Can AI increase our originality and imagination? This is a much-talked-about question. As per findings of the Gottlieb Duttweiler Institute, the response appears to sum up to yes – AI can enhance our inventive capacities.
Jan Bieser, the author of this research, says:
“AI can also take over more creative tasks by identifying patterns in data that humans would not have found.”
“In this case, AI does not just take over tasks that would be time-consuming; it might provide insights humans would have never found themselves.”
Using AI might sound like an easy solution. However, one catch is whether the data the systems run on is viable. Industry experts are concerned about companies not rigorously checking the data through which decisions are made. This data can be critiqued or inadequate and, in some cases, outdated. Arijit Sengupta, CEO and founder of Aible, believes that dry data can impede innovation.
Arijit Sengupta says:
“Your data is constantly evolving as circumstances shift rapidly.”
“Many AI projects fail because they are run on outdated or useless data and ignore the business realities.”
Melanie Nuce, the senior VP of GS1-US – a nonprofit consortium creating digital trading standards – pointed out that data scarcity may be an issue and useless data.
Melanie Nuce says:
“The most common mistake businesses make when implementing AI is believing that all of the necessary data exists in closed-loop systems.”
“Businesses may deploy AI with the belief that they can find value from the technology using all of their own data, but for AI to scale effectively, the data will likely need to be ingested and shared across trading partners.”
As reliance on AI increases, data issues will likely cause decisions “going astray,” asserted Sengupta.
“A mistake even the most established enterprises continue to make is relying on data as the sole source of truth.”
“We need to understand that traditional AI doesn’t have any understanding of your goals, cost-benefit tradeoffs or capacity constraints. All it knows is what is in your data. For that reason, data alone is the wrong basis for a successful AI strategy.”
Many Artificial Intelligence projects do not produce the desired results due largely to inadequate data; Shalabh Singhal, CEO of Trademo, shares this sentiment. Singhal says poor-quality data is the root cause of unsuccessful AI implementations.
Shalabh Singhal says:
“Biased or insufficient data can have serious long-term consequences for any AI project.”
“Most companies complain of poor ROI even after spending most of their budget on data collection. What they fail to understand is the importance of collecting the right data and further, cleaning and labeling it.”
The adoption of AI offers tremendous benefits. To experience those, it is important to recognize the full potential AI can bring.
Nuce went on to say:
“Feed it complete, accurate and consistent data.”
“When 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 strong and accurate as the data that feeds it.”
“Ensure the right data is being collected in machine-readable ways, so companies can achieve value faster.”
By utilizing data standardization, organizations can quickly innovate and launch new services/products promptly. This approach enables businesses to take advantage of arising opportunities swiftly and efficiently.
Nuce continues to say:
“Access to greater amounts of high-quality data enables data scientists to build algorithms that function at a much quicker learning capacity and require less supervision and management. We are still discovering what AI can do for mainstream businesses, but with external collaboration and data sharing, the possibilities are endless.”
Arijit Sengupta emphasizes the importance of careful consideration when designing AI-driven processes. He highlights the need to ensure these systems reflect efficient design with sustainability, reliability, and broader objectives.
Arijit Sengupta went on to say:
“Start with the end in mind.”
“When you start with a hammer, everything looks like a nail. That’s the first, and sometimes fatal, mistake. The available data may simply not support that use case, and AI can’t do anything if the data is not available.”
At the end of it all, using AI for real-world benefit and not for its purpose is essential. As Sengupta emphasizes, having successful AI projects include:
Sengupta continues to say:
“Business objective first.”
“If you want to increase revenue, start by better targeting your sales efforts, improving your marketing strategy, reducing customer churn, or increasing partner sales. The right approach points the AI at all the available data and figures out which use cases can be supported by the data to improve the business objective.”
While still in its adolescence, artificial intelligence has the potential to help business professionals automate repetitive tasks, identify new opportunities, and connect with customers in more personal ways. But for AI technology to realize its full potential in the workplace, businesses must have data sets that are sufficiently large, diverse, and clean.
The good news is that as data becomes more plentiful and accessible—thanks to advances in storage capacity and connectivity—the opportunities for AI will continue to multiply. So although we’re still at the beginning of the AI journey, the future looks promising.