According to Everest Group’s Pinnacle Model report, companies that bet on Artificial Intelligence are the ones that most improve their operational efficiency, the productivity of their workers and the experience of their customers, obtaining more than three times their ROI. Even so, many companies have reported that they have not obtained the results they expected from the AI solutions they have implemented. So what could have happened, and where should organizations focus their efforts to ensure the success of these technologies?
Here are 4 points to take into account to achieve ROI in Artificial Intelligence and not to fall into the typical mistakes that do not achieve the expected results of this technology.
1. Is the objective clear?
Many of the companies that have not achieved ROI from AI rushed to integrate it into their operations without stopping to ask how, where or for what purpose. This shows that they did not have a clear idea of what they expected to gain beyond a vague notion of productivity and efficiency. Wanting to be on the cutting edge sometimes plays that trick.
That is why it is important to analyze the opportunities that this technology can bring specifically to your business according to the needs and data available. Do we need to be more prepared for unexpected events? Do we want to improve the customer experience? Are we looking to minimize costs or time in a specific area? Are there AI solutions that can help in these areas? Do we have the necessary data?
2. Is it the right technology?
On other occasions we find companies that want to apply a specific type of technology to solve a problem for which it might not be the best option. It must be understood that each technique, model and algorithm has capabilities and characteristics that are better or worse for solving certain types of problems. They also have different needs with respect to the volume, type or quality of the data to be used.
For example, mathematical optimization techniques are used to find optimal solutions that maximize or minimize an outcome: mileage on delivery routes, waste in a production process, execution times, etc. However, Machine Learning models use large amounts of data to detect patterns and predict the future based on them: demand, weather, the probability of a customer’s leakage, etc.
3. Is the solution aligned with business objectives?
Another major problem of low ROI is the bad practice of applying AI in silos in an encapsulated way, without understanding it in the real context of business operations and processes.
For an AI solution to have a consistent business outcome you must first identify where improvements need to be made, what implications and efforts they will require and how they will affect strategic KPIs. Otherwise, there will be times when something is improved that really has no real impact on the company’s bottom line.
That is why we believe that the union of AI and the discipline of process intelligence is one of the keys to guarantee the success of this type of technology.
4. Is there a truly qualified team involved?
Finally, another reason for not achieving the expected results is the lack of talent, know-how or experience in the implementation of this type of solutions.
At this point, companies must first analyze whether they prefer to hire data scientists and create an internal team or hire the services of an expert consulting firm. Regardless of the choice, it will be important to have the necessary specific profiles with the skills, knowledge and experience required for the start-to-finish development and implementation of the solution.
Applying Artificial Intelligence and Advanced Analytics brings great value to companies regardless of the sector to which they belong. It helps organizations to make better operational decisions, be more agile and flexible in the face of change, save costs and time in activities and processes, and ultimately to operate in a more efficient way that directly impacts the experience of their customers.
Are you thinking about different AI projects and don’t know which one to choose? Here is an article that may be of interest to you: “Prioritising AI Projects: ROI“.
