5 Steps to Move AI Beyond Buzzwords

Artificial Intelligence (AI) is among the hottest buzzwords in the world right now. It’s been hailed as a game-changer for businesses across industries, promising to transform how we work, live and interact. However, with all the hype around AI, it’s easy to lose sight of the real impact it can have on businesses of all sizes. This article will explore the five steps to move AI beyond buzzwords and deliver a real impact.

Step 1: Focus on the problem and not on the solution

Defining your objective is the first important step. This means clearly articulating the business problem you’re trying to solve and how AI can help.

By thoroughly defining the problem and informing yourself, an idea for a possible AI solution is born. Nowadays, instead of the AI technology itself, the idea poses the real challenge: the more powerful the idea, the more effective the AI solution.

It’s important to involve stakeholders from across the business in this process, including domain experts, IT professionals, and business leaders. By working collaboratively, you can ensure that your objective is aligned with your business strategy and that your AI solutions will deliver real value.

Once you’ve defined your objective, it’s essential to establish clear metrics and benchmarks to measure the success of your AI solutions. These metrics should be tied to your business objectives and regularly reviewed and updated as needed.

Interested in getting started? Keep reading and check out our Discover AI iniatiave below!

Step 2: Focus on the search for qualitative data

AI systems are only as good as the data we put into them. Therefore, the data phase focuses only on searching for and evaluating data sets.

Identify data sources

It is helpful to start by summing up all possible data sources that can be linked to the problem. Are there in-house data sources, or do public databanks exist? Do you need to create (more) data? Don’t hesitate to involve other stakeholders and experts to create a win-win.

Evaluate costs criteria

Once you have a list of possible data sources that can be used to tackle your problem, each data source needs to be critically evaluated to select the best one(s). We proceed only with data sources that contribute meaningfully to the AI model. Three cost criteria are evaluated for each of these data sources: time, money, and expertise.

And remember: you don’t need a massive volume of data to create an impact with AI. Any AI model requires data to be modelled, but not every use case requires large amounts of data. Specialised techniques exist that can extract business value from small data collections.

Read more about it in our blogpost: the data that fuel AI.

Discover AI

If you’re looking to improve your company’s use of data and gain a realistic understanding of the benefits offered by artificial intelligence applications, you may be wondering how to optimize the data you already have.

ML2Grow offers a program for eligible companies to explore the potential of AI and data for their organization over a period of several days. During this program, our team will work with you to evaluate the value of your available data, both internal and external, and identify any relevant AI or data projects within your business context. We’ll also help you determine which stakeholders and experts to involve in these projects and provide tips and tricks better to estimate the added value of your possible AI projects.

Our services include preparation, analysis, reporting, and participation in meetings, whether onsite or remote. We understand that every company’s AI journey is different, so we’ll work with you to map out the next steps tailored to your specific needs.

With over 15 years of experience and over 100 companies helped, we offer expert mentorship & innovative insights to develop a competitive advantage. Over 95% of the companies we talked with, were able to discover potential AI opportunities.

Step 3: Understand the limitations and biases of AI

As AI becomes more prevalent in various industries, it’s important to acknowledge its limitations and biases. For example, AI algorithms can only make predictions based on the data they are trained on, and if the data is biased, the AI system will reproduce those biases. Additionally, AI solutions can only solve the problem they were designed to solve, and they may not be effective in other contexts. Understanding these limitations and biases can help people make informed decisions when implementing AI solutions in their company.

Choosing the proper AI techniques requires a deep understanding of the problem you’re trying to solve and the strengths and weaknesses of different AI techniques. Working closely with your IT team and domain experts is essential to choose and implementing the technique correctly.

It’s also important to recognize that AI solutions are not one-size-fits-all. The techniques for one solution might not be appropriate for another. Taking a customized approach and choosing the best techniques for your problem are important.

ML2Grow can help your organisation understand the limitations and biases of AI by providing training and education on the topic. We can help companies identify potential sources of bias in their data and design AI solutions that are more ethical and less prone to bias.

Step 4: Collaborate with domain experts

Collaboration between AI and domain experts is critical for developing effective AI solutions. Domain experts have valuable knowledge about the problem space and can provide insights into the nuances of the problem that AI experts may not be aware of. Additionally, domain experts can help identify relevant data sources and provide feedback on the AI solution’s output. Collaboration between AI and domain experts can lead to more effective and relevant AI solutions.

ML2Grow can facilitate collaboration between our AI experts and your domain experts by providing a bridge between the two groups. We can help translate technical AI concepts to domain experts and vice versa, ensuring that both groups understand the problem being solved and the solution being proposed.

Step 5: Test and evaluate AI solutions

Testing and evaluation are essential for ensuring that AI solutions are effective and ethical. Testing should involve using a representative dataset to evaluate the AI solution’s performance. Additionally, monitoring the AI solution’s performance over time can help identify any issues or biases that may arise. Evaluation should also include a thorough ethical review to ensure the AI solution is not inadvertently perpetuating biases or ethical violations. By testing and evaluating AI solutions, people can ensure that their AI solutions are practical, ethical, and relevant to their company’s needs.

Monitoring and improvement involve several activities, including performance monitoring, model tuning, data augmentation, and continuous learning. It’s important to establish metrics and benchmarks to measure the performance of your AI solutions and regularly evaluate whether they’re meeting your business objectives.

Continuous improvement also requires a culture of experimentation and innovation. AI is rapidly evolving, and new techniques and approaches are constantly emerging. By encouraging experimentation and innovation, you can stay ahead of the curve and continue to deliver transformative impact.

ML2Grow can help companies test and evaluate their AI solutions by designing and implementing rigorous testing and evaluation protocols. Our team can monitor the AI solution’s performance over time and identify any issues or biases that may arise. Additionally, we can guide how to conduct an ethical review of the AI solution to ensure it meets ethical and legal standards.

Custom-tailored AI solutions

It requires a commitment to collaboration, diversity, ongoing learning, data governance, security, and continuous improvement. AI solutions are not one-and-done; they require constant monitoring, testing, and improvement to ensure they deliver value over time.

AI and machine learning are quickly becoming essential components of any modern business. These technologies can help organizations streamline operations, make better decisions, and gain a competitive advantage. However, implementing machine learning solutions can be complex and challenging, requiring a deep understanding of the technology and the business domain. Our team at ML2Grow can be invaluable in helping to achieve these objectives.

1. Deep expertise in machine learning

At ML2Grow, we have a team of highly skilled and experienced professionals who have a deep understanding of machine learning and AI technologies. We stay up-to-date with the latest developments in the field and have experience working with businesses in various industries. This expertise enables us to identify opportunities for machine learning and develop customized solutions that meet our client’s specific needs.

2. Cost-effective solutions

Hiring an in-house team of machine learning experts can be costly, and it can take time to build the necessary expertise. By partnering with ML2Grow, businesses can access a team of experts who can hit the ground running and deliver value immediately. This can be a more cost-effective solution than building an in-house team from scratch.

3. Faster time to market

Implementing machine learning solutions can be a time-consuming process, especially if the business does not have the necessary in-house expertise. Our team of experts can help your business time to market and start realizing the benefits of machine learning more quickly. We can provide guidance and support throughout the process, from identifying opportunities for machine learning to implementing and testing solutions.

4. Customized solutions

We understand that every business is unique, and machine learning solutions for one company may not work for another. We provide customized solutions tailored to our client’s specific needs and objectives. This can be a more practical approach than using off-the-shelf solutions that may not fully address the business’s needs.

5. Access to cutting-edge technology

We are at the forefront of the latest machine-learning technologies and techniques. By partnering with us, businesses can access advanced technology they may not have been aware of or not have had access to otherwise. This can give them a competitive advantage and help them stay ahead of the curve.

6. Reduced risk

Implementing machine learning solutions can be risky, especially if the business lacks in-house expertise. ML2Grow can help mitigate this risk by providing guidance and support. This can help companies to avoid costly mistakes and ensure their machine-learning solutions deliver the expected value.


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