The truth is far different. Any business can begin harnessing these technologies to innovate, streamline, and grow.
In reality, small and midsize businesses have an advantage over their larger brethren because more often than not they can make decisions faster and are willing to try something new.
The case for starting your first AI project
At the top of everyone’s list is gaining a competitive edge. Early adoption will help companies move faster to optimize operations, reduce costs, and improve customer experiences. The experience enables business leaders to capture valuable data insights, foster better decision-making, and uncover new growth opportunities.
There are many cultural benefits too, from promoting a learning mindset and fostering innovation to enhancing collaboration and data-driven decision-making.
The next question is obvious: how to get started.
This article shares the steps you can take to make your first project a success.
1. Pick a project sponsor
A committed sponsor whether an executive, department head, or team leader provides the project with direction, visibility, and advocacy within the organization. They help secure necessary resources, overcome bureaucratic obstacles, and ensure alignment with the company’s broader strategic objectives.
This is especially true for an endeavor like an AI project, which might face initial skepticism or resistance. If this is the case, the sponsor acts as a champion, getting buy-in from stakeholders and ensuring the project remains a priority.
2. Start small and specific
The allure of AI’s vast capabilities can be tempting. But it’s crucial for businesses with budgetary constraints to resist the urge to jump too far too fast from the get-go. Instead, take a different approach – zero in on a specific pain point or challenge that AI can address.
It might be a customer service bottleneck, a demand generation inefficiency, a marketing analytics gap, or a recurrent inventory issue. By addressing one challenge at a time, you can achieve tangible results faster and pave the way for broader applications down the line.
3. Use a Technology and Consulting Partner
Going solo is seldom cheaper at this stage. Engaging with an experienced AI technology and consulting partner can be a game-changer. These partners have expertise, pre-built solutions, and industry knowledge – all qualities that can help you avoid costly mistakes. They can guide you in pinpointing the most relevant AI applications for your business, reducing the trial-and-error phase.
While there’s an upfront cost, the long-term savings in terms of time, resources, and potential missteps can be significant.
4. Leverage third-party tools
In the AI ecosystem, there’s an abundance of third-party tools and platforms designed to simplify the process. Platforms like TensorFlow, Scikit-learn, and others offer pre-built algorithms and models that can be tailored to specific business needs.
This means you don’t have to reinvent the wheel or invest in creating models from scratch. Instead, leverage these resources to accelerate your AI projects and ensure you are building on tried-and-tested foundations.
5. Use cloud platforms
Infrastructure costs can quickly escalate when diving into AI. Cloud platforms such as AWS, Google Cloud, and Azure offer scalable AI services.
These platforms provide access to powerful computational resources without the need for significant upfront capital expenditure. Their pay-as-you-go models mean you’re only spending on what you use. Plus, many of these platforms offer free tiers, tutorials, and credits to get started, making them ideal for businesses looking to dip their toes into AI without a hefty financial commitment.
6. Emphasize data quality
Although data is the lifeblood of AI, more data doesn’t always translate to better results. Especially when on a budget, it’s essential to focus on the quality and relevance of the data over volume. Clean, organized, and well-curated data can dramatically improve the effectiveness and accuracy of AI models. Investing time in ensuring your data is of high quality can often negate the need for vast amounts of it, streamlining costs in the process.
7. Prototype and validate
Before fully investing in the integration of an AI solution, it’s crucial to start with a proof of concept or minimum viable product (MVP).
This approach allows you to validate the efficacy and relevance of the AI solution in a real-world context. With a prototype, you can gather feedback, assess alignment with business goals, and make necessary refinements before scaling. This iterative approach reduces the risk of substantial sunk costs in projects that might not yield the desired outcomes.
8. Iterate and learn
The AI journey is rarely linear. Like any other project, you can anticipate challenges, unexpected outcomes, and learning curves. So you need to adopt an iterative mindset: launch, learn from feedback, refine, and relaunch.
This closed-loop approach not only optimizes the AI solution but also ensures that you are making the most of your resources. Each iteration is an opportunity to optimize costs, refine strategies, and move closer to your business goals.
Experimenting with AI is not only about solving current challenges—it’s about future-proofing your business. As you experience the tangible benefits of your first project, you will better position yourself to scale these technologies. A modest start can lead to more ambitious projects as your team acquires new skills, gains confidence, and sees a return on investment.