What is AI?
AI refers to the simulation of human intelligence in machines that are programmed to think and learn like humans. It can be divided into two main categories: narrow or weak AI, which is designed to perform a specific task, and general or strong AI, which is increasingly capable of intellectual tasks that a human can do. Strong AI is the future of AI. Nowadays we rely on narrow AI solutions and its fantastic evolution of Generative AI class of solution like Chat-GPT, a large language model that can understand and respond to human language.
Applications of AI in KM
KM is the process of capturing, distributing, and effectively using knowledge within an organization. AI can aid in this process by acting like a Digital Knowledge Assistant (DKA), automating the collection and organization of data, as well as providing new insights through data analysis.
One specific application of AI in KM is through the use of natural language processing (NLP) and machine learning (ML) to improve search capabilities. NLP allows computers to understand and interpret human language, while ML allows them to learn from the data they are processing. Together, these technologies can be used to improve the accuracy and relevance of search results, making it easier for employees to find the information they need.
Another example of using narrow AI for KM is using image recognition software for classifying and organizing documents. The software can automatically recognize and categorize different types of documents such as invoices, contracts, and reports, making it easier for employees to find and access the information they need.
An example of DKA is by using Chat-GPT for KM is in creating a conversational agent that can answer employee's questions in a natural language. The agent can be trained on a specific dataset related to the organization's knowledge and can be integrated with the company's intranet or internal messaging systems, making it easy for employees to access the information they need. It can even write code for you!
Another application of AI in KM is in the area of personalization. By analyzing an employee's search history and behavior, AI can make personalized recommendations for knowledge that may be relevant to the individual leading to increased efficiency and productivity as employees could be presented with knowledge even without having to ask for it.
Commercial and Strategic Factors to Consider
When it comes to acquiring AI-based solutions, there are several commercial factors that companies should understand. These include how information on how the AI model has been trained, cost, scalability, integration, data security, technical support, return on investment, vendor reputation, and flexibility. By considering these factors, companies can make an informed decision and choose a solution that will best meet their needs. But that’s not enough, because when it comes to strategic decision criteria for AI-based solutions, one key consideration is whether the “make or buy” decision. There are pros and cons to both options, and the decision should be based on the specific needs and resources of the organization. The decision may also be affected by the type of AI solution that the organization is looking to acquire.
Make:
Make is to be considered when the AI solution is targeted to the “Core” of the company’s business
Building an AI solution in-house can provide the organization with more control over the development process and the ability to tailor the solution to its specific needs and to develop a competitive advantage by creating a unique solution that sets it apart from its competitors.
However, it can be a costly and time-consuming process, and the organization may need to invest in the necessary resources and talent, such as staff and equipment, to develop the solution. It also requires a high level of expertise on the topic and a strong data science team.
For narrow AI solutions, it may be more feasible for an organization to build the solution in-house as it may not require a lot of resources, and it can be tailored to the specific needs of the organization.
On the other hand, building a strong AI solution can be more challenging, as it requires a lot of resources, expertise, and data.
Buy:
Buy is to be considered when the AI solution is targeted to the “Context” of the business
Buying an AI solution can be a faster and more cost-effective option, especially for small and medium-sized companies.
It can also provide access to a wider range of solutions and the latest technologies, which can be difficult to replicate in-house.
However, it can limit the organization's control over the development process and may not provide a competitive advantage, as other companies may be using the same solution.
Buy is to be considered when the AI solution is targeted to the “Context” of the business.
In conclusion, when considering an AI-based solution, companies should align with their business goals, consider the strategic fit in the context of the company's core competencies, competitive advantage, resource allocation, risk management, flexibility, and long-term vision.
Additionally, companies should also take into account the decision criteria of make versus buy.
By considering these factors, companies can make an informed decision and choose a solution that will provide the most value over the long-term.
It's important to keep in mind that building an AI solution in-house may provide more control and a competitive advantage, but it can be costly and time-consuming, while buying an AI solution can be faster and cost-effective, but it may limit the organization's control over the development process. Companies should evaluate their needs, resources, and goals to decide the best path for them.