This year looks to become the breakout year in Canada for artificial intelligence (AI) initiatives in the enterprise with the accelerated adoption of public cloud – particularly in Western Canada.
Why? In the fall of 2019 British Columbia amended legislation as it relates to the use of cloud computing that will enable the public sector (inclusive of crown corporations, healthcare, etc.) to pursue digital transformation programs with modern technology. Alberta, for its part, has become one of Canada’s leading AI hubs with numerous research labs and accelerators. For example, Google DeepMind, opened its first-ever international AI research office in Alberta. With this progress, Western Canada is poised for a year of innovation and digital transformation initiatives coming to fruition.
From an overarching data analytics perspective, in many of my conversations with CIOs, CTOs and senior IT Leaders across Western Canada over the past year, I found the viewpoints around the benefits of AI and machine learning (ML) to be quite consistent. Namely, the next decade will be about data-driven decision making. And, the feeling that those organizations investing time on assessing a variety of business use cases early and often will have the highest probability of success.
Even at this early stage of adoption within the enterprise, there is much to be learned from both the successful and not (as) successful attempts. It’s important for CEOs to understand that the insights and learnings gleaned from their digital transformation teams and data science teams are the foundations of experience – embrace it.
To highlight the rise of AI in the enterprise across Western Canada in 2020, along with providing insights from a variety of industries, I will be taking a holistic view and feature interviews with CIOs of provincial governments to heads of innovation in the oil & gas sector among others. We’ll tackle both the business benefits as well as the challenges many face by discussing the following topic areas:
- Business case development for data analytics initiatives
- Small vs large AI deployments
- Culture & governance considerations
- What happens when expectations of success are set too high
- The challenge with scaling data analytics initiatives
- What is considered a success? 80 per cent accuracy … 90 per cent accuracy
- Do we have too many data points? (“Perhaps we don’t have enough data” as suggested by one leader)
Additionally, from the vendor landscape perspective, I’ll look at the leading platforms helping organizations along their data analytics journey such as AWS AI Services, Databricks, Datadog, Google AI, Microsoft AI, Snowflake and ThoughtSpot among others. We’ll discuss top use cases across a variety of industries as well. For example, leveraging geospatial data for the public sector to equipment failure prediction for the oil & gas and energy sectors among others.
It will be an exciting year in Western Canada as many embark on their journey over the next decade.
Open Invitation … If you have innovative AI and machine learning initiatives that you are interested in potentially being featured for an article, you may contact me via LinkedIn to discuss further.
