Readiness aspects:
- People readiness
- Data readiness
- Infrastructure readiness
- Leadership readiness
- Project readiness
Business Organization for AI readiness
AI Readiness Self-Assessment
Project readiness
| Best Practices | Description |
| Learning & sharing sessions | Conduct workshops, lunch meetings, brainstorming groups, thought exercises, etc. with the aim of educating employees about AI in the context of their work. |
| Do not overemphasize AI | There is a risk of emphasizing AI too much. If it becomes too much, there is a risk that employees backfire, as people feel ownership of their work and concern that it may change |
| Befores & afters | Valuable to show both the value and the risks of AI implementations, be transparent. Show both before and after, analyze prejudices, impact of implementation in terms of environment, costs, end user experience, etc. |
| (S)low-hanging fruit | Empower employees to identify low-hanging fruit in the areas much time is spent finding things, people or other business assets, where AI could provide a much-needed assist. |
| Internal champions | Cultivate in-house experts by offering and encouraging employees to volunteer and participate in AI software development. Can receive influential input and optimize the AI process. |
| Communicate about the use of AI | Train both employees and AI systems to communicate about the use of AI in the application, do not hide the AI recommendations and obtain transparency to involve more people. |
| Deploy programs | Include skills agility, learnability, creativity and emotional intelligence by deploy programs to increase employees' professional development and skills. |
| Invest in everyone | Train all employees to understand AI, whether directly or indirectly influenced by current initiatives, given the lack of AI experience and the need for diversity. A first priority should be to invest in developing existing employees through training. |
Data readiness
- Data.AI is only as good as the data upon which it is built, and its appetite for data is voracious. Design a data governance system that includes engineering and security. Data governance should include rules for sourcing, accessing, and quality management.
- It is essential to organize company data and information, to enable east access to data from multiple sources.
- Data and information should be structured and in a machine-readable format, such as business taxonomy and metadata schemes.
Infrastructure readiness
- Data Sources - Diverse and extensive data sources are used such as events, metrics, logs, different job data, tickets, monitoring, etc.
- Big Data - Aggregation of IT data for historical analysis and real-time reaction and insights.
- Computation and Analytics - Enable to generate the new data and metadata from given existing IT data:
-
- Eliminate noise
- Identification of patterns and noise
- Isolate probable causes
- Expose underlying problems
- Artificial Intelligence Algorithms - Apply computation and algorithms efficiently and appropriately to expertise the machine and get desired outcomes.
- Unsupervised Machine Learning - Automatic alteration and creation of new algorithms based on the output of the algorithmic analysis.
- Introduction of new data into the system
- Visualization - Presenting insights and recommendations in an easily consumable way.
- Automation with Artificial Intelligence - Automatic identification of issues with the use of outcomes obtained from analytics and machine learning.
Leadership readiness
- The company should make sure there is an AI leader; else should identify this person (from the organization or external);
- The AI leader has to be a senior professional;
- The AI leader should create, jointly with the management, the company vision and should inspire the whole organization;
- The AI leader should work with individuals and teams to discover some of the organization’s biggest problems and opportunities.
People readiness
- Identify AI leader and team;
- State-of-the-art review of AI in the business sector;
- Thorough analysis of competition with regards to AI readiness and plans – if available;
- Landscape of available data and strategy for data acquisition;
- Deployment AI infrastructures;
- Continuous monitoring and optimization.
References
- Pringle, T., & Zoller, E. (2018). How to Achieve AI Maturity and Why It Matters. Ovum.
- Groopman, J. (2018). AI Readiness; Five Areas Businesses Must Prepare for Success in Artificial Intelligence. Kaleido Insights.
- Zhu, Y.-Q., Corbett, J., & Chiu, Y.-T. (2020). Understanding employees’ responses to artificial intelligence. Organizational Dynamics, 1-10.
- Jagreet Kaur, Artificial Intelligence in IT Infrastructure Management, 20 September 2021
