Creating value with AI in B2B SaaS – practical examples and tips
How do you create value with AI in your SaaS solution? Blinqx CAIO Ynze Sipkema and Blis Digital partner Arco van der Velde love to spar about it! From their passion for technological development, they share practical examples and insights, benefits and dilemmas when using AI in B2B SaaS.
In this blog, they list the three biggest benefits of AI in a SaaS solution, including some crucial tips for implementing it.
3 benefits of AI in your SaaS solution
Arco explains how Blis Digital and Blinqx work closely together to deliver technology innovations. “We help customers optimize AI models, classify documents and validate data to achieve huge efficiencies. By automating repetitive tasks, companies not only save time but also improve the accuracy and quality of their services.
- Predictability
AI can analyze data to establish patterns and make predictions about future events.
- Automation
Automating repetitive tasks, such as data entry and report generation, frees up time for employees to focus on more value-creating work, such as consulting.
- Scalability
AI can help scale your SaaS solution by automating processes and minimizing manual operations.
Ynze adds: “We have a central AI team dedicated to GenAI. With this, they create efficiency modules for our SaaS products for multiple industries, including insurance, legal and mortgage.
Lawyers work a lot with text-related cases. At the start of a case, a lawyer must go through all the documentation and create a timeline. By automating this process, lawyers save a lot of time and increase accuracy.
For claims handlers (insurance), we applied AI to quickly find out whether a claim is valid. This allows the advisor to inform his client in a shorter time what the deductible is, and/or what communication is convenient in this.”
3 tips for implementing AI in your SaaS solution
Think about your data
How do you make sure the output is representative?
Arco: “We need to realize what responsibility we have in the use of data in AI algorithms. Who is liable and to where does our responsibility extend? There is a responsibility for us (as SaaS players) to train those super powers we get with AI. So that it is not baised. For example, your programmers can already create a bias in the absence of diversity in the team. How can you mitigate that? You have to be aware of that before you start developing a solution.”
Define added value
What’s in it for your customer?
Ynze: “Realizing business value with AI requires huge investments. If you start using AI, but have not first looked at what value you really want to bring to your customer with it, it makes little sense. So market knowledge is essential here: where is a sector in the adoption phase of technological innovation, and which parties are leading the way? We bring this together at Blinqx by linking our AI expertise to our market knowledge and the customer’s problems. Then you make it tangible and understandable. And you immediately make the barrier to investment a lot lower.”
Timing is essential
When is the right time to bring your AI solution to market?
Arco: “Realize that the market is not always ready for your innovations. For example, we have applications that work and could be implemented technically, but then there is sometimes the question of whether laws and regulations allow you to use that data in that way. You have to be critical of that.
And in addition, is the customer ready to apply the developments you can realize? We often organize workshops with the customer to see what data sources are already there, and how you can use AI. Customers have to trust it enough to want to invest in it. You have to be able to bring them into that.”
