I’ve spent the last 20 years designing, building, and commercializing enterprise software products, so it’s safe to say I’ve made a career out of high-stakes problem-solving. In all that time, I’ve learned that even when you have the ideal scenario of a clear-cut problem, a winning solution, and a group of people who could really benefit, it’s still not an easy sell. And that’s when things are “simple.”
Here are seven things to keep in mind for anyone planning to bring advanced technologies to conservative industries that may just be catching up on disruptive innovation.
1. Consult your customer.
Today, we’re living in the Age of the Customer and it’s not enough to simply “keep them in mind” as you design your product. The best solutions take it one step further and bring their customers along for the ride.
You should develop your product with your intended end-user by partnering with future customers and hiring professionals from within the industry. This step is especially critical when your product team lacks experience in the industry they are building for. The combination of views from both inside the targeted industry (for better product fit) and outside of that industry (for the “art of the possible”) leads to better innovations.
2. Combine user-centered and vision-centered innovation principles.
Start building your product by applying user-centered innovation techniques. Strive to understand the needs of users through deep analysis of their behaviors and how they interact with existing workflows and products. Later, vision-centered innovation techniques should be applied. If user-centered design helps to take current processes and make them better, vision-centered design asks how we might do things differently. Combining both ensures you’re not being too myopic in your design thinking process. During our development process, I pushed the team to bring up ideas and concepts that customers did not specifically ask for or did not think were technically possible, and some of those ended up being key parts of our solution.
3. Build in flexibility.
Each customer has their own way of doing things and requires the technology to adapt to the way they run their business. This is simple when you build a custom solution, but much harder with a “one size fits all” SaaS platform that is used by many different customers. Rather than trying to anticipate the infinite number of permutations that our customers might require, we designed a standard “core” to the platform with built-in flexibility provided by APIs and microservices. That meant providing the building blocks that would allow users to configure their own scoring algorithms, workflows, underwriting scenarios, and outputs.
4. Build your human/machine interaction to be mutually beneficial.
No matter the level of sophistication of the artificial intelligence (AI) and automation that you develop, it must be clear that the user is always in control. AI can enable customers to make scientifically informed decisions, but it should not make decisions for them. We designed our product to provide ways for users to validate AI-driven information and easily correct it when desired. For example, we use machine learning models to select the best sales and rent comparables to provide the most accurate valuation, but users can still include or exclude comparables based on their specific experience and knowledge of the area. Not only do users end up getting better data that fits their needs exactly, but these interactions ultimately help train our AI models and make them even better in the long run.
5. Pay attention to the unique needs of the sector you’re serving.
If you’re working with AI, the type of models you develop must fit the needs of the specific use case and the industry you’re building for. In highly regulated industries, there will always be a need to understand the results of the models, so you should avoid deep learning models that typically don’t provide reasons or explanations for how predictions are reached and concentrate on those that do. A great example would be the movie recommendations on Netflix. The platform learns your preferences and patterns and recommends content accordingly: “Since you liked X, you might like Y.” If Netflix didn’t explain the “why,” you might be less likely to pay attention. And in a heavily regulated industry, a conclusion like that without an explanation would not be acceptable at all.
6. Keep it simple.
When it comes to product design, the Law of Parsimony applies: the simplest answer is usually the right one. Technologists are often so focused on “innovating” that they build features that will never actually be used. If advanced technology doesn’t make sense, don’t use it (no matter how cool it is). You can spend months building complicated screens and models that are expensive to build and hard to maintain, just to mimic spreadsheet functionality, when what users really want is to use that spreadsheet. Keep this in mind when designing product features to avoid developing complex technology where it’s not needed.
7. Maintain a parallel track of “leap” innovation.
Sometimes customers (or industries) are just not ready for truly disruptive innovation. So, while adhering to the previous recommendations, I always maintain a product innovation track to research and develop truly disruptive innovation that could be eased into in the future. Today, users would probably not accept blindly making loan decisions based on AI recommendations, but we can build a parallel predictive model in the background while still allowing users to analyze risk in a way they’re comfortable doing. Then, sometime in the future when we can show them how accurate the AI model would have been, they’ll be ready to adopt it—no wait time required.
Hopefully you’ll find something helpful in these seven tips. Introducing new ideas and technologies to an industry that has been satisfied with doing things the way they’ve always been done can be daunting. It can also be fun—especially when you know that you’re truly making a difference.
Tal is co-founder & COO at Blooma, a data-driven software solution for the commercial real estate industry. Connect with Tal on LinkedIn.