Check out the Low-Code/No-Code Summit on-demand sessions to learn how to successfully innovate and achieve efficiencies by enhancing and scaling citizen developers. Watch now.
Three things are certain in life: death, taxes and artificial intelligence (AI). AI is not only significantly less depressing than the other two, but it has also spawned a number of business innovations and is becoming more affordable as a mass solution. In the startup space, AI has helped predict trends in Covid variants, power military tools, and prevent burnout among doctors.
Beyond the “sexier” applications of AI, the technology has thrived in a sector that literally powers everyday life: logistics. Here, AI has been optimizing delivery routes, reducing last mile delivery times, driving sustainable measures and reducing operating costs. I know this firsthand because I designed the AI for my logistics startup, initially creating an algorithm for my master’s thesis that planned routes for firefighters. This algorithm saved 1,400 lives and reduced arrival times in some of the world’s most congested cities by 40%.
The advantages of AI are undeniable. However, companies often avoid fully committing to it because they believe it is too complex or expensive to integrate. Of course, in light of today’s unstable market, companies need to double down on efficiencies, but it’s still possible to embrace AI and not send shockwaves through your accounting department. With that in mind, here are my tips for small and medium-sized businesses (SMBs) hoping to move their last mile into the world of AI and make a long-term home there.
SMBs: Make sure your foundation is fit for AI
It sounds obvious, but all companies must first identify that they have a real need for AI before making it firmly a part of their model. In the last mile, that means asking yourself if your customers want personalized delivery—for example, if they want to be able to select the times they receive products—or if they’re happy with more standardized processes. If there is no demand for nuances deliveryAI may not be the right path for you, as the specialty of AI lies in its ability to cater to multiple varied outcomes.
Next, take a look at your customers’ behaviors and expectations. Are they changing daily or are they generally consistent? If your preferences are fixed (for example, when and how you receive deliveries remains the same), AI will not be as beneficial to your business. AI is valuable for spotting and understanding patterns in data sets, so if you already have a clear understanding of your customers, AI won’t be able to tell you anything new.
For the final stage of sense verification, fall back on your existing technology. If you don’t have software intelligence to begin with, jumping into AI could get you in trouble. Ideally, you need some smart, automated processes to take place so you can scale them using AI. Remember, AI is not the end result, it should be an accelerator among your predefined practices.
Most SMBs will choose to leverage AI through third-party tools, which makes sense since building your own AI from scratch essentially means becoming a software company. That said, even if you take advantage of others’ AI, you’ll still need to build a team to manage the technology; That means data scientists, people who know what to do with AI output, how to measure it, and how to seamlessly assimilate it into workflows. . The more tech-oriented your team is, the faster and smoother you can integrate AI.
Make a toolbox to cultivate your AI
AI is not a “set it and forget it” solution; you’ll need a comprehensive toolbox to empower and measure your effectiveness from day one. Fortunately, due to the importance of AI in business, there are plenty of tools out there to keep your AI in check.
Let’s start with the basics. Over the past decade, the most common elements of AI have been packaged and made more accessible to a variety of industries. One of the most popular AI tools is TensorFlow which is great for bundling and building AI – the open source core library helps train machine learning models and can be run right in your web browser. In the meantime, Piton is a common AI programming language, and R. helps data scientists scale and align with different AI models.
Elsewhere, Google Machine Learning Kit it is useful if you want to build your own AI offering. and communities like hug face they are ideal for researching AI and participating in conversations about it.
Beyond these tools, you need to make sure you regularly collect feedback from real people using the AI. Take care to recalibrate the algorithms accordingly. The tools to fix a car are all very well to have, but if you don’t know how to use them to accommodate the driver, they are of little value. At SimpliRoute, we ask all of our delivery staff to rate the routes our AI recommends for them on a scale of 1-5. This quantitative information is then used alongside qualitative data (such as surveys) so we can better understand what you do and it doesn’t work with the AI.
Prepare data to be your long-term AI power supply
Becoming an AI company means entering into a long-term relationship. AI will not serve your SME or your users if it is stagnant: it has to be dynamic, combining real-time and historical data to generate insights. That’s why roughly 80% of your last-mile spend will go into collecting, mining, and correcting the data that powers your AI and keeps that information available.
However, the data needs maintenance. You need to be constantly retrieving data from multiple sources to ensure you have the most complete picture possible of your last-mile operations. For example, we need a lot of GPS data, but we also need service information about the time it takes to unload trucks and what the drivers’ preferred routes are. You cannot select the data that confirms what you already know (or want to know). Your data must be genuinely representative for your AI to be most effective.
Be mindful of not only investing in data resources, but also in data people. You’ll need training on emerging AI trends and models for current staff, as well as any new members you bring on to manage AI. If you’re hoping to grow your AI team, partner with universities to attract cutting-edge talent, or offer internships detailing why your AI application is unique – having a mix of business and academia can do wonders for your AI footing.
At the same time, data should not be siled only in departments where AI is at stake, but should influence decisions across the board. whole company, in their marketing teams, sales funnels and more. If data isn’t put at the center of all decision making, you’ll never really put yourself in the shoes of your end users and determine more precisely what to do with the insights your AI brings you.
Embracing AI doesn’t have to be an almighty hill to climb. With so many businesses that have successfully carved out their space in the AI landscape and so many resources to facilitate new player companies, SMBs are better poised than ever to become an authority on AI.
Welcome to the VentureBeat community!
DataDecisionMakers is where experts, including data technicians, can share data-related insights and innovation.
If you want to read about cutting-edge ideas and up-to-date information, best practices, and the future of data and data technology, join us at DataDecisionMakers.
you might even consider contributing an article yours!