How Low-Code brings your AI idea to life

How Low-Code brings your AI idea to life

You can’t open your LinkedIn feed without encountering the latest article or infographic explaining why AI is relevant for your industry. Whether it’s personalizing customer experiences in retail, ensuring quality control in manufacturing, or enhancing risk assessment and underwriting in insurance, numerous examples demonstrate AI’s potential impact. However, although many people talk about AI, just few companies know how to implement effective solutions. Implementing AI solutions that create real impact for your organization remains complex for several reasons.
  • The first reason: Technological bottlenecks often slow down implementation. Such as the fact that valuable data is often unavailable, inaccessible, or not integrated. A common challenge in the insurance industry is siloed data. As a result of mergers and acquisitions, preventing insurance companies from implementing valuable AI solutions. Making this data available can be time-consuming, especially considering the shortage of data engineers, a challenge many organizations already face.
  • Second reason: It can be difficult to get the right team together. Many organizations experience a disconnect between those with business knowledge and technological expertise. Bridging this gap is essential, as business users sometimes struggle to grasp the technology, while data engineers and scientists may lack insight into the specific business needs required for feasible and impactful solutions. Look at retail, where the majority of the end users is working in shops or distribution centers. Making structured communication and collaboration with IT engineers a challenge.
  • Third reason: Organizations often struggle to bring AI into their operational teams. While the board members urge their organization to “do something with AI”, operational teams are focused on implementing short term business value. The highest priority tickets often look at low-effort and high-impact. Meanwhile, the impact and effort of first-time AI projects is unclear at best. With development teams not having the knowledge or time to incorporate AI into their product backlog, it comes down to a few individuals driving the topic.
Given these challenges, it’s unsurprising that organizations hesitate to embark on AI initiatives.

Why low-code excels in AI innovation

Low-code entered the software development sphere to democratize the process and increase accessibility of custom software. Its visual nature simplifies development, enabling business users to better understand the process. Additionally, platforms like OutSystems and Mendix accelerate development, leading to faster time-to-market. While most low-code platforms showcase their large-scale use cases, such as migrating complete ERP systems or supporting large citizen development initiatives, low-code is particularly compelling for experimentation and innovation, including AI. Ease of Integration Low-code platforms like OutSystems and Mendix offer numerous integrations out-of-the-box, simplifying the combination of multiple data sources. This is crucial for AI solutions, as it enhances data availability. Moreover, since low-code developers often have full-stack capabilities, integrated app development can be handled by a single individual. Integration with various AI agents is also seamless, with both OutSystems and Mendix offering out-of-the-box connections to major AI providers like AWS Bedrock, Snowflake, and Azure’s AI Services. Rapid Development Developing a low-code solution is 4-7 times faster than traditional programming languages. This accelerates time-to-market and reduces the risk associated with experimentation. Faster, less resource-intensive coding allows for more disposable solutions—if a concept fails, fewer resources are wasted. Collaboration between Business and IT The visual nature of low-code facilitates alignment between business and IT, enabling quick feedback loops and iterative development. And since one developer can already implement a proof of concept in a matter of days, only one additional business user is needed to run a first experiment.

Getting started with low-code for AI innovations

Even though low-code can speed up the delivery of your first AI project, there are still many ways to get started. To make a first project manageable with both technology and stakeholders it could be advised to start with a Narrow AI or GenAI solution. Narrow AI performs a one-sided task like image or text recognition, for example to validate documents, find data outliers or track shipments. Solutions with Narrow AI often require a smaller data set, making it easier to implement in environments with a lower data maturity. Because the tasks are like people’s day-to-day work, the quality of the outcome is easier to judge by end-users. This creates a broader understanding and recognition for the solution. A good first implementation could also be using Generative AI. Many of your colleagues already use GenAI tools like ChatGPT and Midjourney. Implementing such solutions in their workflow to automate processes like e-mail communication or information finding could speed up their work drastically. For first use cases, it might be advised to have an end-user judge the result before communication is sent out to your clients. This improves the quality of the solution, but also lowers the perceived risk of the organization. Ultimately, both AI and low-code are merely tools. To maximize the potential of low-code for AI implementations, it is important to use them in the right way. Keep in mind to:
  • Prioritize safety and security, ensuring compliance with data privacy regulations. Besides the importance of correct handling of data, it will be much more difficult to get stakeholders enthusiastic for the use of AI once a security flaw has occurred.
  • Verify data quality before beginning, starting with projects that have accessible and usable data. Often this means starting with a use case that touches a small process and one department rather than companywide solutions.
  • Involve the right people from the beginning, prioritizing enthusiastic stakeholders over perfect business cases. People will most likely need to do work that is not in their job description, so let’s spend it on something they enjoy.

Discover what AI and low-code can achieve

In summary, low-code is a potent tool for realizing AI ideas and other innovations. By collaborating and adopting a strategic approach, organizations can fully leverage AI’s potential to drive innovation and growth. At LINKITSYSTEMS, we conduct AI discovery sprints. In two weeks, we will bring together your end users, data specialist with one of our low-code experts to select and build a feasible AI use case. By starting small and aligning use cases with organizational goals, we minimize risk and increase stakeholder buy-in. Through small Proof of Concepts, we test feasibility and kickstart conversations with the business. We also establish prerequisites and requirements to ensure AI implementation is smart, secure, and ethical. Once the first use cases are in place, we can train more development teams to actively evaluate the opportunities of AI for their product backlog. Is your organization grappling with the implementation of AI solutions? Don’t just keep reading about it. Reach out to us today to explore how low-code can kickstart your journey into AI innovation.

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