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Event Planning and Business Entertainment in the U.S. Corporate World

A liberal democracy can survive for a while on institutional strength and widespread agreement. As long as most people are generally satisfied with how things are going (or have made peace with the status quo), it is easy to imagine that something like a social contract will keep things on track. Hamish MacAuley makes a persuasive case that many Canadians came of age politically between the collapse of the Berlin Wall and the 2008 financial crisis, when consensus was widespread and politics seemed optional, thus many chose to stay out. We abandoned democratic governing habits during prosperous times. Instead, we played politics. In response, McGill's Jacob T. Levy advocates for political action that rejects the status quo while also refusing to burn it all down or take our ball and go home. We should participate in politics, even if it is unsatisfying. When the foundations of our democratic structure or the rights of vulnerable people are jeopardized, it makes sense to delegate aut
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Internet and Technology Adoption Canada and the USA

According to recent research conducted in Canada by KPMG, while more than one-third (37%) of Canadian businesses are considering how to use the well-known generative artificial intelligence (AI) platform ChatGPT to enhance their operations, this number is tiny compared to the nearly two-thirds (65%) of American businesses that are already utilizing the technology.

Only 35% of Canadian businesses report



they presently employ AI in their operations, compared to 72% of US businesses. This indicates that the overall adoption of AI by Canadian organizations is less than half that of US enterprises. Over 50% of the Canadian firms who use AI acknowledge that they could be employing it more effectively and efficiently. Of those that do, over four out of ten use it in their call centers.


Based on a poll of senior business and technology leaders at major U.S. and Canadian organizations, the results evaluate how these leaders are utilizing artificial intelligence (AI), which is fast turning into a competitive advantage for enterprises. Although generative AI is a particular kind of artificial intelligence that uses algorithms to create content quickly, AI has much broader applications, and American and Canadian organizations in the fields of healthcare, education, law enforcement, and finance have used various forms of AI to increase productivity and efficiency.

When it comes to adopting AI
 Canadian businesses are lagging behind their American counterparts. This is happening at a time when advancements in the field are advancing quickly, especially in the area of generative AI, according to Benjie Thomas, Canadian Managing Partner, Advisory Services at KPMG in Canada.


"When applied properly and ethically, generative AI can be very effective in increasing a company's productivity, efficiency, and competitiveness. Businesses in Canada who aren't implementing AI now run the risk of slipping behind, particularly as rivals in the United States continue to make progress in this area," Mr. Thomas continues.

Important conclusions


Lack of qualified personnel and high-quality data for AI algorithm training are the key obstacles keeping companies from utilizing AI efficiently. More than four in ten (44%) Canadian organizations stated their data sets are either too small or too vast, contain missing data, are inaccurate, or are improperly formatted. Almost half (47%) of these businesses indicated they lack the internal competence to validate and verify the quality of their AI algorithms.

"Big companies have enormous volumes of data at their disposal, but in order to use it to their advantage, they must first accurately evaluate the quality of the data. 'Get your data ready,' says Zoe Willis, Partner and National Data and Digital Lead at KPMG in Canada. "Without it, their path to being AI-ready will be difficult."

The actions listed below are recommended by Ms. Willis for organizations to become AI-ready:

Make a thorough inventory of all the data.
Evaluate the data's accuracy, relevance, and recentness.
Determine any current gaps in the data as well as any possible future gaps.
Analyze, find, and chart the organizational data locations.
Ascertain who can access the data and to what extent they are qualified to do so.
"Without quality data, AI algorithms are susceptible to output that is biased, incorrect, misleading and unreliable, and the consequences for businesses include errors that lead to poor business decisions," says Ms. Willis.

While most Canadian businesses are aware of the dangers of having bad data, only 44% regularly hire independent third-party experts to review or evaluate their AI algorithms for bias and errors. Of these, over half (54%) admit they are extremely concerned that their company may be making decisions based on badly designed AI algorithms.

The necessity of ethical AI



Kareem Sadek, Partner, Advisory, IT & Emerging Technology Risk Leader, states that "organizations need to adopt strong AI operating models and frameworks that include policies, practices, and tools to manage their risk in addition to having quality data."

Mr. Sadek, who co-leads KPMG in Canada's Responsible AI Framework, continues, "Organizations that want to build their AI capabilities need to have a comprehensive and robust governance plan that addresses data integrity, privacy, reliability, accountability, fairness, and security, among other considerations."

According to the report, just 43% of Canadian firms (compared to 72% of American organizations) have implemented responsible AI frameworks, and of those, 50% think their company strictly abides by them (70% of American organizations).

"This highlights a critical need for more organizations to adopt responsible AI frameworks, but also mechanisms to help stick to those frameworks," says Sadek. The author on to state that "any responsible AI framework must also incorporate monitoring, evaluation, and continuous evolution."

As demonstrated by ChatGPT, for instance, artificial intelligence is developing swiftly, therefore businesses require AI models that are not only long-lasting but also flexible enough to change with their environment. Businesses who don't take this action will eventually lag behind and become less reliable and competitive, according to Mr. Sadek. 

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