Discover the path to AI success

  • 10 minutes

Now that you've learned about the basics of an AI-centric organization, it's important to understand that AI adoption is a journey. In their collaboration and discussion with business leaders, Microsoft is discovering insights on how organizations can achieve AI success.

Note

For this purpose, Microsoft has developed a leader's guide to build a foundation for AI success.

This model is based on five pillars that drive organizations to AI success:

  • Business strategy.
  • Technology strategy.
  • AI strategy and experience.
  • Organization and culture.
  • AI governance.

In the following video, Jessica Hawk, Corporate Vice President of Azure Data, AI and Digital Applications and Innovation Product Marketing, explains in detail this model and its five pillars.

This whitepaper includes a model of the stages of AI success. This five-tiered chart is a tool to help organizations take AI to the next level and evaluates their AI maturity.

  1. Exploring stage: Companies at this initial stage of AI adoption are just starting their AI journey yet. They're still learning about AI and experimenting with it in some parts of the organization.

  2. Planning stage: Organizations at this stage are actively assessing, defining, and planning an AI strategy across the company.

  3. Formalizing stage: At this point, companies are formalizing, socializing, and executing on AI strategy across the organization. These AI initiatives take place in multiple business units. AI is starting to generate value.

  4. Scaling stage: Organizations are now in position to think bigger. AI initiatives deliver both incremental and new value across the company.

  5. Realizing stage: At this final stage, AI achieves consistent AI value across the organization and in multiple business units.

Diagram that shows the stages of AI success: exploring, planning, formalizing, scaling, and realizing.

However, we've experienced an enormous AI acceleration during the last few years. Great breakthroughs in generative AI and premade models, such as the large language models (LLM) offered by OpenAI or Bing Chat AI, have greatly disrupted the field. This new context has two major implications:

  • Need to be up to date: Now, even mature companies need to reinvent themselves and adopt new waves of AI to avoid losing their competitive edge. Their AI strategy must reflect and leverage the impact brought by recent technologies.

  • Mainstream AI: Generative AI has changed the rules of AI adoption by empowering business users at an unprecedented level. It might be easier than ever to implement AI in business. Many companies are working hard to rank higher in the maturity assessment model.

Now that you’ve considered various aspects of what it means to have an AI-ready culture, how to assess your organization’s AI success, and prepare for change, let’s wrap up everything you’ve learned with a knowledge check.