Practical advice for non-technical entrepreneurs looking to integrate AI and machine learning into their products and services.
You have a startup and you are developing a product that uses data to provide value to your customers. You have been thinking that AI could provide additional value and help differentiate your product from that of your competitors. So you are looking into integrating AI into your product and taking it to the next level.
But you still have doubts and as a result, you are not ready to proceed. Depending on your own knowledge of AI, you could be be asking yourself a few different questions:
- How exactly could AI make our product better?
- Is implementing AI really worth the effort?
- I am ready to do it, but how do I implement it?
Each of these challenges has a specific solution. Read on to find the answer to your specific question.
So, what exactly can AI do for my product?
Depending on your product, integrating AI could make it immensely more valuable to the point of transforming your value proposition; in other cases the new value could be negligible, and you’d be better off not bothering. To know where your situation falls within this spectrum, a good first step is to understand the general categories of AI and identify which ones apply to your use case. Since AI covers so many different areas, and means different things to different people, we compiled the following practical list.
AI Areas
- Cognitive computing: This area aims to automate cognitive tasks that would otherwise require humans to accomplish. Some of the applications include:
- Classifying images or digitizing handwriting using computer vision
- Transcribing audio using speech recognition
- Analyzing physical behaviours using video image processing
- Predictive analytics: This area aims to predict future outcomes based on historical data. Some applications include:
- Analyzing historic and current trends to forecast future activity
- Determining data relationships, spotting patterns and detecting irregularities
- Prototyping specific problems to identify process improvements
- Natural language processing: This area aims to analyze human language and understand various aspects, for example:
- Determining whether a comment is positive or negative
- Detecting threats or abusive language
- Understanding the intent of an inquiry
- Combining areas: Some of these areas can also be combined. For example, you could have an application that transcribes handwriting from a printed customer satisfaction form and determines whether the feedback is positive or negative. In this case, you would be relying on cognitive computing (computer vision specifically) to digitize the handwriting and natural language processing (sentiment analysis) to determine whether comments are positive or negative.
Understanding which areas of AI apply to your product will help you determine which parts of your product would benefit from them.
How much more value will AI bring to my product?
At this point, you have determined the AI areas that apply to your product. Now you might be asking yourself if in fact AI will solve the problem you have in mind, and if so, how much of the problem it will solve. Thankfully, there is a way to answer these two questions without embarking on a costly implementation effort: meet the proof of concept.
Next up: the proof-of-concept
The indisputable way to answer whether the value brought by AI justifies the effort to implement it, is to set up a proof-of-concept (POC) and measure the results. The POC will help you answer two key questions:
- Can AI actually accomplish what I have in mind?
- What are the measurable benefits that AI can bring to my product?
The first question is about feasibility and the answer should be a clear-cut yes or no. The answer to the second question should be quantifiable (e.g. “implementing AI would reduce a person’s workload by 20%”)
So, when setting up the POC, make sure you do it in such a way that you would be able to get the answers to these two questions.
How do I go about implementing AI?
By now, you know which AI areas will bring value and how much value each area will bring. The next logical step is to determine whether the value justifies the cost to implement the solution into your product. This implementation effort is sometimes referred to as operationalizing AI, and in our situation, it means integrating the POC with the rest of your product.
Knowing the quantifiable benefits that AI will bring and the cost to implement these benefits will help you measure ROI, perform cost-benefit analysis and gain confidence to proceed with the work.
To implement AI, you will need:
- A work plan outlining tasks, timelines and costs
- A team to carry out the work
As a product startup, you may already have an in-house development team that can handle this on their own. Or, you may be working with a development team that doesn’t have experience implementing AI and could benefit from technical guidance. Or, you may not have a team at all, and you would need to outsource the entire project.
Whichever your case, make sure that you work with a technical team to help you create a realistic work plan, assess level of effort and costs, and determine the gaps in skillset that you will need to fulfill.
The path forward
As a startup, you can’t afford to embark on costly implementations without a strong sense of certainty. Implementing the approach from this post will help you minimize risk and increase your changes of arriving at a successful outcome. Integrating AI into your product could be the single most valuable feature in your roadmap.
Would you like to discuss how you could integrate AI into your product? We are here to discuss your use cases and provide you with advice specific to your situation. Contact us today.
The photo on this post is courtesy of ThisisEngineering RAEng.
