The potential value added of artificial intelligence (AI) to businesses is undisputed, yet research confirms that most companies still struggle to capitalize on the technology. In a recent panel hosted by YPO member and Managing Director of Techstars Vijay Tirathrai and Jean-Philippe Linteau, Consul General of Canada in Dubai and the Northern Emirates, industry leaders from Canada and the Middle East shared insights on how organizations can leverage AI while mitigating risks.

Sizing the opportunity

According to the International Data Corporation’s latest release, worldwide revenues for the AI market are forecast to grow 16.4% year-over-year, reaching USD554.3 billion by 2024.

Along with the U.S. and China, Canada is positioned to gain the most from this growth. “Canada has a thriving AI ecosystem, with world-leading research centers that have evolved into major hubs of AI, including Canada’s supercluster project in Montreal, Scale AI,” says Linteau. “Canada is now home to more than 800 AI companies, including more than 45 global tech multinationals, more than 60 investment groups, and 40-plus accelerators and incubators that focus on AI.”

In the past five years, regional players in the Middle East have recognized the significance of AI for their future growth and prosperity. Countries including the United Arab Emirates (UAE), Saudi Arabia, Egypt and Qatar are making substantial investments in building national ecosystems. In the UAE, initiatives such as the UAE Strategy for Artificial Intelligence and the world’s first AI minister, have placed AI at the center of the national economic strategy.

Organization challenges

At the forefront of the adoption of AI in the Middle East is Stallion AI, a research and development company headquartered in Ottawa, Canada, with branches in the region. Despite the growing adoption of AI by public and private organizations, its CEO, Samer Obeidat, says the impact of AI remains largely elusive.  

“Effective leadership is required to have a successful working AI strategy. Leaders need to possess a solid understanding of the basics to ensure they are making the right investment and aligning AI with business needs and strategy,” he says. “Many companies want to catch the trend, with no readiness, using AI as a marketing tool to start a big project and eventually failing to produce reliable solutions at scale.”

He also recommends organizations assign their “AI readiness” as the first step toward AI-driven digital transformation, including checking if the infrastructure is ready and the quality of data. Otherwise, they risk suffering from “rich-information, poor data syndrome.”

“Proper data infrastructure, right skills and right leadership are important cornerstones for change management to the AI space,” says Obeidat.

His work with multi-lingual AI tools across different regions has also allowed him to witness the importance of having, in addition to the right skills, a diverse team. “An observation I have made is that the systems we are building inherit (reflect) the principles and values of the data scientist teams building them. If the data scientist team is diversified, the system will reflect a similar diversification, and not show bias to one group.”

Health care applications

Mohammad Yaqub, Ph.D., a faculty member at the Mohammed Bin Zayed University of Artificial Intelligence, recently moved from his research position at Oxford University to lead a team working on AI applications in health care. Yaqub believes that AI, which he defines in simple terms as “the capability of a computer, through concepts and technologies, to act like a human being,” has the potential to revolutionize health care — specifically in diagnostics, treatment management and drug discovery.

“Before 2010, the use of AI was not massive in health care,” says Yaqub. “The breakthrough came, especially in health care, after that point. The focus began on using large-scale mining of data that doctors had generated over the years to address problems that we previously thought were untouchable – for example, breast cancer detection or predictions of Alzheimer’s disease,” says Yaqub. “AI has since allowed us to develop algorithms capable of working with low-quality data from machines that are not very expensive. That can help with affordable health care, deploying artificial intelligence solutions in developing countries.”

While acknowledging ongoing ethical issues around AI, including privacy needs and the challenge of cybersecurity, Yaqub is confident that post-COVID-19, investment in adopting AI tools to health care will continue to accelerate. In particular, he cites the area of epidemics, where access to cleaner data can allow meaningful predictions and preventive measures against a COVID-19 repeat scenario.

AI for clean energy

Devashish Paul, CEO and Founder BluWave-AI, formed his Canadian-based company in 2017 to help bring AI innovations to the distributed renewable energy sector. Since then, BluWave-AI has won regional, national, and international awards for driving the energy transition to a decarbonized economy.

“We don’t use AI for the sake of AI, but for impact, leveraging data that exists in enterprises more actively to save companies’ money and offset carbon. AI happens to be a great mechanism to make that happen,” says Paul. “We help utility and power generator companies, fleet operators, as well as large industrial customers, use their dispersed data to predict, optimize and control, making their system more efficient with respect to how energy is used and how carbon is offset.”

He adds that while human operators may make better decisions in some situations, AI tools offer overall better performance as input complexity increases. “We allow end customers to realize savings continuously by being an assistant to human operators initially. When comfortable, they (humans) let go and get computing to react to real-time changes. This is a huge opportunity worldwide, using the same assets more efficiently,” says Paul. “Industries generating data in real-time are sitting on a goldmine but have to mine that gold while data is generating.”

Employability and AI

Terralynn Forsyth, the Co-founder of FutureFit AI, has been working on a reskilling platform in partnership with employers and governments, what she calls “an AI-powered career GPS” for individuals as they navigate career transitions. The aim is to allow people to understand what skills they have, identify possible career paths in the labor market, and craft a personalized roadmap of learning programs and other support resources to make a successful career transition.

“By using a combination of live data on talent supply and employer demand, we can help workers make faster smarter, and more successful career decisions in an age of automation and disruption, anticipating and optimizing for better career transition outcomes,” says Forsyth. She adds that the platform also includes training and support to bridge opportunities for work positions at risk. “We are trying to use AI tools to simplify and distill the vast world of labor market information on careers and skills, helping with what employees need to be doing now for whatever career disruption.”

I would tell universities not to fixate on any given technology. Instead train on creating an innovation process. ”
— Devashish Paul, CEO & Founder BluWave-AI share twitter

Like Yaqub, Forsyth cites 2010 as a pivotal year, when computational social science and innovation in algorithms picked up as large access to data became available. “Having access to 350 million talent profiles, resumes and CVs from online sources allows us to track trajectories,” Forsyth says. “For example, we can look back at the financial crisis (in 2009) to see how people adapted and what was their career trajectories. There is a lot of interesting data that we can start to quantify and anticipate some of the changes.”

As for the skills disappearing because of AI, she adds, “There are buckets of competencies that are on the decline, including anything routine like data input and processing where we are already starting to see RPA (robotic process automation). Routine work can be both cognitive and noncognitive, and we see AI sweep over both.”

To prepare the next generation for AI, Paul says, “I would tell universities not to fixate on any given technology. Instead train on creating an innovation process. What is most important is the creative skills to deal with any app in future.” To the next generation of AI leaders, he adds, “Look at where the problems are in the world and figure out what skills are needed to help solve these problems. That will make the world a better place, and you make money along the way.”

Techstars is a strategic partner of YPO.