Artificial intelligence (AI) is here, and it's here to stay. It offers some incredible business benefits including increased productivity, efficiency, and better product quality while reducing costs. However, out of all AI prototypes, only a few make it into production.
AI solutions are expensive, and if not set up correctly, they can harm your business's ROI. It takes a lot of engineering and data sorting to create a practical AI solution that can improve an organization. Let's dig deeper into how AI helps improve your ROI, allowing you to prepare your business for the future.
1. Find Areas Where AI can Help You Increase Efficiency
The first step in introducing new technologies is always the most important one of all. Before you spend a fortune on an AI solution, it would be a good idea to identify business processes that could use an improvement. If the benefits don't help you increase your ROI, the solution will be impractical.
For example, if your project requires a lot of data pipelines and sources, an AI solution might be too expensive. For an AI solution to help, it has to have a clear goal or a valid purpose. It's up to you to define the purpose in the first place. Here's a quick overview of the benefits AI solutions provide:
- Improved efficiency by streamlining business processes and workflows
- Faster processes and shorter time to market
- Scalable capabilities
- Automation that eliminates certain time-consuming tasks
- Lower risk, fewer errors, and increased accuracy
Automation is one of the primary reasons for introducing AI in business processes. It's supposed to complete low-value, time-consuming tasks to give workers more time to focus on more important work.
Another example of how AI helps simplify tasks is its power to handle complex data. Modern AI solutions, for example, can process and analyze all kinds of data, including images and videos. Video processing used to be a time-consuming manual process, but today, AI makes the effort much more practical. Therefore, you need to pinpoint the areas where AI can help you save money by increasing efficiency.
2. Choose The Processes You Want to Automate
The framework of your business can tell you if an AI solution is a good fit. The times where you apply AI to an operation to see how it helps are over. Most businesses these days are aware of the power AI has to offer, so they focus on practical applications.
However, if you want to apply AI to get practical solutions, you must first know its limits. In other words, you need an excellent understanding of AI processes, workflows, and components. Start by assigning the AI with the tasks it's designed to complete, and reframe the ones it can't for manual workflows.
Of course, you can't do that yourself. You need to hire an experienced data scientist who knows how to adapt the AI and machine learning (ML) models to fix your operational problems. They will have to set rules, determine how to process data, filter information, and only then apply the AI solution. Sometimes, the process is simple, but the challenge here is to create an infrastructure that supports the tools they pick.
3. Organize Your Data
Getting an AI solution and hiring the right people to run it is one thing, but you won't get far without the infrastructure. AI solutions are only as accurate and efficient as the data they have access to. Small amounts of poor-quality data won't give you the benefits you expect. Moreover, it will harm your ROI, rather than improve it.
Organizing large amounts of data is easier said than done. You'll need data storage organized in a way to simplify the handling of unstructured data. You have to find a data storage platform that meets modern data demands such as data consolidation, high performance, and data reuse. The solution needs to run within existing data centers, connecting all available data into one building block your AI can use to manage processes and access information. All other approaches will have you turning in circles, so you'll end up spending more money than you can handle.
4. Set Team Roles According to Their Skills
By this stage, you already have a data scientist in charge of organizing data, but one expert won't be enough. You'll need more people with the right skills if you want the AI solution to help you increase ROI.
You can achieve the highest effectiveness of AI by making it an integral part of your business strategy. Create mixed-role AI teams and initiatives and align them with your business initiatives. That will help you deliver value to existing processes. Let's face it, more people and teams will help you generate more insights, allowing you to apply the AI to more processes.
The steps above will help you implement a practical AI solution into your operation, but it still doesn't mean that it will help you increase ROI. For an AI to work, you have to make the platform as transparent as possible. You don't need teams that focus on the future. You need people that focus on the now.
The solution needs to be effective in existing systems, but it also has to be scalable and ready for the future. Your engineers will still be responsible for introducing innovations and automation solutions, but their work must be easy to replicate and repeat. DIY-style solutions might help you improve the ROI of existing processes, but if it doesn't fit existing technologies, your ROI will shrink with time.
6. Create a Detailed Reporting System
Once you implement an AI solution into your operation, you have to keep a close eye on its performance. Regular reporting and system updates will help you develop the AI according to the latest trends. Without a feedback look, you will surely lose revenue and operational effectiveness.
Monitor your AI's performance constantly to see how much time it saves, how many mistakes it helped avoid, and how much additional revenue it delivers. The key metrics should give you a clear view of how AI helps your operation across all stages. Knowing which areas it affects positively and which ones it puts at risk will lead to a better system in the future.
Let's say that you use an AI to analyze videos and images to improve overall security. After some time and millions of analyzed images, the system can be applied to other, more specific processes such as customer reaction monitoring, etc.
7. Don't Rush
Even if you get all of the above rights the first time, it will probably take you over a year until the AI delivers the results you want. The average time for an AI to evolve from a prototype to production is around 9 months. Even so, most solutions never make it into production.
When developing an in-house AI solution, you should never rush things. Try to set realistic deadlines and follow the steps above to start the integration on the right foot. With that said, you should still be ready to extend integration times if you want the AI to work correctly. Remember, if you rush things, the integration process will harm revenue, and you won't be able to multiply ROI.
Creating New Business Opportunities With AI
AI is still an emerging technology that comes with long integration times and unexpected challenges. It definitely has the potential to improve your business and increase ROI, but it depends on many different factors. The smallest mistake in preparation can complicate the system, making the transaction much more expensive.
Take your time, monitor all processes closely, and keep improving data quality to get more out of your AI solution. Lastly, keep your expectations realistic, and success will follow.