Posted on August 16, 2018 by staff

‘SMEs are in box seat to exploit AI’

‘SMEs are in box seat to exploit AI’

Studio Graphene founder Ritam Gandhi
Studio Graphene founder Ritam Gandhi

The founder of a digital agency which works with existing AI solutions says small businesses have the agility to take advantage of AI.

Size is no obstacle to adopting artificial intelligence, according to Ritam Gandhi, founder of Studio Graphene.

Gandhi believes that the existing solutions, such as Google’s Vision API, allow businesses a relatively cheap way to integrate AI into their operations.

“Small businesses have a great opportunity with AI, because they are more agile and can more quickly adapt,” he told BusinessCloud.

“It’s incredibly cost-effective. My theory is that it is a positive vicious cycle. The companies already offering AI solution like Google need companies to use their AI capability, and the more data they receive, the more their AI capability gets refined.”

Gandhi, who founded his digital agency in 2014, explained that though small businesses are agile, they might face problems providing their new solution with enough data to make it usable.

“Developing new algorithms is hugely dependent on the volume of data that you can pass through to train those algorithms.  The larger companies have more data, and as a small company it is harder to amass that data an utilise it.”

Studio Graphene is an advocate of these existing products and has worked with its clients to implement AI solutions using Google’s Vision API for image recognition, and IBM’s Watson AI on IoT solutions.

Despite these more cost-effective options, Gandhi said that AI is not for everyone.

“It is a bit of a buzzword,” he said. “Everyone has a perception of what AI does, but people’s understanding is limited in terms of its use.”

Gandhi offers some helpful questions that businesses should ask themselves before getting involved in the AI game.

“Businesses should ask themselves first of all, is there anything that could be automated assisted,” he said. “Could a machine be trained on your data? Would a greater amount of data help you to accurately predict an outcome?”

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