3 Things Business Decision-Makers Must Do to Create Better AI

Artificial intelligence (AI) is steadily changing how we do business. From automated customer services by chatbots to overseeing decisions on how banks give credit, AI is the future of business. AI has proven to be the only means for achieving efficiency and effectiveness in business operations, bringing about improved, innovative capabilities, and also offering invaluable business opportunities. AI-powered technologies are advancing rapidly and have become even more affordable with time. There is no industry that is not putting its best foot forward in adopting AI. In fact, Harvard Business Review estimates AI will pump $13 trillion into the world economy by 2030.

Tech Republic indicates 24% of businesses are already using AI models or planning on using AI in the next year. About 63% of the respondents say AI will be beneficial to businesses. Clearly, AI is good for business, with the potential to drive revenue and also save critical resources such as time. This explains why Dr. John Kelly, IBM’s Senior Vice President for Research and Solutions, said: “The success of cognitive computing will not be measured by Turing tests or a computer’s ability to mimic humans. It will be measured in more practical ways, like return on investment, new market opportunities, diseases cured, and lives saved.”

See, an effective AI model needs to be business-centric, and that requires you to be at the centre of the AI development process. Otherwise, playing a spectator role will land you on AI failed projects’ hall of fame. Without further ado, here are the three things you must do to create a better AI for your business.

Get Yourself Involved in the Process

Creating an AI model is far beyond what a data scientist can do. It is normally a process marked with significant business decisions that define business operations. As a key business stakeholder, you must get yourself involved every step of the way to make sure the team developing the AI model is following a path that is business-centred. The path that an AI project takes, from idea to full implementation, is time-consuming and resource-intensive. The bottom line is, stay in close contact with your project’s team executing the AI model to avoid any painful mishaps.

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Align the Right Objectives with Your Project Milestones and Success Measurements

Did you know that heavily-funded AI projects do go wrong or fail even for experienced AI professionals? Well, it happens quite often. It takes more than getting the most seasoned AI personnel with proper intentions and your best interests at heart to produce good results and achieve the defined objectives.

In 2019, Amazon’s Rekognition software erroneously identified 27 other New England professional athletes as criminals. The software matched the athletes to a database of mugshots in a test organized by the Massachusetts chapter of the American Civil Liberties Union (ACLU). A number of high-profile athletes, including Patriot’s Duron Harmon, were falsely matched with images of the arrested. The results were then verified by an independent scientist. According to the results, every sixth athlete was misclassified by the software. Kade Crockford, the director of the Technology for Liberty Program at the ACLU, mentioned: “Face surveillance is dangerous when it doesn’t work, and when it does. There are currently no rules or standards in place in our state to ensure the technology isn’t misused or abused.” Plus, it is incredibly difficult to eliminate bias in face identification completely. A similar failure in Amazon’s AI was a slap into Amazon’s reputation that eventually led to massive investigations by the police agencies.

Managing a project successfully from conception to implementation is an arduous quest. By making sure your team has the right objectives in place, milestones, and success measurements, your AI model will never veer off the road.

Consider How Your AI Model’s Decisions Impact People’s Lives

When building an AI model, it is imperative to consider all the ethical implications of your AI model’s decisions because it has a real impact on the community it is meant to serve. A good model is not judged by its ability to drive business results alone, but how the model’s decisions impact people’s lives – and that is what matters in any AI model design.

For example, if you are building an AI model that relies on human ethical decision-making in order to perform, then it becomes important for your tech team to avoid homogeneity. Data scientists are trained to encode intentional bias into AI models to make a decision, and when the team fails to consider any sensitive areas reliant on human judgment, then an unintentional bias is imminent. To avoid unintentional bias, stay in touch with your team to ensure all ethical considerations are in place.

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Key Considerations

When all is said and done, a business decision-maker should know what constitutes a better AI model. So whenever you set yourself to build a game-changing AI model, it is important to:

Label Data Effectively

Data labelling is a process that involves tagging objects in raw data with labels to help the model make accurate predictions and estimations. Only when you label data effectively will your model’s end results be additive to business, including detecting patterns and making predictions with high precision.

Train Data Properly

An AI model is as good as its training data. Your algorithms become meaningful if you train data properly, and all the accuracy, efficiency, and functionality that your model can give are owed to the training data.

Scale Annotation Pipeline

We know that finding the best annotation pipeline that fits your project’s needs is a rule of thumb, but to scale annotation pipeline is also key. Scaling your chosen annotation pipeline means having a robust toolset, managing your data quality, among other considerations. At the end of the day, all you want is an enhanced accuracy for top performance from your model, right?

Final Thoughts

AI is not a novel idea to marvel at as if it were some magic. Scientists have simply collected technologies and harnessed them together to make decisions that meet the objectives set. We have learned from this blog that it is people who make AI and go through the process of labelling data, training the data, and creating scalable annotation pipelines. This means your AI team deeply understands AI and is capable of building the most efficient AI model, yet they do not understand the business aspects of your model more than you do. So getting yourself involved in the process, aligning the right objectives with your project’s milestones and success measurements, and considering how your decisions impact people’s lives is what makes your AI successful.

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