Leaders Versus Laggards In AI: Latest Findings On Generating ROI From Your AI Investments

Companies starting out should work closely with business teams to identify use cases and demonstrate AI’s worth through pilots. But the true value of AI can materialise only with widescale deployment when firms can offset their upfront costs with substantial business gains.

The gap between leaders versus laggards in AI has widened significantly in the last 6 months, even as leaders are investing big time on pilot projects to transform business teams with AI and Deep Learning. In a powerful survey finding, market research firm ESI ThoughtLab has found out APAC region leads (14.1 Billion USD) in average revenue earned through the adoption of AI applications in 2020. North America ($13.9 billion) and EU ($12.7 Billion) have also reported significant revenue growth from AI adoption.

The research shows that delivering ROI on AI can be elusive for the uninitiated and slow going even for experienced organizations. Those in earlier stages of AI adoption often see flat results. It is not until they scale AI more widely across their enterprises and become leaders that the ROI rises to 4.3%. With frequently high upfront costs in data preparation, technology adoption, and people development, it takes an average of 17 months for a firm to reach break-even and months more to generate significant returns.

AI techniques include:

  1. Machine Learning (ML)
  2. RPA
  3. Natural Language Processing (NLP)
  4. Deep Learning (DL)
  5. Computer Vision (CV)
  6. Big Data Management
  7. Intelligent Assistants / Chatbots

About 85% of companies that generate large AI returns work to ensure close collaboration between AI experts and business teams. AI leaders are better at providing non-data-scientists with AI skills. They also decentralise AI’s authority to help ensure that AI’s responsibility and expertise are distributed across their organisations.

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