Reimagine Everything

If a company gets the basics right, it can build on them to advance and grow. As a business evolves, however, new challenges arise. Tools such as artificial intelligence (AI) are useful in addressing these challenges, but getting customers and businesses to adopt AI requires technical capabilities and experience. Only then can there be increased confidence and adoption, argues Wissam Youssef, co-founder, and CEO of CME.

Basic operational necessities in business cannot be overlooked, and companies rarely ignore them. Restaurants can be closed down for non-compliance to food standards; support call centers can access data to enhance service levels and customer satisfaction. Such ‘basics’ if eschewed, can be a real problem that costs money and damages a brand, observes Youssef.

It can sometimes be challenging to convince customers and businesses to adopt ‘new’ technology like AI, even when they solve challenges that typical software can’t. It’s more about going the extra mile, where you can have a smart solution for optimization and automation.

AI in action

Let us take the food industry as a typical example of how AI can be a game-changer. One of the challenges restaurants have, in general, is the ability to register temperature logs to satisfy health and safety requirements. Restaurant staff consider preparing and serving the food their primary job and may neglect an additional ‘chore’ like logging food and environmental temperatures or follow the right procedures for doing so.

“So, we automated the whole experience of temperature logging through hardware ‘internet of things’ (IoT) and AI,” says Youssef. “We created a mobile app and engineered an IoT device to identify items or food ingredients automatically, using image recognition techniques and mapping it to the infrared thermometer and to the device itself.”

This example combines the IoT with AI, specifically machine learning, into a model that seamlessly gathers data to ensure a restaurant or food brand operates safely and consistently while allowing staff to focus on their core competencies – preparing or serving delicious meals.

Another example is support for call centers. Most companies log and store calls; they go back to their records whenever they have problems. Using the business basics of optimizing data, and with Natural Language Processing, voice calls were transformed into readable tags, the essential elements of speech. “We were able to map the call and extract customer sentiment or a measurement for customer satisfaction from it,” explains Youssef.

“What’s nice is that because we were able to identify even the Arabic language; there are different dialects in Arabic, so this was challenging,” he adds. (CME has offices in Lebanon since 2003 and USA, Argentina, India, and China.)

AI: a luxury or a necessity?

Wit is also the case that when some companies try to apply the digital transformation, they forget the basics. It is essential for AI, or any other technology, to start solving the fundamental problems and then gradually build up the momentum for the technology’s more significant and long-term goals, Youssef explains.  “The challenge is not only the technical solution; it’s more about helping the customer to understand the benefits of adopting the solution,” he says.

Acceptance depends on the leadership of the company. Today, the job of technology firms is to spread awareness about AI and its impact. After all, technology firms that influence business leaders, the decision-maker is not usually the IT person, but the CFO or CEO. This needs to be taken into consideration when evangelizing AI.

Companies may put up barriers, such as privacy and data sharing issues, but the IT industry’s response is to present federated learning, to effectively address these, Youssef says. “One of the most effective ways to address these barriers is to act as co-creator with our customers. CME’s approach helps customers co-create their solutions for their existing problems based on their needs and constraints. We’re not here to apply ready-made products,” says Youssef. “If you’re worried about data privacy, we will find another solution. We want to make the customer the champion, not CME. It’s about making them shine.”

Youssef believes businesses will start to trust AI when they see the final output value, i.e., financial gain. He offers the example of medical claims adjudication in the healthcare payers’ industry. Today, it is a long process to analyze and decide whether a claim is entirely, partially approved, or fraudulent. “This is not just a rule-based solution. It needs what I call ‘the additional mile’, where you can make smart decisions using AI,” explains Youssef. 

Running different types of tests can help the enterprise to see the benefits of using AI. Using random sets of data, results from AI models and human claims managers were compared.  It showed that the AI model reduced the decision-making process and detected anomalies that were not noticeable before.

An especially compelling approach is to present AI as a black box (where the inner workings are unseen) to let the client judge its efficiency based on financial results. Whether it is cost optimization, reduction in administrative costs, or detecting values or anomalies in the system, the results, literally, speak for themselves.

Even software or technology companies need AI. “We had to use AI in our hiring processes at CME HR because we were not capable of handling all the typical HR processes we have in place,” reveals Youssef. “We all need AI; we need chatbots, but [adoption] depends on the scale, the location, and the need for AI in a company.

Whether working with partners in the medical, insurance, or food industries and supply chains, CME has provided an optimized solution to meet operational challenges. Predicting human error and other variables in an automated process lets businesses get the basics right with added efficiency.