Using responsible AI to drive innovation in your business

When thinking about how to lead through innovation and impact, what springs to mind? Change management? Digital transformation? Emerging tech? Company culture? Or employee wellbeing?

Whichever one you pictured, in the pursuit of all of them is a tech solution that offers various benefits.

Artificial Intelligence (AI) is geared to drive productivity and efficiency through process improvements, which can lead to further innovation and more impactful businesses. 

But before you can bring all the benefits of AI to your business, you have to understand what adopting AI entails. 

AI isn’t “the Terminator”, designed to take over the world and erase the human race. Nor is it robots, in human-sized form with human-level intelligence.

Simply put, AI is code developed to mimic human intelligence and boost human productivity.

The OECD (Organisation for Economic Co-operation and Development) defines AI as “a machine based system that can, for a given set of human defined objectives, make predictions, recommendations, or decisions influencing real or virtual environments.”

While AI has seen a spike in adoption in recent years, with forecasts of a market value of $1.8B by 2030 and a CAGR of 32.9% from 2022-2030; user trust in AI is slowly waning, with a 35% drop over the past 5 years due to the ongoing issues encountered with the technology.

These issues range from misinformation, human rights violation, data leakages, representational harms or biases, and so on. 

However, generative AI when implemented safely, has the potential to affect employee productivity, where recent studies have shown a 66% increase.

It is also known for its efficiency and data-driven solutions, revolutionising businesses and organisations from their processes, to their strategies.

AI promises to drive further economic growth and empowerment across all industries, especially when adopted with the right strategies, risk management and responsible AI frameworks.

The following case studies show how AI has improved business performance and will give you ideas on ways to leverage the benefits of AI in your business.

The case studies highlight different types of AI that improved customer experience, safety, and fraud detection. 

1. Transform customer experience—Allstates, a leading insurance company in the United States, partnered with Boston Consulting Group (BCG) to develop a predictive machine learning (ML) model using ChatGPT. Predictive ML models are AI algorithms used to predict and forecast outcomes. Allstates’ predictive ML algorithm helped them gain better understanding of customer touchpoints, which led to an improvement in their customer service. Their customer service representatives were able to identify and resolve potential issues speedily, identifying issues before they occurred 71% of the time. 

2. Driving customer safety—Toyota, a multinational Japanese car company, used Amazon’s Web Services (AWS) to get real-time data on customer emergencies. AWS is a cloud hosting platform that provides cloud computing and APIs (Application Programming Interface) for its customers. Toyota hosted their data on Amazon’s cloud which enabled their teams to respond to customer emergencies within seconds. This led to improved safety for their customers. Toyota also uses generative AI to enhance the driver experience by providing voice assistive responses in their vehicles.

3. Fraud detection and risk management—Mastercard, an American multinational card service corporation, are using generative AI to protect their consumers from potential fraud. The generative AI techniques adopted by the company are used to scan over one trillion data points to predict the genuine intent of a transaction in real time. This has enhanced the speed and accuracy of Mastercard’s anti-fraud solutions, further protecting banks and customers. 

These examples are quite promising and show the potential of AI, including the ability for generative AI to improve businesses across various sectors.

However, with the waning trust in AI from consumers and the threats to data and security, there are some important aspects to consider when adopting AI technologies. 

1. Ensure you have a thorough understanding of your business goals and objectives before assessing any AI tech.
2. Associate a use case or business need that AI could potentially solve for you.
3. Make sure to run a pilot when adopting AI, and evaluate and monitor results and performance before wide-scale adoption across your business.

Ensuring you have a responsible AI framework in your AI strategy is also crucial to the success of your business. A responsible AI framework can help you avoid the risks and harms associated with AI technologies.

Some of these risks include data leakages, privacy violations, human rights violations, regulatory fines, representational harms and biases, and so on.

Responsible AI principles can save your business from reputational and brand damage, ongoing systemic risks, harms to customers, and invariably—profit loss or bankruptcy. 

The following responsible AI principles are recommended during the adoption, implementation and deployment of AI technologies within your business. 

1. Establish AI governance within your organisation. This includes company-wide policies on AI development and use.
2. Adopt data ethics and ethical data curation methods to ensure inclusive, clean, and diverse data practices. This leads to clean training data and accurate algorithms. 
3. Run fairness tests and metrics to ensure equal and safe output for all groups and communities.
4. Conduct safety tests such as adversarial testing and red teaming to ensure safe outputs, driving user psychological safety from AI interaction.
5. Include ML privacy methods, robustness techniques, and accuracy processes to protect user and enterprise data from malicious actors and leakages, and ensure accurate outputs.
6. Introduce human agency and human-in-the-loop processes to enable proper human supervision during the ML life cycle, development, and deployment processes.
7. Document information on ML models and datasets in the form of model and data cards, to provide further transparency and explainability of your AI systems. Also run explainability (XAI) tests using existing XAI tools.
8. Ensure ethical considerations are in place such as copyright violations and the use of copyrighted material in datasets. Develop your ESG (Environmental, Social and Governance) strategies to consider the environmental impact of large-scale models. Be aware of the dangers of deep fakes, and employ technical standards such as the C2PA (Coalition for Content Provenance and Authenticity) if generating content.

Final thoughts

Now you know how to drive further impact and encourage innovation in your workplace through AI technologies. It’s important to remember that AI adoption must be done with responsible AI principles in place to ensure success, customer satisfaction, and profitability.