Artificial intelligence (AI) is “completely living up to its expected hype and hysteria,” with 70% of daily digital interactions being AI-based, and businesses generating multimillion-dollar revenue streams from it.
This was the word from Mike Bugembe, founder of UK-based AI consultancy, Lens.ai, delivering a keynote at the ITWeb Business Intelligence Summit 2020, in Johannesburg, today.
Bugembe is a bestselling author, international speaker and executive advisor, helping organisations use data and AI to transform their businesses and grow.
Discussing the importance of an AI strategy to gain business value, Bugembe noted that companies across the globe are ramping up investments in AI-related technologies and gaining multimillion-dollar-revenue streams, cutting costs, managing risk, improving operations, and finding innovative ways to develop products and strengthen customer intimacy.
However, he warned that without an intelligent roadmap, companies risk focusing on the wrong opportunities, resulting in failure to tap into the true promise of AI.
“Business and technology experts believe AI will be the most significant technological revolution that businesses have ever experienced,” said Bugembe.
“The reality is, yes this hype is undeniably real and you cannot ignore the potential of AI if you want your business to succeed in the coming decade, because the very existence of your organisation will depend on it. Despite this excitement, some executives are still asking if this is all hype, while others are asking if it is even relevant to their industry,” he pointed out.
Investments in AI have increased from $4 billion globally in 2014 to $27 billion in 2019, with around 70% of that amount going to early stage or series A funding rounds, he noted.
Referencing a PwC report, he said by the year 2030, AI is poised to contribute an estimated $15.7 trillion to the global economy.
“While it’s exciting to think that your company could reap the benefits of even a tiny portion of this astronomical pay-off, this will happen only if organisations play their cards right – and data remains key to the success of any AI strategy. Developing a strategic AI game plan will ensure that organisations focus on asking the right, smart questions of their data – the ones that will produce answers that will affect their bottom line.”
Listing examples of organisations that have made multimillion-dollar profits through their introduction of AI-based projects and services, he noted Google reported $46 billion in revenue in its fourth-quarter of 2019, while Amazon reported $87 billion – a portion of that is attributed to its AI-based recommendation engine, used in its online retail sales.
“Facebook reported a quarterly record $21.08 billion in revenue for the fourth quarter of 2019, largely driven by AI-centred advertising revenues.”
Besides the big tech giants, smaller organisations across various industries worldwide are also reaping great rewards from AI, he noted.
In the retail sector, 26% of companies which are currently using AI to run their operations will almost certainly grow rapidly in the coming years, with the technology offering them billions in cost-saving opportunities, enabling them to scale and expand the scope of their existing deployments.
“One key example of this is Walmart, which has used AI-driven image optimisation to realise savings of $86 million, with estimated savings of over $2 billion over the next five years. In the UK, meanwhile, Morrisons used AI for stock replenishment to reduce the company’s shelf gap by 30% during trial sessions, demonstrating the potential for major savings in the future.”
To be successful with their AI implementation strategy, organisations need to ensure their data is top-notch, and this takes strategic planning, as well as knowledge of what data is needed, how to obtain it and how best to gain insights, Bugembe pointed out.
“Certainly, data is the bedrock of any data science initiative, and sourcing top-quality data requires expertise and planning. Most approaches to AI require at least the following roles: a data engineer to organise the information, a data scientist who understands data analysis, and software engineers who implement applications of the data.
“These roles should all be led by a chief data officer, who understands exactly what the business needs to accomplish and will work in tandem with the corporate goals and strategies to make it all happen.”
While often tricky, all these resources need to work together – not only with each other, but also with their existing operations and all the people that make the core of the existing business initiatives work, he added.
“Deciding to implement AI into the organisation is a smart choice. However, if organisations don’t have a smart implementation strategy in place, investing efforts in developing an AI plan can lead to failure. If you fail to plan, plan to fail!"