With the emerging AI trend, more and more organizations are looking to implement AI solutions in their business operations but do not know if they are ready as a company or organization to adopt AI. Many factors need to be considered if an organization wants to be “AI-ready” and they can be divided into three categories: Technology, Organization, and Environment. As explained in previous blog posts, analyzing aspects around data, such as data quality, data availability, data accessibility, data storage solutions, data governance, data management, etc. is inevitable (Link to this blog article: Structured vs. Unstructured Data: Challenges and Opportunities for Language Models (hico-group.com)), but next to the technology evaluation, the organizational aspects of a company must also be evaluated and are a crucial part of a company’s AI Readiness. This blog post focuses solely on the organizational aspects that a company should have in place before implementing AI solutions in their business.
Strategy
A very important aspect of AI Readiness is to have an overall strategy and vision for how AI should be integrated into current business processes and what goal should be achieved by implementing AI into business operations. As there are different approaches to integrating AI into a company, it is important to decide how AI should be introduced into the company:
- Should AI only be used in one department?
- Should AI be used across all departments?
- What are the use cases for AI?
- What are the current challenges and pain points in the company that AI can solve?
- What are business cases regarding AI?
Answering these questions before starting the journey of introducing AI into a company or process is crucial. Additionally, having a data strategy in place that focuses on the following points is unavoidable:
- What is the state of data quality?
- Can the data used for AI be trusted?
- Is the data monitored?
For a company to be AI-ready it also needs the buy-in from top-level management. If management has not recognized the business value of AI, AI implementation can be a challenge, as management needs to allocate and enable resources to this topic.
Culture
Another decisive factor for AI Readiness is the corporate culture. The company’s employees must be open and willing to adapt to the introduction and use of AI, recognize AI opportunities, and take responsibility for data management. This willingness to adapt is an important factor in a company’s AI Readiness. Otherwise, the risk that AI solutions will not be used once implemented is very high. Even if the best AI solution is implemented in the company, this does not mean that the solution will be used by the employees. For this reason, corporate culture is a crucial component of AI maturity. Another crucial factor regarding the culture of the company is that the company has a data-driven mindset. If employees do not recognize the importance of data, there can be data quality issues that prevent AI solutions from delivering the best possible outcomes for users and pose the threat of hallucination and confusion. Since the development and implementation of AI solutions involve a lot of testing and improvement, it is important to incorporate the mindset of trial and error into the company. A company must be prepared to fail in order to learn from mistakes and improve the existing solution and not stop after the first failure. This can be practiced in a company through an agile mindset, that enables employees to test, fail, and improve in different cycles.
Competence and Knowledge
In addition to the corporate culture and strategy, it is also essential to have sufficient competence and knowledge within the company. The type of knowledge and depth of knowledge a company needs to implement AI in the company depends on the company’s AI strategy: Should AI solutions be developed by internal employees only, or will external service providers be commissioned to develop and implement AI solutions? Depending on the answer to this question, the knowledge required to develop and implement AI solutions such as Machine Learning solutions or AI chatbots with GenAI is procured internally or externally. The amount of knowledge that is required therefore very much depends on the strategy and lies between having data scientists and engineers who know how data is governed and managed within the company and receiving external help on developing AI solutions or having an entire AI team with designated AI engineers who can develop Machine Learning and GenAI solutions and identify and prioritize use cases according to the company’s overall goals. In general, in both extreme cases, the company must have knowledge of the company’s data management and governance, even if external service providers develop the AI solution for the company. If AI solutions are used internally within the company, employee training is also required to ensure the correct use of the AI application, otherwise the desired results may not be achieved.
Resources
The final topic that needs to be considered in order to be AI-ready as a company is the allocation of resources to AI. Like any other project, AI projects and AI adoption require the allocation of financial and time resources to this topic. Otherwise, a company cannot be AI-ready. Again, the dimension of resources is highly dependent on the company’s strategy. It is possible to use internal knowledge and employees or to commission external third parties to implement AI solutions.
In summary, any organization preparing to implement AI should not only consider the technological and environmental aspects of such an implementation, but equally as important would be the organizational assessment. Thus, holistic AI strategies should be developed, that take data, culture, resources, and competence and knowledge into account.
*Disclaimer: The insights on AI Readiness were extracted from interviews with subject matter experts on AI, AI Readiness, and AI Maturity.
The author: Melanie Kloppenburg – AI Consultant
