It's a case study in the dangers of overthinking. Many businesses are stuck trying to solve complex problems it instead of focusing on the obvious opportunities where it could deliver value very quickly. It goes back to the fundamental way that the executives of those companies perceive artificial intelligence – is it a tool or a threat? Part of the problem is that people are aware that AI can do a lot. However, the fear of wrong initiatives leaves many businesses frozen like a deer in headlights.

Businesses will only begin to overcome any surrounding uncertainty The potential of AI getting practical and finding the best use cases. Look no further than Amazon for inspiration. Its AI-driven recommendation system for personalized marketing has become one of the the most prevalent features in e-commerce.

Using data from a customer's purchase history, this feature uses AI to analyze customer behavior patterns and suggest products tailored to their preference. It has been so successful 35% of what consumers buy on Amazon comes from these recommendations and the feature has since become a benchmark in the industry.

As big as Amazon is, its success shows that when AI is implemented well, there is nothing to fear and everything to gain. However, I have seen businesses without a clear path to optimal AI implementation spinning their wheels and failing to make progress or see results. To help combat analysis paralysis, I've put together a basic guide on how to take advantage of AI's potential.

Connected: How AI is transforming keyword research

Get rid of these misconceptions

The first stop in our guide is to dispel some common myths. To start your AI journey, consider the following:

• AI does not require perfect data. Of course, data feeds AI. But there is there is no such thing as perfect data. In fact the best thing about AI is that it thrives on unstructured data. Previously difficult to use, unstructured data now represents untapped potential. What business data do you have that shows what “good” looks like? Reports, advice, plans? Put these huge resources into AI along with the problem being solved. Once the model understands the problem it's solving and what it looks like, it can start producing those results on its own.
• You don't have to build it yourself. During implementation AI initiatives, off-the-shelf solutions can be a great way to start because they meet the needs of most organizations. A host of AI products are coming to market. Take some time to browse their features and check out the review comment. Just seeing what's out there can inspire innovation.
• Internal champions are needed to drive AI initiatives. Like any new initiative, the team needs to be invested and passionate. Don't give an AI innovation project to a team that isn't excited to participate. We all know where it will end. Find your champion, someone who sees potential and wants to learn and grow. If you find the right leader, your plans will flourish.

The key message for business leaders is to start now. Don't wait for the conditions to be perfect. Use whatever data you have available now and focus on quick wins to deliver immediate value to your company.

Connected: How designers can use AI to be more creative and efficient

Identify quick wins

As tempting as it may be to think big picture, to find those quick wins, narrow your focus. Usually, this means moving forward in processes that are manual, repetitive, time-consuming, and often prone to human error. Then, apply AI strategies to identify patterns and trends in the data, such as customer preferences, habits, and seasonal trends. Determine which ones are most important to turn into a quick win with the help of the people who work on them every day.

Another tip is to target areas with high data availability, such as customer service or human resources and find smaller, scalable opportunities where AI tools can add the most value. For example, AI can easily extract the most common themes of customer complaints that can then be used to improve services. Other easily identifiable quick wins include:

• retail chatbot: A Gartner survey found that about one the fourth of the organizations will rely on chatbots as their primary customer service channel by 2027. When automated shopping assistants are integrated into retail operations – eg, mobile apps, websites, messaging platforms, etc. . – they can analyze user data and patterns and make product suggestions tailored to the customer's specific interests. Plus, customers will have access to efficient after-hours assistance, increasing efficiency and reducing customer wait times.
• Supply chain management: AI is helping businesses optimize their supply chains and manage their inventories more efficiently by analyzing large amounts of data and making accurate predictions. Whether the data is structured or not, it can illuminate customer profiles, complement planning documents, highlight inbound offerings, and even draft planning documents.

According to a McKinsey report, implementing AI-enabled supply chain management can save early adopters up to 15% in logistics costs, significantly optimizing inventory levels. Such examples show that an organization's AI strategy must go beyond simple technological improvements to align with its business objectives, so that any iterative initiative works toward commercial benefit.

Clear value, low risk

Companies that will thrive in our emerging digital market are quick to exploit the full potential of AI-powered tools. This includes generative AI, a powerful asset for any decision maker. Extracting knowledge from volumes of data offers new opportunities and can help a lot leaders avoid prejudice in decision making.

Remember to focus first on high-impact opportunities where AI can provide clear value quickly and with minimal risk. This will help leaders break out of analysis paralysis and start realizing the tangible benefits of AI. From there, the future is unwritten, but likely belongs to those willing to embrace change and adapt to new realities.