Element AI's Acquisition by ServiceNow is Proof Canadian Industrial Policy on AI Needs to Change

Read insights on ServiceNow's recent acquisition and how this move can start a conversation on key elements that can help improve Canada's industrial policy on artificial intelligence

By: Warren ShiauResearch Vice President, AI & Analytics

IDC's Point of View


ServiceNow is a "perfect fit" for acquiring Element AI. ServiceNow has built a US$3.5 billion annual revenue base by selling process and workflow orchestration/automation software in key areas of the business where companies have operational metrics and seek process and workflow efficiency (IT, customer service and experience, HR, etc.). Element AI's technical talent is a sure fit basis for a Service Now technology center focused on developing AI augmentation for process and workflow digitization and automation.

TechCrunch reports it has heard from multiple sources the deal for full acquisition of Element AI is US$500 million, substantially below the company's US$600 million to $700 million valuation when it last raised money in September 2019.

What does the acquisition of one of our country's most prominent homegrown AI stars tell us about Canada's ability to compete in the AI future? IDC believes it is proof AI talent and tools alone are difficult to commercialize, and Canada needs to modify its industrial policy on AI.

Canadian industrial policy on AI focuses on education – producing data science graduates. This is of primary importance to organizations that plan to build data science departments and develop algorithms in-house. It does not help companies like Element AI commercialize their work.

The flaw in focusing on producing data science graduates as a policy priority lies in the assumption "AI sells itself" and producing data science graduates is the critical enabling factor for supporting a viable Canadian AI industry. IDC Canada's research contradicts this view:

  • The reality of the Canadian market is that most organizations will not establish their own internal AI-development or data science teams. Instead, they will adopt "out of the box" or "building block" algorithms supplied by orchestration/automation, analytics, and business application vendors as low-code/no-code software that LOB personnel or enterprise developers can deploy

  • What organizations need to take advantage of AI is the expertise to examine existing processes and workflows and determine which ones present the best opportunities for digitization and how to implement digitization and automation (and augment it with AI) effectively

If Canadian AI companies are going to be more than development body shops or talent pools for worldwide acquirers, Canada's national strategy and industrial policy for AI needs to start incorporating marketplace realities:

  • Operational process and workflow efficiency drive AI solution implementation: successful AI implementations are based on use cases with known operational impacts

  • Industry/sector-specific operational process and workflow expertise drives AI use case identification

  • Turning AI proof of concepts into implementations depends on using this knowledge to guide solution development and sales/marketing efforts

In all of IDC Canada's research, AI commercialization depends on AI augmentation of process and workflow digitization and automation. Because operational process and workflow efficiency drive AI solution implementation, Canadian AI industrial policy needs to focus on process and workflow digitization and automation skillsets at least as much as on the development of data science skillsets.

Without government policy support to develop critical commercialization skillsets, Canadian AI companies face more challenges than necessary in becoming viable international competitors – that's the true lesson for Canadian government AI strategy and policy.

 

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