What Are the Risks of Failure in an Artificial Intelligence Project?
According to Harvard Business Review, nearly 80% of AI projects are destined to fail.1 Indeed, many companies underestimate the level of effort and resources they need to invest.
Failure Factors
Here are some failure factors, to name just a few:
- Lack of data
- Choosing the wrong opportunity
- Lack of planning
- Choosing the technology partner
- Poor change management
The good news? You have control over these factors because, in most cases, they are the responsibility of the company and not the technology partner.
Objective: Outsmart the Statistics and Increase Your Chances of Success
We present you with 7 tips to outsmart the statistics and increase the chances of success for your project. This is not an exhaustive list but rather guiding principles to help you achieve the desired results.
1. Consolidate Your Data in One Place
Most organizations have data scattered across multiple platforms and networks, creating silos. These silos can impact the data that your artificial intelligence solution can access, thereby limiting its effectiveness.2
Example: Understanding Customer Satisfaction
For example, if you want to analyze customer sentiment but only have access to data published on Twitter and not on Instagram or LinkedIn, you could have incomplete data that gives you a biased picture of reality. The first step would be to invest in a centralized platform that gives you access not only to social media comments but also to conversations with your customer service agents, satisfaction surveys, CRM data, etc.
2. Break Down the Problem into Specific Use Cases that Can Be Solved with AI
The problem to be solved may require multiple solutions, some of which do not require artificial intelligence. It is therefore important to accurately target the problem, then break it down into several parts, and identify how artificial intelligence can help solve it.
Example: Customer Sentiment Analysis
In the previous example, our goal is to better understand customer satisfaction. A use case for artificial intelligence would be to use natural language processing (NLP) to analyze the sentiment of posts on social media and other platforms.
3. Choose the Right Software According to Your Needs, Budget, and Systems
This tip may seem obvious, but it is easy to be influenced and choose the wrong solution for your needs.
Traps to Avoid
- Choosing the same software as your competitors – they do not necessarily have the same needs or the same budget!
- Choosing the solution that offers the most features – ask yourself the following question: do you really need them? A more complex solution may be more difficult to maintain.
- Choosing a solution that does not integrate well with your existing systems – this creates silos.
- Choosing the first option presented – do your research, talk to other users, ask questions, get support.
4. The AI Solution Should Be Able to Explain Its Logic When Needed
The artificial intelligence solution you deploy will only be as effective as its adoption. If your solution has a 95% accuracy rate, but your adoption rate is 25%, you will not achieve the desired results. It is therefore essential to understand the importance of transparency in your context.
Example: Demand Prediction to Optimize Inventory
You are implementing an artificial intelligence solution to predict your demand and optimize your inventories. In your field, it is essential for customers that you have the material in stock; otherwise, they will go to a competitor. The person responsible for inventories does not understand the logic used to estimate demand. Will they trust the system? You guessed it, if the people responsible for acting on the recommendations do not understand the logic, they will be hesitant to change their ways.
5. Prepare Your Data
Artificial intelligence runs on data. The better the data, the better the results. What does this mean in practice?
Example: Standardizing Date Formats
Let’s go back to our first example, where we are trying to better understand customer satisfaction. We have consolidated our data in one place and selected an artificial intelligence software using natural language processing (NLP) to analyze the comments. We want a weekly analysis to dynamically adjust our advertising campaigns. However, the dates are in different formats depending on the original source of information. The data is therefore not ready to be used.
6. Optimize Your Processes
Process optimization is essential not only to eliminate waste but also to modify it considering the new opportunities offered by artificial intelligence.
Example: Automating Customer Updates
You are integrating artificial intelligence into your customer service. The current process is to send a confirmation email to the customer once their order is placed, but no further follow-up is done. The process is manual, and the team lacks time to send regular updates. An optimization of the process in this case could be to schedule regular automatic updates to be sent to the customer about the status of their order. The new process considers the possibilities of technology and maximizes its value by offering better service to your customers.
7. Not All Solutions Need to Be AI-Based
As we saw in our first example, some parts of the problem do not require artificial intelligence. Consolidating data from social media platforms is essential to the success of the project but does not require a complex solution.
It is always important to return to the fundamental question: what problem are we trying to solve?
Conclusion
Artificial intelligence offers immense potential to transform businesses, but its success largely depends on your approach. By applying these 7 tips—from data consolidation to process optimization—you significantly increase your chances of success.
Remember that AI is just a tool: it’s your understanding of the problem to be solved and your preparation that will make the difference. Start by evaluating your current data and processes. Identify a specific use case where AI could add real value. And most importantly, don’t hesitate to seek guidance from experts to navigate this complex yet promising field.
The success of your AI project is within reach. So, which tip will you start with?
Need help?
- Iavor Bojinov, Keep your AI projects on track, Harvard Business Review, Nov-Dec 2023.
↩︎ - Afif Khoury, Data Consolidation: The Key To Unlocking AI’s Transformative Power In Organizations, Forbes, 11 avril 2024.
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Other References
United States Artificial Intelligence Institute, Certified AI transformation leader training material, 2023.
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