AI Readiness Assessment

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AI Readiness Assessment: Improve Your Organization’s Odds of Succeeding with Artificial Intelligence, by Dr. Scott Burk

Improve your organization’s odds of succeeding with artificial intelligence.

Topics

Introduction


SECTION I―The Essentials


Chapter 1: Organizational Commitment

Will your organization have the discipline to do it?

An important note for leadership

Leader and management roles

Important questions


Chapter 2: An Overview of the Assessment Process

  1. Organizational commitment
  2. Project team structure
  3. Top-level education and assessment customization
  4. Organizational communication
  5. Questionnaires and worksheets completed

Business goals and initiatives

Leadership assessment

Culture assessment

Operations and structure

Industry and market assessment

People assessment

Data assessment

AI technology assessment

  1. Assignments aggregated and analyzed
  2. Executive report and presentation
  3. Remediation and projects start

The six foundations for AI success

Business knowledge

Data knowledge

AI and analytics knowledge

Technology stack

Culture

People

Chapter homework


Section II―The Assessment


Chapter 3: Business Goals and Initiatives

Problems/opportunities/business case strategy

Chapter homework

Projects

Plant nursery improves revenue and gains customers

Chapter homework


Chapter 4: Leadership Assessment

Why leadership is important to AI project success

Organizational alignment

Leadership assessment for your organization

  1. Team selection
  2. Team education
  3. Brainstorming and open exchange
  4. Scoring assessment and gap identification
  5. Share findings with oversight committee for aggregation

Questions for discussion

Communication

Independence and trust

Trust and positive relations

Willingness to reward the learning experience

How do you handle failure and setbacks?

Alignment and collaboration

Commitment

Scoring assessment and gap identification

Identification of strengths and weakness / potential failure points


Chapter 5: Culture Assessment

Why culture is important to AI project success

Foundations of the new AI and analytics culture

Culture’s role in AI projects

Culture lessons from the AI front

Target business problems

Before the AI solution

Potential of an AI solution

Cultural strengths that led to project deliverable success

Culture assessment for your organization

  1. Team selection
  2. Team education
  3. Brainstorming and open exchange
  4. Scoring culture – assessment and gap identification
  5. Share findings with oversight committee for aggregation

Questions for discussion

Flexibility / adaptability and ability to change

Community, team strength, and innovation

Loyalty, commitment, and risk taking

Learning, improvement, and innovation

Scoring culture – assessment and gap identification

Identification of strengths and weakness / potential failure points


Chapter 6: Operations and Structure

Why operations and structure are important to AI project success

AI projects for human-in-the-loop operations

AI projects for human-out-of-the-loop operations

Structure versus culture

Operations and structure assessment for your organization

  1. Team selection
  2. Team education
  3. Brainstorming and open exchange
  4. Scoring assessment and gap identification
  5. Share findings with oversight committee for aggregation

Questions for discussion

Access to people across functions, roles, and hierarchy

Repetitive environment or creative environment?

Machine-based work or human-based work?

Novel environment or conventional environment?

Scoring operations and organizational structure – assessment and gap identification

Identification of strengths and weakness / potential failure points


Chapter 7: Industry and Market Assessment

Why industry and market are important to AI project success

Industry affects regulation, compliance, and more

Industry and market affect AI projects

Industry and market assessment for your organization

  1. Team selection
  2. Team education
  3. Brainstorming and open exchange
  4. Scoring assessment and gap identification
  5. Share findings with oversight committee for aggregation

Questions for discussion

Industry standards and processes

Industry and market education and innovation

Ability to respond to outside forces

Scoring industry and market – assessment and gap identification

Identification of strengths and weakness / potential failure points


Chapter 8: People Assessment

Why people are important to AI project success

The importance of domain knowledge

Community of practices strengthen data and AI literacy

True benefits of diversity

People assessment for your organization

  1. Team selection
  2. Team education
  3. Brainstorming and open exchange
  4. Scoring assessment and gap identification
  5. Share findings with oversight committee for aggregation

Questions for discussion

Skills and knowledge

Aptitude / willingness to learn

Open mindedness

Emotional intelligence

Desire to make positive changes

Drive, tenacity, and willingness to succeed

Team attitude

Scoring people – assessment and gap identification

Identification of strengths and weakness / potential failure points


Chapter 9: Data Assessment

Why data is important to AI project success

Data for AI projects

Types of data

Locations of data

Access to useful data

Customer service

Data assessment for your organization

  1. Team selection
  2. Team education
  3. Brainstorming and open exchange
  4. Scoring assessment and gap identification
  5. Share findings with oversight committee for aggregation

Questions for discussion

Data as a strategic asset

Investment made in data technology

Investment made on human support of data

Data quality and consistency

Data availability to personnel

Data security, governance, and compliance

Scoring data – assessment and gap identification

Identification of strengths and weakness / potential failure points


Chapter 10: AI Technology Assessment

Why an AI technology assessment is important to AI project success

The importance of partner relationships

Buying a packaged analytics or mobile app solution is an AI project

Call center optimization

AI technology assessment for your organization

  1. Team selection
  2. Team education
  3. Brainstorming and open exchange
  4. Scoring assessment and gap identification
  5. Share findings with oversight committee for aggregation

Questions for discussion

AI technology as a strategic asset

Investment made in AI technology

Investment made on human support of AI technology

AI tool availability to personnel

Scoring AI technology – assessment and gap identification

Identification of strengths and weakness / potential failure points


SECTION III―Project Selection, Remediation, and Kickoff


Chapter 11: Assignments Aggregated and Analyzed

Example aggregation of mapping tools

Strengths and weakness / potential failure points


Chapter 12: Executive Report and Presentation

Report and presentation team

  1. ARA report creation
  2. ARA presentation preparation
  3. ARA presentation meeting

Chapter 13: Remediation and Projects Start

Next steps

Three major steps with recommendations

Future state


Chapter 14: Parting Thoughts

Managers struggle to determine whether they have the right people, knowledge, data, and tools to compete in a new business landscape―a landscape revolutionized by AI. Although most leaders ‘hope’ that they have the ‘right stuff’, hope is not a strategy. Organizations must critically assess where they are currently and what gaps they must fill to be competitive in this new environment. 

Through questionnaires and assessments covering eight critical dimensions, this book will help you build the foundation for a successful artificial intelligence program.

About Scott

Dr. Scott Burk has worked with companies of all sizes and industries for over 30 years. He has held executive and VP roles as well as hands-on positions in startups to Fortune 50 enterprises. He is the author of six books and has taught at several universities, including Baylor, Texas A&M, SMU, and CUNY SPS. 

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