Artificial intelligence opens up new possibilities when the data allows it

Can artificial intelligence make us more creative and innovative? It is a subject of hot debate and discussion. A recent analysis by the Gottlieb Duttweiler Institute suggests that AI can help us expand our range of innovations.

In addition to dealing with the mundane, “AI can also take on more creative tasks by identifying patterns in data that humans would not have found,” emphasizes the study’s author, Jan Bieser. “In this case, AI not only takes on tasks that would be time-consuming; it could provide insights that humans would never have thought of.”

There’s just one catch: How usable is the data running through these AI systems? AI does not appear in a vacuum. It is the result of the data behind it. Many industry professionals are concerned that companies are not paying enough attention to the data that drives their decision-making systems, data that may be imperfect, too limited, or outdated. Dry data also dry up innovation. “Your data is constantly evolving as circumstances rapidly change,” said Arijit Sengupta, CEO and Founder of Aible. “Many AI projects fail because they are based on outdated or useless data and ignore business realities.”

Data can be useless, or there just isn’t enough right data. “The most common mistake companies make when implementing AI is believing that all the necessary data is in closed systems,” said Melanie Nuce, senior VP of innovation at GS1-US, a nonprofit consortium that develops digital commerce standards . “Companies can use AI with the belief that they can derive value from the technology with all their own data, but for AI to scale effectively, the data will likely need to be ingested and shared by trading partners.”

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As reliance on AI increases, there is a risk that decisions will go astray due to underlying data issues. “One mistake even the most established companies continue to make is relying on data as the only source of truth,” says Sengupta. “We need to understand that traditional AI doesn’t understand your goals, trade-offs, or capacity constraints. It only knows what’s in your data. Data alone is therefore the wrong basis for a successful AI strategy.”

Bad data is why many AI implementations fail to deliver. “Biased or insufficient data can have serious long-term consequences for any AI project,” says Shalabh Singhal, CEO of Trademo. “Most companies complain of poor ROI, even when they spend most of their budget on data collection. What they don’t understand is the importance of collecting the right data and further cleaning and labeling it.”

To reap the full benefits of AI adoption, “feed it with complete, accurate, and consistent data,” says Nuce. “If data is not structured or harmonized, business processes cannot be automated and the investment is wasted – along with valuable time and resources. The insights we gain from AI are only as powerful and accurate as the data that feeds them.” She calls for stricter industry standards to “ensure the right data is captured in a machine-readable way, so companies can realize value faster.” .

With data standardization, companies will be able to innovate faster, Nuce continues. “Access to larger amounts of high-quality data enables data scientists to develop algorithms that work with a much faster learning ability and require less monitoring and management. We’re still discovering what AI can do for mainstream companies, but with external collaboration and data sharing, the possibilities are endless.”

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When designing AI-driven processes, “start with the end in mind,” says Arijit Sengupta. “When you start with a hammer, everything looks like a nail. This is the first and sometimes fatal mistake. The data available may simply not support this use case, and there is nothing AI can do if the data is not available.”

It boils down to not implementing AI for AI’s sake. The most effective AI projects are “business goal first,” Sengupta continues. “If you want to increase sales, start by better targeting your sales efforts, improving your marketing strategy, reducing customer churn, or increasing partner revenue. The right approach assigns the AI ​​all available data and figures out what use cases the data can support to improve the business objective.”

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I am an author, independent researcher and speaker covering information technology innovations, trends and markets. I co-chaired the AI ​​Summit in 2021 and 2022 and have also attended the IEEE International Conference on Edge Computing and the International SOA and Cloud Symposium Series. I am also co-author of the SOA Manifesto, which outlines the values ​​and guiding principles of service orientation in business and IT. I also regularly contribute to Harvard Business Review and CNET on topics shaping careers in business and technology.

Much of my research is affiliated with Forbes Insights and Unisphere Research/ Information Today, Inc. and covers topics such as artificial intelligence, cloud computing, digital transformation, and big data analytics.

In a previous life, I was the communications and research manager for the Administrative Management Society (AMS), an international professional association dedicated to advancing knowledge in the fields of IT and business management. I am a graduate of Temple University.

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