3 top tips to help SMBs make a successful home in the AI space

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Three things are certain in life: death, taxes and artificial intelligence (AI). Not only is AI significantly smaller than the other two, it has also spawned many business innovations and is becoming increasingly affordable as a mass solution. In the startup space, AI has helped predict trends in Covid variants, bolster military tools and prevent friction between doctors.

Beyond the “sexier” applications of AI, the technology has surged in a sector that literally drives everyday life: logistics. Here, AI has been optimizing delivery channels, reducing last-mile delivery times, driving sustainable measures and reducing operational 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 travel time by 40% in some of the world’s most congested cities.

The advantages of AI are undeniable. However, companies often shy away from fully committing because they believe integration is too complex or expensive. Of course, given today’s volatile market, companies need to double down on efficiency, 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 Enterprises (SMEs) looking to take their last mile into the AI ​​world and make a long-term home in it.

SMEs: Make sure your foundations are right for AI

It may seem obvious, but every company must first identify that they have a real need for AI before making it a firm part of their model. In the last mile, this means asking yourself whether your customers want personalized delivery—for example, they want to be able to select the times they receive their goods—or whether they are satisfied with more standardized processes. If delivery is lacking in nuance, AI may not be the right path for you, as AI’s specialty lies in its ability to achieve multiple outcomes.

Next, look at your customers’ behaviors and expectations. Do they change daily or are they generally consistent? If their preferences are fixed (for example, when and how they receive deliveries remain the same), AI will not be as beneficial to your business. AI is valuable for detecting and understanding patterns in data sets, so if you already have a clear understanding of your customers, AI can’t tell you anything new.

For the final stage of sensory control, turn to the technology you already have. If you don’t have intelligence software to begin with, jumping into AI can cause problems. Ideally, you need some automated and intelligent processes to scale using AI. Remember, AI is not the end result, it should be an accelerator between pre-tuned practices.

Most SMBs will choose to leverage AI through third-party tools, which makes sense because building your own AI from scratch essentially means becoming a software company. That said, even if you leverage other people’s AI, you’ll need to create a team to manage the technology; that means data scientists, people who know what to do with AI output, how to measure it and easily assimilate it into workflows. . The more technology-focused your team is, the faster and more efficiently you’ll be able to integrate AI.

Make a toolbox for crafting your AI

AI is not a “set it and forget it” solution; you’ll need a comprehensive toolbox to nurture and measure its effectiveness from day one. Fortunately, given the prominence of AI in business, there are plenty of tools 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 ideal for linking and building AI – a leading open source library that helps train machine learning models and can be run directly in your web browser. Meanwhile, Python is a common AI programming language, and R helps data scientists scale and align with different AI models.

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Elsewhere, Google’s ML Kit is useful if you want to create your own AI offering. And communities like Hugging Face are perfect for researching AI and engaging in conversations about it.

Beyond these tools, you need to make sure you’re regularly gathering feedback from real people using AI. Be careful to calibrate the algorithms properly. It’s good to have tools to fix a car, but if you don’t know how to use them to suit the driver, they’re of little use. At SimpliRoute, we ask all of our delivery staff to rate the routes recommended by our AI on a scale of 1 to 5. This quantitative information is used in conjunction with qualitative data (such as surveys) to gain a deeper understanding of what it does and what it does. it doesn’t work with AI.

Prepare data to be your long-term AI power supply

Being an AI company means entering into a long-term relationship. Intelligence won’t work if your SMB or your users are idle; it must be dynamic, combining historical and real-time data to generate information. That’s why roughly 80% of your last-mile spend will go to collecting, extracting, and processing the data that drives your AI and continues to extract those insights.

However, data requires maintenance. You should be constantly pulling data from multiple sources to ensure you have the largest possible picture 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 the routes drivers prefer. You cannot select data that confirms what you already know (or what you want to know). Your data needs to be truly expressive for your AI to be most effective.

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Be conscious of investing not only in data resources but also in data people. You’ll need training on emerging AI trends and models for your current staff, as well as for new members you bring in to manage AI. If you’re hoping to grow your AI team, partner with universities to attract top talent or offer internships that explain why your AI application is unique: a mix of business and academia can do wonders for your AI rank.

At the same time, data should not be isolated to the departments where AI is at play; they should influence decisions. whole across the company, your marketing teams, sales funnels, and more. If data isn’t put at the center of all decision-making, you’ll never step into the shoes of your end user and determine more precisely what to do with the conclusions your AI gives you.

Embracing AI doesn’t have to be an almighty mountain to climb. With so many businesses that have successfully carved out their place in the AI ​​landscape and so many resources to facilitate the initiatives of new players, SMEs are better prepared than ever to become AI authorities.

Alvaro Echeverría He is the founder and CEO of the company SimpliRouteroute optimization software that helps companies reduce logistics costs and increase customer satisfaction.

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