Human-Centric, Ethical, and Practical AI

Augment the power of your organizational DNA through machine learning and artificial intelligence.

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Explore, Expand, and Evolve Your Organizational DNA Through AI

Getting to “Go”

Get ready to start, optimize, and operationalize AI with human-centric use cases.

Strategize & Govern AI

Start aligning data and security strategies and approach AI ethically.

Plan a Proof of Concept

Prioritize and plan your first purpose-driven AI use case.

Architect AI

Build an architecture that effectively supports practical AI.

Select Technology

Select the right tools and technologies.

Develop & Deploy

Design, develop, validate, and deploy AI use cases.

Champion & Communicate

Collaborate, communicate, and co-create competitive advantage.

Human-Centric, Ethical, and Practical AI
Human-Centric, Ethical, and Practical AI

Getting to “Go”

When making an AI go/no-go decision:

  • Understand what AI really means for your organization and how it can solve your business problems.
  • Learn what others are doing in your industry to leverage AI technologies for competitive advantage.
  • Define the use cases that maximize the value of your AI investment.
  • Ensure that the organization is ready for AI.

Strategize & Govern AI

  • Make sure that your data is ready for AI.
  • Align your data and AI strategy with your business goals and objectives.
  • Define AI and data governance operating and resource models.
  • Align with the analytics strategy.
  • Ensure you take an ethical approach to AI and data.

Plan a Proof of Concept

  • Ideate and define AI use cases and choose the first AI proof of concept.
  • Identify a specific problem where the AI solution will meet business needs and enable business and technology transformation.
  • Create the business case for AI.

Architect AI

  • Build your target-state architecture from predefined best-practice building blocks. Using architecture building blocks will speed up the architecture decision phase.
  • The success rate of AI initiatives is tightly coupled with data management capabilities and a sound architecture.

Select Technology

  • Select a technology platform that meets your data science, machine learning (ML), and AI requirements.
  • Select an off-the-shelf AI tool.

Develop & Deploy

  • Start with the proof of concept.
  • Design and develop your first AI/ML solution.
  • Test, deploy, and validate.
  • Build your data, data science, ML, and AI operations practices.

Champion & Communicate

  • Create a change management strategy to ensure adoption across the enterprise.
  • Use talent management to drive the success of AI initiatives and help with the culture shift.
  • Create a communication strategy.

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