Why you should pick AI that is right for your business, not latest hype

Why you should pick AI that is right for your business, not latest hype

Laith Al-Bazirgan, Digital Evangelist, Endava

AI adoption is on the rise and on course to fulfil PwC’s regional projections and while some wring their hands over possible  job losses, we should note that AI is being adopted to improve employee experiences rather than replace them, explains Laith Al-Bazirgan at Endava.

Artificial intelligence, AI has rapidly evolved to become a defining force in the modern zeitgeist. In just a few years, the questions being asked about AI in boardrooms have shifted from why should we adopt to when should we adopt to how do we adopt to why have we not already adopted?

This latest attitude is one that seems to be especially pertinent to generative AI, the new tech sensation that has gripped the imagination and launched a thousand conversations about efficacy, integration, privacy, and security.

Chatting naturally with a generative AI tool like ChatGPT is a mesmerising experience. And yet we seem to have forgotten about the established and proven variants of AI in the face of the generative generation.

Remember that PwC prediction of a US$320-billion impact on the Middle East economy from AI by 2030? It predated ChatGPT, Google Bard, and Bing AI. We have been living the AI revolution for some time now, and for enterprises to make sure they share in that multibillion-dollar windfall, they must avoid the trap of adopting the latest thing simply because it is the latest thing.

They must shake off the gen-AI hype and get back to business, asking things like what is our mission, how do we do it better, and how do we pick the right flavour of AI to help us get there? What follows are some use cases that should help focus the mind.

Deep learning, DL

Sifting through what humans could never accomplish in a thousand years is quintessential AI. Deep learning, DL is a type of machine learning that uses artificial neural networks to crunch numbers at scale. Indeed, given enough data, these algorithms outperform baseline machine learning. DL can be used for a range of analytics use cases based on its ability to extract complex features and entity relationships from data.

It is good at conversational tasks, understanding and speech as well as image and video processing, even saving scientists from hours of eyeballing specimens under a microscope by taking over and doing the task significantly more quickly.

Any industry that places a premium on such R&D will benefit. Deep learning also brings value to customer service. It improves everything from the individualisation of digital experiences to the parsing of documents.

Natural-language processing, NLP

While large-language models, like ChatGPT, fall into this category, NLP is so much more than large language models, LLM. Many technologies oil the gears of understanding between computers and humans. Any speech-to-text tool uses NLP, but most predate the widely available LLMs of recent months.

Virtual chatbots are another application. Virtual assistants have the capacity to enhance employee productivity. Effective semantic translation tools enable more effective collaboration in multicultural workplaces across the region. NLP can also sift through tens of thousands of feedback forms, emails, online reviews, and social posts in search of patterns and insights; sentiment analysis gives brands and their marketing teams a huge leg up.

Predictive analytics

When statistics and ML team up, we get predictive analytics. Meaningful patterns found in historical data are powerful indicators of what is to come. If they duplicate certain determining factors, decision makers will see the probability of the recurrence of previous results. They can take informed action, which allows them to capitalise on positive predictions or avoid the worst impacts of negative forecasts.

Computer vision, CV

This is a type of deep learning used to process images for various use cases. A neural network consumes a huge dataset of images, assimilates human guidance, and learns. Advanced CV models can spot safety hazards on live CCTV footage. Autonomous vehicles know the difference between a brick wall and another moving vehicle. And production lines can automate quality control by allowing a CV module to look for defects and flag them.

Bigger picture

AI adoption is on the rise and on course to fulfil PwC’s projections. As some wring their hands over job losses, we should note that, to date, AI is being widely adopted to improve employees’ experiences rather than replace them. According to McKinsey, as of May 2023, 62% of GCC organisations use AI in at least one business function, so adoption is well underway and shows no sign of slowing.

Doom-and-gloom predictions of supplanted workforces, are overblown and fail to account for generational norms where younger, tech-savvy workers expect to be supported by advanced, relevant technology, not necessarily generative AI.

Generative AI has many applications, and some of them will fit use cases in a range of Middle East businesses. But it would be a mistake to be so focused on this single segment that you miss the impactful opportunities that other forms of AI present.

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