 |
| Artificial Intelligence Opens New Possibilities, If The Data Allows Gadgets 36T |
Can artificial
intelligence make us more creative and innovative? It is the subject of heated
debate and discussion. A recent analysis by the Gottlieb Duttweiler Institute
suggests that AI can help us extend the reach of our innovations.
"In addition to
dealing with everyday things, AI can also take on more creative tasks by
identifying patterns in the data that humans would not find," says study
author Jan Bieser. “In this case, the AI not only performs tasks that would
take time; it can provide information that humans would never find.”
There is only one
problem: what is the reality of the data being sent by these artificial
intelligence systems? Artificial intelligence does not arise in a vacuum. This
is the result of the data behind it. Many industry insiders worry that
companies aren't paying enough attention to the data that underpins their
decision-making systems—data that may be insufficient, too limited, or
outdated. Dry data also undermines innovation."Your data is constantly
evolving as circumstances rapidly change," said Arijit Sengupta, CEO and
Founder of Aible. "Many AI projects fail because they run on outdated or useless data and
ignore business realities."
The data may be useless
or there simply is not enough relevant
data. “The most common mistake companies make when implementing AI is to think
that all the necessary data is in
circular systems”; says Melanie Nuce, senior vice president of innovation at
GS1-US, a nonprofit consortium that develops standards for digital commerce.
“Enterprises can implement AI with the confidence that they can take advantage
of the technology with all their data, but for AI to scale effectively, data will likely need to be processed and
shared between business partners.”
With the increasing use
of artificial intelligence, there is a risk of wrong decisions due to
data problems. "Even the most established companies make the
mistake of relying on data as the only source of truth," says Sengupta.
“We need to understand that traditional AI doesn't understand your goals,
tradeoffs, or performance limitations. It only knows what's in your data. For
this reason, data alone is not the foundation of a successful AI strategy.
Poor data availability
is why many AI implementations fail. "Bias or insufficient data can have
serious long-term consequences for any AI project," says Shalabh Singhal,
CEO of Tradem. “Most companies complain about low ROI, even after spending most
of their budget on data collection. What they don't understand is the
importance of collecting the right data, then cleaning and labeling it.”
To reap the full
benefits of adopting AI, “deliver complete, accurate and consistent data”, says
Nuce."If data is not structured or harmonized, business processes cannot
be automated and investments and valuable time and resources are wasted. The insights
we gain from AI are as powerful and
accurate as the data that feed them." Industry standard to “ensure the
right data is captured in machine-readable form so companies can deliver value
faster.” Data scientists to create algorithms that operate at a much faster
learning rate and require less monitoring and management. .We're still
discovering what AI can do for large enterprises, but with external
collaboration and data sharing, the possibilities are endless.
When designing
AI-powered 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 available data may simply not support this use
case, and there is nothing the AI can do
if the data is not available.
It's about not implementing AI for AI's sake. The most successful AI projects are “business goal first,” Sengupta continues. “If you want to increase your sales, start by refocusing your sales efforts, sharpening your marketing strategy, reducing your customer base, or increasing the sales of your partners. The right approach takes AI to all available data and determines what use cases the data can support to improve the business objective.
