AI Readiness: Five Areas Businesses Must Prepare

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AI Readiness Report Kaleido Insights

This research report identifies and examines the five fundamental areas enterprises must prepare for successful deployments of artificial intelligence. To date, enterprise preparation for AI has centered almost exclusively on data prep and data science talent. While without data there would be no AI, enterprises that fail to ready the broader organization—chiefly people, process, and principles—don’t just stunt their capacity for good AI, they risk sunk investment, jeopardize employee trust, brand backlash, or worse.

“AI Readiness: Five Areas Businesses Must Prepare for Success in Artificial Intelligence” is the first report of its kind to introduce a framework for organizational preparedness—not only of data and infrastructure but of people, ethical, strategic and practical considerations needed to deploy effective and sustainable machine and deep learning programs. The five fundamental areas businesses must prepare for sustainable AI include:

  1. Strategy
  2. People
  3. Data
  4. Infrastructure
  5. Ethics

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Report Includes

  • 54-page Research Report AI Readiness: Five Areas Businesses Must Prepare for Success in Artificial Intelligence, including real-world examples, pragmatic recommendations and best practices, frameworks to activate, and endnote resources, sourced from more than 27 research interviews
  • 11 high-resolution graphics and frameworks visualizing research findings
  • Two (2) Downloads per user

Who Needs This Report?

  • AI Leaders, Digital Strategists
  • Innovation Leaders
  • C-Suite: CMO, CIO, CDO, CPO, CXO
  • VPs, Directors leading Strategy, Product, Support, Marketing, Partnerships
  • Brands and enterprise adopters
  • Technology leaders and suppliers
  • Agencies involved in customer strategy, product development, positioning
  • Investors

Table of Contents

  1. Executive Recommendations
  2. Introduction
    1. Definition of Artificial Intelligence
  3. The Struggle for Readiness
  4. The Five Areas of AI Readiness
    1. Strategy
      1. AI Transformation is an Extension of Digital Transformation
      2. Strategic Approaches to AI
      3. Lay the Foundation for AI Governance
      4. Measuring AI’s Success
      5. Key Questions to Ask
    2. People
      1. The AI Mindset
      2. Identify Key Personae and Ready Each Group Accordingly
      3. Address AI’s Limitations & Cultural Stigma
      4. Best Practices
      5. Key Questions to Ask
    3. Data
      1. Assess Enterprise Data Strategy
      2. Ready the AI Data Feedback Loop
      3. Leverage AI for Ongoing Enterprise Learning and Knowledge Management
      4. Best Practices
      5. Key Questions to Ask
    4. Infrastructure
      1. Assess Architecture Needs and Evaluation Criteria
      2. Prepare Infrastructure
      3. AI Software Solutions
      4. AI Hardware
      5. Best Practices
      6. Key Questions to Ask
    5. Ethics
      1. Organization & Resources
      2. Bias In, Bias Out
      3. Transparency, Consent, and Data Privacy
      4. Best Practices
      5. Key Questions to Ask
      6. Next Steps Towards Artificial Intelligence
  5. Methodology
  6. Ecosystem Inputs
  7. Open Research
  8. About Kaleido Insights
  9. Endnotes
  10. Further Reading

Report Frameworks

Figure 1: Business, Market, and Societal Barriers to AI Adoption
Figure 2: The 5 Areas of AI Readiness
Figure 3: Bridging Digital Transformation and AI Transformation
Figure 4: Ready Employees with the AI Mindset
Figure 5: Key Roles in AI Programs
Figure 6: How to Measure the Success of Cultural Readiness
Figure 7: The Enterprise AI Data Feedback Loop
Figure 8: Types of Machine Learning
Figure 9: Enterprise Knowledge Management in the Age of AI
Figure 10: Architecture Considerations for AI Infrastructure
Figure 11: AI Ethics Spans Three Broad Categories

Research Methodology

This research was developed through extensive primary and secondary qualitative research methods. We interviewed 27 market influencers, vendors, and adopters between September 2017 – June 2018. We also conducted countless briefings and discussions with industry innovators in the artificial intelligence, big data, cloud and related software and hardware markets. Input or mention in this document does not represent a complete endorsement of the report by the individuals or the companies listed herein.

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