In today’s economic climate, educational institutions are seeking technology solutions to help them unlock big data and effectively utilize its potential. Many vendors claim to offer data-driven solutions powered by “support automation” or “artificial intelligence” technology. How can you verify if a firm marketing a product truly offers adequate support automation or relevant AI applications?
Some may struggle to comprehend the distinctions between terms like “deep learning,” “machine learning,” and “artificial intelligence.” So, consider asking these questions when performing your evaluation:
Can the vendor tackle specific issues?
Machine learning is one of the most accurate and effective methods for issue resolution. However, before machine learning can be used to address a problem, there must be one. When looking for machine learning technology, seek companies that specialize in a particular area or use case.
Machine learning algorithms, on the other hand, struggle with open-ended jobs. Therefore avoid vendors who make promises to mitigate any issue.
How does it work?
To get the most out of an AI solution, you must first comprehend how data, AI, and integration processes interact. An accurate outcome of a machine learning product will never be given if the appropriate type or volume of data is not available. As a result, knowing the data required to train an AI system is critical in assessing if it is a suitable fit for higher education.
Do you have access to the data?
Once the appropriate data streams have been identified, the next step is to determine if the data is available. Is it structured so that it can be readily included in an artificial intelligence platform?
Gathering big data does take a significant amount of time and money if the data is not publicly available. Before purchasing a support automation and AI tool, your university should consult with its IT or AI professionals to ensure that the necessary data is accessible. Why? Well, implementing support automation and AI solutions may be problematic since some forms of data are not handled or saved.
What steps are involved in integrating and deploying this solution?
It’s vital to have an understanding of the deployment process.
Can the vendor provide references?
Interviews with current or prior customers are essential for gathering information regarding a product’s installation and integration (i.e., previous buyers of this exact solution). These individuals have most likely dealt with similar issues in the past and may provide valuable client insight.
Is the company willing to conduct a proof-of-concept study?
Many platforms claim to have high accuracy rates, making it difficult to determine which solution can solve your problem and provide meaningful insights. A proof of concept project with specific objectives is an excellent way to put your hypothesis to the test.
Is the tool compatible with your current systems?
Will it integrate with Windows, macOS, Salesforce, Canvas, and other platforms your institution uses regularly?
What are your support options?
It is critical to understand how the original vendor of a tool and its user and developer communities maintain, update, and support it. Eventually, you will need assistance from the seller or the community at some point throughout your AI and support automation journey.
In some cases, the manufacturer or user community no longer supports the tool or that the documentation is out of date.
What are the costs?
Naturally, the price of the platform will factor into the final decision. Fees for licensing and support must also be addressed. While there are several free or low-cost tools available, they all need the aid of a third party. Any additional expenditures can be incurred due to increased training and time expended to adequately comprehend and address technology-related difficulties.
Here are a few more critical questions to ask: