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Not all AI is created equal

How can businesses differentiate between ‘good’ AI and ‘bad’ AI when evaluating what is powering the organisation’s supply chain? André N Verdier, Managing Director at Blue Yonder ME discusses

Not all AI is created equal

Investing in supply chain technology is a mandate for all retailers in the Middle East today. This need has only become more acute over recent months as COVID-19 has disrupted traditional industries and relationships.

Blue Yonder and WMG, University of Warwick recently released a report exploring the digital readiness of today’s retail supply chains. Only 15% of global retailers reported having prescriptive or autonomous supply chains driven by artificial intelligence (AI) and machine learning (ML). However, over half of those surveyed are eager to embrace AI and ML, with 61% indicating plans to implement these technologies. It’s perhaps no surprise that the annual growth in the contribution of AI to the region’s economy is expected to range between 20-34% per year across the region, according to PwC.

Yet when shopping for solutions, it’s important to remember that not all AI is created equal. So how can businesses differentiate between “good” AI and “bad” AI when evaluating what’s powering their organisation’s supply chain? We have developed a pragmatic, five-stage process called “IDEAS” for evaluating whether an AI solution is good or bad, with retail forecasting used as an example.

Interconnected

AI models are built to mimic reality, but the closer the model can get to the complexity and interconnectedness of reality, the better that model is. Some AI solutions start with a base forecast and use machine learning to iteratively add on factors. However, AI solutions that make fewer baseline assumptions are stronger, as they consider how factors change and impact each other rather than expecting the same things to happen repeatedly.

When it comes to the retail supply chain, for example, forecasting previously used a layered approach beginning with a base forecast of weekly sales and adjusting that forecast based on factors like a promotion or an event. That base forecast would then be evaluated and altered in completely different silos based on each factor, and the results split by location and day of the week often using simplistic or static profiles. This resulted in interconnectivity between factors not being considered and the time horizon rather stretched. As we all know, human behavior isn’t so simple; it’s more like a network with a myriad of factors simultaneously impacting our decisions and the products we buy. So, would it not be better to have a model that mimics reality? Seemingly impossible a decade ago, Big Data, SaaS and AI have joined forces to make simulating reality…a reality. Good AI models look at all the factors that impact a situation concurrently, resulting in the most accurate insights available.

Dynamic

AI solutions must be dynamic as the market landscape is rapidly changing in complex ways. To effectively deal with uncertainty spurred by the velocity of change day to day, good AI can keep up with changes in real-time and adapt accordingly on its own. Along with responding quickly to changing influences, good AI understands that forecasts are not certain. If they were, we would all be rich, but humans are not 100% predictable.

Good AI is not only able to limit the unpredictability but also understand it, creating a probabilistic view of the forecast based on the individual uncertainty of each location, item, and day. The probabilities will change as the inputs change, producing a distribution that is information-rich, revealing for example the risks of under or over-ordering, which can then be used to fundamentally improve inventory management processes. When implemented in the retail supply chain, this allows for a deeper level of understanding when it comes to choosing the right times for offering certain products and promotions, as well as the right times for scaling back.

Explainable

One of the biggest obstacles for AI adoption in the Middle East today is trust. People tend to wonder how they can put their trust in something they don’t fully comprehend. If it’s not understood how the machine works, then how can you possibly know what benefit it’s providing?

An example of bad AI is “Black Box” AI, a type of model that does not allow the user to conceptualise the reasoning. It’s essentially an impenetrable system that fails to offer human collaboration capabilities. To thoroughly evaluate and trust an AI solution, the model must follow a “Glass Box” AI composition, so that the machine’s thinking can be observed and understood. Today’s AI interfaces can, for example, graphically illustrate the impact of influencing factors such as promotions, social media, or weather, on predicted outcomes. This accelerates personal and organisational learning, building trust while improving results.

Automated

Probabilistic forecasts can be used to set the right levels of stock in every location to meet your business strategy, whether to maximise profit, reduce waste, manage distribution costs, or improve freshness. With hundreds of factors combining with tens of thousands of products over hundreds of locations, it is increasingly efficient to trust the machine to automate and reveal demand forecasts for every type of product at each store location. The depth of information provided by outcome automation solutions can add deeper layers of transparency and understanding that can be applied across the supply chain.

Scalable

AI solutions must be scalable so that they can be utilised widely and effectively. The “AI Chasm” is the void that sits between a demo and an actual, scalable, and self-learning solution that can be successfully deployed at a vastly larger level, interacting meaningfully with users. When it comes to the supply chain, implementing a robust AI model that crosses the chasm not only helps to keep things running, but AI also delivers real-time optimisations, understanding the latest impacts to optimise tasks and actions, minimising disruptions, delivering efficiency, profit, service, and competitive advantage.

Together, the five qualities that make up a “good” AI offering – Interconnected, Dynamic, Explainable, Automated, and Scalable – reveal the underlying need to inform business decision making with a precision and scale unattainable just a short time ago. This is a truly transformative outcome for the growth of both Middle East businesses and supply chain professionals.

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