Artificial intelligence (AI) has been a hot topic for many years, with significant technological advancements allowing researchers to achieve remarkable progress. As a result, many companies are now investing in AI startups, and the AI market size is projected to reach over USD$1,597.1 billion by 2030, increasing at 38.1% annually.
Many factors have facilitated this growth, including various technological advancements. The emergence of big data, cloud computing, and increased computer processing power have helped researchers attain remarkable progress.
With such growth potential, it is essential to consider how to maximize returns on AI investments. But how will you maximize your returns? This article outlines practical tips. Read on!
Understand your business needs
Like any other business, you must understand your needs before investing in AI. Remember, this industry is vast and becoming more competitive every day. Going in without clear goals may lead to wasted resources, unproductive projects, and missed opportunities.
How can you identify specific problems you can solve using AI and drive value for your business? Start by analyzing business processes, customer feedback, and industry trends. This research should give you an idea of what the market needs and how you can take advantage.
While at it, it’s essential that you also identify the ideal AI tool for each problem. Fortunately, several helpful resources online, like Future Forge, will help in this process. They’ll show you the best tools to make your dreams a reality.
Identify the suitable AI use cases
Although artificial intelligence can transform businesses across industries, not all problems are solvable through AI integration. Therefore, finding excellent use cases that can deliver measurable value is crucial.
Prioritize all possible use cases based on their potential impact and feasibility. Consider factors such as the level of automation and potential influence on business operations and stakeholders.
Some options are easier to implement and can deliver quick wins. Conversely, others require significant investment and long-term planning. But as you compare these factors, consider the return on investment vis-à-vis the resources needed.
Examples of AI applications
Not sure how to implement AI in your business? Here are some use cases in different industries:
- Retail: You can use it to optimize pricing and promotions and improve customer product recommendations. It may also help you in forecasting demand for inventory management.
- Healthcare: It assists in medical diagnosis, identification of patients at risk of developing certain diseases, and personalized treatment plans based on patient data.
- Financial services: AI can detect fraud, improve credit risk assessment, and optimize investment portfolios. So, if you have a financial institution, you can use AI to speed up operations and serve as many clients as possible.
- Manufacturing: Operators can use it to predict equipment failures, improve quality control, and optimize supply chain management.
Customer service: It’s ideal for automating routine tasks like answering frequently asked questions. You can also apply it in building personalized chatbots and analyzing customer feedback to improve products and services.
Build a productive AI team
The success of your AI project depends on the team of experts you hire. Remember, AI requires specialized skills to develop, implement, and manage.
Below are key roles and skills for an AI team:
- Data scientists: Data scientists analyze data and develop models to solve specific business problems. They should have expertise in statistics, machine learning, and programming languages such as Python and R.
- AI engineers: AI engineers build and implement AI models and solutions. Their skills include software development, data infrastructure, and machine learning frameworks like TensorFlow and PyTorch.
- Project managers: Project managers oversee the implementation of AI projects from start to finish. Your consideration for this role should be someone skilled in project management methodologies and tools. They must also understand business processes and the stakeholders involved.
These are the leading members of your team, but you could have more depending on the project size. Regardless of the position, always ensure you find employees experienced enough for the task.
Monitor and evaluate your project continuously
After completing the implementation phase, ensuring your AI solution meets business goals is essential. Also, assess it to see if it delivers value. The best way to do this is to monitor performance continuously and adjust accordingly.
Below are steps in your monitoring and evaluation process:
- Define key performance indicators (KPIs): KPIs are metrics used to measure an AI solution’s performance. KPIs should be defined based on the specific business goals the AI solution intends to achieve. For example, if the purpose of an AI solution is to reduce customer churn, KPIs could include the percentage of customers retained and the reduction in customer complaints.
- Collect and analyze data: Data collection is essential for monitoring and evaluating the performance of an AI solution. Data should be collected continuously and analyzed to determine whether the AI solution meets its KPIs. Data analysis can also identify areas where the AI solution can be improved or optimized.
- Regularly review and refine the AI solution: AI solutions should be regularly reviewed and refined to ensure they meet business goals and deliver value. Refinement can involve adjusting algorithms or models, adding new data sources, or optimizing the solution’s infrastructure. Regular improvement can help to ensure that the AI solution continues to deliver value over time.
Of course, there are many other procedures that you may need to consider in the process. However, the three mentioned above are crucial and will help you improve your AI investments.
Investing in AI is one of the best ways to improve your business profits today. However, you’ll need to understand your business goals and needs before investing money into this project. You must also find good use cases to avoid wasting resources and time.
Another way to maximize profits is by building an experienced and productive AI team. Couple this with monitoring their daily performance, and you’ll enjoy more from your investments.