Canada: Future of Intelligence

IDC's Canada: Future of Intelligence research examines how and why artificial intelligence (AI), advanced analytics, and automation technologies will change the future of workflows, processes, and decision making. How are these technologies being used to effect business, employee, and consumer changes, such as quicker reaction times, faster product innovation, improved customer experience, and sustainable market leadership? Topics examined include Canadian AI market trends and use cases, organizational buying behavior for AI, what drives successful AI projects, and why AI initiatives fail. Technology buyers will find insights to assist in the purchasing process. Professionals working for technology vendors will find quantitative and qualitative research useful for go-to-market and sales insights, marketing tools development, sales targeting, and strategic planning.

Markets and Subjects Analyzed

  • Artificial intelligence and analytics use cases and solutions
  • User organization buying behavior trends and expectations
  • Relevant coverage of key AI and analytics software vendors
  • Artificial intelligence and analytics software adoption and usage insights
  • Artificial Intelligence use cases and project success and failure factors

Core Research

  • Canadian Market Forecast
  • Canadian Market Shares
  • Canadian User Organization Case Studies and Vendor Profiles
  • AI Adoption and Implementation in Canada
  • AI Implementation Spectrum Maturity in Canada
  • Analytics Adoption and Implementation in Canada

In addition to the insight provided in this service, IDC may conduct research on specific topics or emerging market segments via research offerings that require additional IDC funding and client investment.

Key Questions Answered

  1. What is happening in the Canadian AI and advanced analytics markets, and where are we on the adoption curve?
  2. What are the operational goals of Canadian user organizations that are adopting AI, advanced analytics, and automation technologies?
  3. What processes and workflows are being targeted for AI deployment? And are these AI-specific projects, or are they part of larger digital initiatives?
  4. What are the specific causes for AI POCs and initiatives failing? What drives successful AI projects?
  5. How much of a barrier is data maturity for Canadian AI adoption, and how should vendors structure services and offerings to address this?
  6. What are key buying criteria for AI/ML platforms and data warehousing/analytics platforms/solutions?

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Meet the Experts
Warren Shiau

Research Vice President, AI & Analytics