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What Questions to Ask AI Solution Providers
Markus Bernhard, Chief Evangelist at Obrizum, and Paige Chen, CEO of Two Three Solutions, present five key questions for vendors in their article for The Learning Guild. They also share recommendations on what to look for when evaluating the answers. In this article, we will briefly outline the main ideas.
The first question concerns the types of artificial intelligence. There are two main types: narrow artificial intelligence and general artificial intelligence. Narrow artificial intelligence, also known as specialized artificial intelligence, is designed to perform specific tasks, such as speech recognition, image processing, or playing chess. General artificial intelligence, on the other hand, is capable of performing any intellectual tasks at a human level, which makes it more versatile, but such AI has not yet been created. Understanding these types is important for further study and application of artificial intelligence technologies in various areas of life and business.
- Generative AI. It allows for the creation of new content: for example, it is used to develop courses and training scenarios, and to prepare educational materials (such as infographics and lists). It can also serve as a virtual conversational partner in chatbots.
- Predictive AI. It is designed for data processing and analysis and is used, accordingly, in adaptive learning systems, content management, personalized user recommendations, or, for example, assessment.
This question plays a key role in determining the necessary details for further analysis.
The question of what data and methods were used to train the neural network directly affects the quality of its results. For generative artificial intelligence, it is critical that the training process is based on reliable, verified, and up-to-date information. This ensures not only the high accuracy of the generated data but also its compliance with modern standards and requirements. The quality of training data is a fundamental factor determining the effectiveness of neural networks in various fields.
Artificial intelligence may require additional training specific to a specific company, its products, and services. It's important to determine what data is needed and in what volume to achieve the best results. You should also consider how to load this data to effectively train the model.
For predictive AI to work effectively, you need to determine the optimal amount of information available. Predictive analytics requires significant amounts of data, and if you don't have enough, implementing predictive AI models will be premature. Ensuring sufficient data is key to achieving accurate and reliable predictions.
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Artificial Intelligence in Education: Analyzing Practical Examples
Artificial intelligence (AI) is increasingly being implemented in educational institutions, transforming traditional teaching and management methods. The use of AI in education opens up new opportunities for students and teachers, improving the quality of the educational process. In this context, it is important to consider real-world examples of AI application and its impact on learning.
One of the most common uses of AI in education is adaptive learning. AI-based systems can analyze student progress data and offer individualized learning plans, taking into account the unique needs of each student. This is especially relevant for students with different levels of preparation.
Furthermore, AI technologies are actively used to automate administrative processes. Chatbots and virtual assistants help process student requests, providing information about courses, schedules, and procedures, which reduces the workload for teachers and improves interaction with students.
Another important aspect is the use of AI to analyze large volumes of data. Educational institutions can use analytical tools to evaluate the effectiveness of educational programs and identify problem areas, thereby facilitating continuous improvement.
Thus, artificial intelligence is becoming an integral part of modern education, offering innovative solutions and approaches. Real-world practical examples demonstrate how AI can improve the quality of learning, make it more accessible and personalized, and optimize the management of educational institutions.
Each system has its strengths and weaknesses. The GPT model, for example, demonstrates good results on biology exams, but its performance in physics and chemistry is significantly lower. This is due to both the algorithms and the nature of the subjects themselves. This example highlights the importance of discussing the features and limitations of neural networks with suppliers. Understanding these nuances will help use technologies more effectively and adapt them to specific tasks.
The authors of the article emphasize that if artificial intelligence is offered as a universal solution to all a company's problems, this is a serious signal of potential risks. The reality is that AI is not a panacea, and its use must be informed and targeted.
Understand where the data needed to train and run the AI will be stored. Markus Bernhard and Chen Page identify two main options: on-premises storage and cloud solutions. On-premises storage involves using your own servers, which provides greater control over the data but requires significant infrastructure and maintenance costs. Cloud solutions allow you to use third-party resources, which simplifies data access and scalability but may raise concerns about security and privacy. It is important to carefully evaluate these options to choose the most appropriate solution for your project.
- Global. An example of a global instance is ChatGPT: while you interact with the neural network, it learns from your interactions, and the information you feed into it can be stored and used as output for other users. Therefore, it is best not to upload sensitive or personal information to such systems.
- Local. The developer essentially creates a separate "version" of AI for each client without connecting to any "centralized" system. This means the vendor will deploy separate instances of their AI for each client without duplication or connecting to or transmitting data to any "centralized" AI system.
The vendor can provide its own content, which will be accessible to all clients. It is important to understand how this content will be integrated into existing systems, as well as where it will be stored. These issues are a logical continuation of the discussion on content integration and management.
The final question remains: who will manage the artificial intelligence and interact with the data? For example, if additional training of neural networks or changes in the company structure are required, who will make decisions and how will this be implemented? How often will updates be performed, and how long will they take? Which tasks can be handled internally, and which require the intervention of the vendor's technical support?
Leading technology providers often use clear terminology and define user profiles, such as learner, user, administrator, and super administrator. It is important to understand these aspects to better understand how the technology will be used for each profile. This will also help organize the distribution of roles and responsibilities after the system is launched and during its configuration. Understanding these nuances allows for effective implementation of technologies and optimized workflows.
The first person you speak with may not have all the necessary information, and this is completely normal. It is important to find a professional team or experts willing to engage in an in-depth discussion. This will allow you to confidently choose the AI-powered technology that best meets your needs and expectations. Discussing with experienced professionals will help you better understand the available solutions and how they apply to your business.
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Josh Bersin discussed the next generation of corporate training platforms, highlighting their capabilities and benefits. Modern solutions in this area are becoming more adaptive and personalized, enabling organizations to effectively develop employee skills. Platforms integrate advanced technologies such as artificial intelligence and data analytics to create customized learning paths. These changes help companies not only enhance employee skills but also improve overall productivity and competitiveness. Importantly, new platforms emphasize continuous learning and fostering a culture of self-improvement, which is key to successful business growth in a constantly changing environment.
How to Choose an AI Platform
While the previous questions focused on technologies and their operating principles, choosing a specific solution raises even more questions. Stella Lee, Chief Learning Strategist at Paradox Learning, has developed a comprehensive checklist to help you determine whether a particular product is right for your needs and whether it's worth choosing. She organized her questions into five key areas, allowing for a more precise assessment of the potential and effectiveness of various solutions.
The first section, dedicated to solution relevance, helps assess how well the chosen tool meets the company's goals and objectives. It's important to determine whether a potential acquisition aligns with the organization's educational goals and whether it can help fill gaps in employee knowledge and skills. Analytics capabilities should also be considered: can the neural network provide real-time feedback and provide users with training recommendations? These aspects play a key role in the decision to implement an educational tool, as they determine its effectiveness and alignment with business needs.
The expert emphasizes the importance of having an evidence base for specific AI solutions. This is critical for assessing their effectiveness and reliability. Without substantiated data, it is difficult to judge the feasibility of using such technologies in various fields.
While it is too early to talk about the long-term effectiveness of artificial intelligence, it is recommended to request practical examples, guides, studies, or case studies from other clients for in-depth analysis. Stella Lee recommends focusing on real-world results and experience to assess the potential of artificial intelligence in your business.
The choice of solutions should meet training goals and ensure ease of use. It is important to evaluate how intuitive and easy-to-use the AI interface is. It is also important to determine whether employees will need additional training to work effectively with the system, as well as what training formats the provider offers. This may include online courses, webinars, or hands-on training, which will significantly improve your level of technology proficiency.
Usability issues extend beyond UX design to workflows. Generative AI, for example, can suffer from hallucinations—a situation where it provides fictitious information when real data is insufficient. Therefore, it is critical to determine the percentage of such hallucinations, assess the overall reliability of the results, and understand how long it takes the neural network to generate a response. These aspects directly impact the effectiveness and reliability of using AI in various fields.
Stella Lee recommends considering the prospects for increasing the volume of training content. It is important to evaluate the ability to train the neural network based on new data and adapt the tool to the needs of different user groups, such as beginners, remote workers, or teams with varying levels of experience. These steps will help create more effective training solutions that will meet the requirements of different user categories.
This category of issues includes all aspects of security and technical support. It is important to find out what measures the provider takes to protect against cyberthreats and how data is stored. It's also worth clarifying how technical support is organized and how quickly you can get the help you need. Effective data protection and prompt support play a key role when choosing a service provider.
It's important to find out whether the developer adheres to the principles of data protection, transparency, and fairness. To what extent is the company prepared to take responsibility for its products? These aspects directly impact its customer policy and build trust in the brand. Transparency in the company's actions, its willingness to take responsibility for the quality and safety of the product, and respect for customer rights are key factors for successful cooperation.
The expert emphasizes the importance of verifying whether neural networks are truly trained on non-discriminatory content, as this can negatively impact the learning process. Unlike humans, algorithms are unable to independently evaluate the data they receive, which increases the risk of offending certain groups of people due to a lack of attention to the quality of the information. Furthermore, it is important to determine whether the experience of employees and their feedback are taken into account when improving artificial intelligence. This can significantly impact the efficiency and safety of neural networks.
Check out additional materials:
- How neural networks can simplify the work of L&D
- 4 ways to use AI in corporate training now
- What will happen to staff training: "Trainings will become a rarity"
- Why neural networks will not replace L&D specialists (at least for now)