foundation of Best of Business AI 2021, which includes 10 clients who are advancing their AI journey by tying a strong leadership strategy to their business objectives and technological capabilities.

Include everyone in your organization

Successful AI implementation companies are aware that AI is not simply an issue for leadership or technical teams. To better understand how each area of the company might profit from and be impacted, they have meaningful interactions with the workforce. Outokumpu gave access to data and AI models to employees across the firm from the start, allowing them to make the best choices in their daily job.

Stefan Erdmann, Chief Technology Officer at Outokumpu, says, “You have to get trust from the leadership team, talk to people about the rollout and get everybody on board.”

Boost business value using AI

AI can help you by resolving challenging issues, but it may also present new chances for success. By providing AI applications as a service to consumers, HOCHTIEF is making money off of its new AI competence.

“We will offer all these solutions to market and we will have a new business model for the company,” says David Koch, Chief Risk, Organization, and Innovation Officer for HOCHTIEF.

Select a good starting point

Find the best use case for applying AI-first, and then make sure you routinely share your findings. At CES 2019, Bell showcased its vision for AI and how it could change the company and the industry through an augmented reality experience. The organization has continued to build on the excitement generated by this display by making steady advancements toward its major objectives.

Matt Holvey, Senior Manager of Intelligent Systems at Bell, says, “Highly iterative, incremental proofs of concept—demonstrating something every three to six months—are the best way to gain and continue the championship and endorsements from upper leadership.”

Bring business and technology together

Everyone in your business is there because they have the necessary skills. Encourage them to work together, be creative, and contribute to developing original AI solutions. For the greatest impact, product specialists and AI experts at Mondelez International collaborated.

Rob Hargrove, Executive VP of Research, Development, and Quality at Mondelēz International, says, “On one side we have our data science, modeling, and simulation experts. On the other, we have our biscuit, chocolate, gum, or candy development experts in our product teams. Neither side knows the other’s job perfectly. So they know they can’t work in siloes because then neither side will be successful.”

Activate your values

The application of AI may have unexpected outcomes as with any big technology advancements, including security and privacy issues. Implement principles, processes, tools, and governance to assist your company in identifying and reducing risks, and to ensure that the outcome is consistent with your values. Making governance tools and procedures at AXA was not only the proper thing to do, but it also enabled the company to better understand its operations and the needs of its customers.

“Our responsibility is to properly manage the data that the customer opts to share with us,” says Jerome Lafon, Head of Connected Car Business Domain, Data and Tech Innovation at AXA.

Work with other organizations

Responsible AI use generally requires coordination with government agencies, connection with end users, and cooperation with other organizations. Seek advice from others and use what you discover to assist where you can. OceanMind, a nonprofit organization, collaborates with others to comprehend how human activity affects the oceans.

Kanit Naksung, Director of Fish Quarantine and Fishing Vessels Inspection Division at the Thailand Department of Fisheries, which is partnering with OceanMind, says, “We can use AI to help enforce fisheries law and to help authorities make better decisions.”

Test it first, then scale it

Invest in spreading something out across your organization once you’ve seen that it works. Devote resources to assessing AI technologies and formulating plans to broaden their application. At DHL, this involved creating the appropriate infrastructure, followed by offering support and communication to move from productization to proof of concept.

Markus Voss, CIO & COO for DHL Supply Chain, says, “In almost every one of our facilities, we are deploying those mature digital solutions. It is my vision to have every customer, every site, and every one of our employees senses that the world is changing in the supply chain.”

Put the most important things first

Prioritize your efforts, weigh the costs and rewards, and decide precisely what success will entail because you can’t tackle every issue at once. You might discover, as CSIRO did, that AI is needed everywhere, but it’s crucial to make wise decisions so your effort can have genuine benefit.

Jon Whittle, Director of CSIRO’s Data61, says, “Everybody wants to work with our AI experts, both within the organization and also outside CSIRO…The danger of that is that you get pulled in too many different directions, and you are no longer able to really make a difference in any area because you tried to do too many things.”

Empower everyone

Since good ideas can come from any place, Grab’s “AI everywhere” strategy has been incredibly effective. Non-technical employees collaborate with experts to develop concepts into solutions.

“When people embrace and have the conviction of what you’re trying to do with it, you really unlock the value of AI,” says Wui Ngiap Foo, Head of Technology at Grab.

Put your data to work

Unexpected uses of data can be made to improve and scale human expertise. Data at WPP helped the company’s designers and marketers are even more imaginative and develop new concepts.

Di Mayze, Global Head of Data & AI at WPP, says, “Data isn’t boring; it can really inspire and surprise. And that’s what we want, to make sure that data and creativity work together and party together!”

 


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