A new report by Rackspace Technology Inc, an end-to-end, multi-cloud technology solutions company, suggests that UAE organizations are now on par with their global counterparts in boasting mature capabilities in Artificial Intelligence (AI) and Machine Learning (ML) implementation.
But the vast majority of organizations in the wider EMEA region, including the UAE, are still at the early stages of exploring the technology’s potential (52%) or still require significant organizational work to implement an AI/ML solution (36%).
The survey indicates that many organizations typically lack the expertise and existing infrastructure needed to implement mature and successful AI/ML programs.
Many are still trying to navigate common pain points such as lack of internal knowledge, outdated technology stacks, poor data quality, or the inability to measure ROI.
Other key findings of the report include the following:
AI/ML implementation often fails due to a lack in internal resources. Half (50%) of respondents in the UAE report AI research and development initiatives have been tested and abandoned or failed.
The top causes for failure include lack of data quality (46%), lack of expertise within the organization (36%), and poorly conceived strategy.
The data reveals that organizations in the UAE see AI and ML potential in a variety of business units, including IT (49%), finance (39%) operations (41%), and customer service (50%).
Further, organizations that have successfully implemented AI and ML programs report increased productivity (44%) and improved customer satisfaction (38%) as the top benefits.
The top key performance indicators used to measure AI/ML success in the UAE include revenue growth (72%), data analysis (65%), profit margins (56%), and customer satisfaction/net promoter scores (46%).
Enhancing the customer experience with AI
Following the announcement of the UAE to use facial identification in some sectors, Paul Bogan, Chief Digital Officer, Serco Middle East commented “now more than ever is the time for facial recognition technology to empower the customer experience. The use of this technology to verify the personal identity of individuals in certain sectors should not be underestimated, and it will cause a ripple effect that ultimately will help drive home the importance of offering a great customer experience. Not only this, but it will further support the overall smart agenda by aligning Dubai with new more disruptive technology driven by artificial intelligence.”
Executive decision-making with AI
AI won’t supplant intuitive decision-making any time soon. But executives will need to disrupt their own decision-making styles to fully exploit AI’s capabilities. They will have to temper their convictions with data, test their beliefs with experiments, and direct AI to attack the right problems.
Executives across fields will face a self-disrupting choice: Learn to operate the machine, or be replaced by it.
AI is superior to humans at solving certain types of problems and that can inform executives’ approach to the technology.
The key for leaders in optimizing their work with AI is to recognize which sorts of problems to hand off to AI and which sorts the managerial mind, properly disrupted, will be better at solving.
The work of the acclaimed computer scientist Judea Pearl provides a guide who famously conceived the Ladder of Causation, which describes three levels of inferential thinking that, for our purposes, can provide a roadmap for self-disruption.
As Pearl notes in The Book of Why: The New Science of Cause and Effect, “No machine can derive explanations from raw data. It needs a push.” The first rung of the ladder is inference by association (if A, then B); next, inference by intervention (if you change input X, what happens to outcome Y?); and finally inference by applying counterfactuals: non-intuitive constructs that seem at odds with the facts and that lead to novel insights.
1- Association involves examining the correlation between two variables: When we raise prices, what happens to profits? AI is exceedingly good at sifting through vast quantities of data to uncover associations.
Humans aren’t very good at this, being both slower and often more subject to biases.
2- Intervention is the process of taking an action and observing its direct impact on an outcome. Business decision-makers do this all the time; for example, they might adjust a product’s price and then measure the effect on sales or profits. But they run into trouble when they’re overly confident about a predicted outcome.
3- Counterfactual inference involves the creative act of imagining what might have happened had a certain variable in an experiment, or in our case, a business activity, been different, given everything else we know.
Although without a time machine it’s impossible to test a true counterfactual to a previously executed business decision, you can seek out evidence of what the counterfactual reality might look like with AI.
All this doesn’t mean we need to hand decision-making over to the machines. Rather, it requires decision-makers to focus on the creative interventional and counterfactual thinking that humans are uniquely good at while relying on AI to do the data-intensive prediction and association tasks at which it truly excels.
AI in Schools
Founder Viola Lam says it can make the virtual classroom as good as, or better than, the real thing.
Students work on tests and homework on the platform as part of the school curriculum. While they study, the AI measures muscle points on their faces via the camera on their computer or tablet and identifies emotions including happiness, sadness, anger, surprise, and fear.
The system also monitors how long students take to answer questions; records their marks and performance history; generates reports on their strengths, weaknesses, and motivation levels; and forecasts their grades.
The program can adapt to each student, targeting knowledge gaps and offering game-style tests designed to make learning fun. Students perform 10% better in exams if they have learned using 4 Little Trees, says Lam.