There’s no question that machine learning and deep machine learning can produce huge competitive advantages. Unfortunately, some barriers don’t allow all companies to benefit. Will no-code AI be the solution to this problem? We at SANDSIV think this is a viable solution.
I created my first predictive models around the year 2000. They were fairly simple models compared to those available today, but they still had good quality, especially at predicting the churn risk of telecom customers. We used low-code software to create the models back then. In that particular case, Clementine was later purchased by SPSS and then IBM. The advantage was that with all-too-low effort, you were quick to create and bring into production models. Two main factors contributed to these benefits: you didn’t have to be a developer to create these models. The people who created the models knew the business and the processes inside out because they were marketing people, not data scientists.
Those two factors are key to creating a competitive advantage, especially in machine learning and AI in general. This strategy enables fast time-to-value and high model accuracy: deep knowledge of business processes is key in creating an accurate AI model.
With the same awareness, it is the same spirit that I brought to the sandsiv+ platform. Here even bringing an additional challenge: no-code. Creating NLP models on Deep Learning architecture within everyone’s reach. No-code, in fact. Why? This is mainly because of one of the biggest obstacles to AI adoption in enterprises: AI technologies and skills are too expensive. No-code AI solutions like sandsiv+ help democratize AI by making it widely and easily available at a low cost.
What is No-code AI?
No-code AI means using a no-code development platform with a visual, no-code, and often drag-and-drop interface to deploy AI and machine learning models. No-code AI allows non-technical users to classify and analyze data quickly. And easily build accurate models to make predictions. It’s a programming method and movement that doesn’t necessarily involve writing code. Instead, it works with a graphical user interface (GUI), where people can use templates, drag & drop functions, conversational interfaces, and logic sequences to bring any AI model to reality.
Is there a difference between no-code and low-code?
Yes, and it’s an important difference: low-code assumes some technical knowledge. Let’s take low-code solutions like SAAS, Knime, the same Clementine now called IBM Modeller. To use these applications, you need to have a certain technical background, even an important one. The challenge of no-code is to make sure that business people, without any deep technical knowledge, can create AI models without writing a single line of code.
Why is no-code AI important for businesses?
Businesses need to build AI models. According to Forbes, 83% of companies say AI is a strategic priority for their businesses today, but there isn’t enough data science talent. The demand for AI talent has doubled in the past two years. Technology and financial services companies are absorbing 60% of AI talent, forcing smaller companies to slow down their AI deployment programs with a clear loss of competitive advantage.
Democratization of AI. Building AI models (i.e., training ML models) takes time, effort, and experience. No-code AI reduces the time to build AI models to minutes by allowing companies to adopt machine learning models into their processes easily. Through such tools, sales/marketing/product/operations teams and founders anywhere in the world can build sophisticated workflows and applications without needing any technical knowledge.
Where do we stand with no-code AI?
According to Google Trends, although interest in no-code AI has started to increase, it is still far less than the number of people interested in learning ML or autoML. No-code AI solutions have not yet replaced data scientists. This is still an emerging field. The increasing maturity and flexibility of existing solutions and widespread integrations will drive greater adoption.
What are the benefits of no-code AI solutions?
No-code AI solutions reduce entry barriers for individuals and enterprises to begin experimenting with AI and machine learning. These solutions help companies adopt AI models quickly and at a low cost.
The big challenge, how to combine business know-how with AI?
Data science is still an emerging field, and most data scientists have less business experience than domain experts. According to a data science survey conducted by data science competition platform Kaggle, a crowdsourcing solution for AI projects, the most common age of respondents is 24, and the median is 30. Business users can leverage their domain-specific expertise and quickly build AI solutions relevant to business problems with code-free solutions. To solve the problem, it’s not enough to technically know what needs to be done at the AI level, and you absolutely need to understand the business in all its aspects and nuances deeply.
Building custom AI solutions require code writing, data cleansing, categorization, data structuring, model training, and debugging. These also require more time for those unfamiliar with data science. Studies claim that low-code/non-code solutions can reduce development time by up to 90%.
Low cost
One of the most obvious benefits of automation and no-code technologies is cost savings. Companies need fewer data scientists when they can have their business users build machine learning models.
Helping data scientists focus
For companies that already have a data science team, requests from other employees shift the team’s focus to easy-to-solve tasks. No-code solutions minimize these distracting requests, as they allow business users to tackle these requests on their own.
What’s the difference between AutoML and no-code AI?
Unlike no-code solutions, AutoML solutions are focused on enabling data scientists to be more efficient. They provide transparency into the entire machine learning pipeline, which increases complexity and allows data scientists to refine the way models are built. Whoever can merge these two worlds into one that is easily usable by business people will surely have created a winning product that will win their customers.
Currently, no-code AI solutions focus on helping non-technical users build ML models without going into the details of every step in building an ML model. This makes them easy to use but harder to customize. Combining No-code AI with AutoML and allowing deep customization of models is definitely a move that will create the possibility of creating a huge competitive advantage. This is the mission we are following at sandsiv+, especially in the field of Natural language Processing. But not only that.
No-code AI in practice – how does it change CX?
Now that you know a little more about no-code AI let’s look at what it can do in practice. Depending on the industry you’re in, no-code AI can be used for several things, such as:
- automating processes
- making decisions
- analyzing data automatically
- helping with marketing automation
- improving customer service close-the-loop processes
- make conversational survey chatbots smarter
…and clearly much more.
For example, we look at AI in marketing and how to integrate customer experience insights:
While the marketing world is highly creative, creativity must connect with expected business outcomes. No-code AI modules from sandsiv+ can be introduced into the marketing industry to target and align marketing campaigns with customer demand closely. For example, by connecting sandsiv+ to your Marketing Automation system, you can timely personalize content across channels to ensure that customers find the content they want to see relevant at that moment and avoid inundating them with inconsistent and counterproductive messages. No-code AI in Customer Experience can better inform current and potential customers about the solutions they want and need.
What should companies think about before adopting no-code AI?
Choosing to adopt no-code AI in your business is not a matter to be taken lightly. Like all business decisions, it’s essential to analyze how your desired solution should fit your overall ecosystem upfront. If you don’t, you could end up with a rather expensive piece of technology that doesn’t work the way you intended. In some cases, your company might better benefit from a fully customized AI solution rather than a no-code variant. However, this will only be discovered through a deeper analysis of your company’s specific needs.
Also, finding a vendor that fits your needs is a must. Make sure your vendor is prepared to answer any questions you may have before investing. The market today has many offerings, and what works for one company may not work for another. So, before you decide to get on board with any no-code solution, make sure you reach out to the right team.
