One of the most important things leaders need to know as they race to capitalise on the impact of artificial intelligence (AI) is their programs will only ever be as good as the data they use to train their AI models.
While tools like ChatGPT and Google Bard have inspired and accelerated the adoption of AI in organisations across the globe, we are going to see an even bigger wave of innovation and impact as companies increasingly integrate their own data sets within their models. This is because the precision, quality, and reliability of the output from AI is directly linked to the data it is built on top of.
AI’s greatest gift
Training AI models with the right data is fundamental to their success. But AI’s greatest gift is not the answers it provides; AI’s greatest gift is its ability to inspire new questions, identify new opportunities, and drive action. And for businesses looking to realise these capabilities, human expertise is critical.
Humans know what questions need to be asked, which metrics need to be tracked, and how to respond to certain situations. Similarly, AI tools will need to be monitored, configured, and optimised based on real-world outcomes and unforeseen circumstances across industries and geographies.
The defining impact of AI and the central role humans have to play in realising it is best summed up by Harvard Business School professor Karim Lakhani, who recently said, “AI is not going to replace humans, but humans with AI are going to replace humans without AI.”
AI is set to be a more transformative technology than the internet and mobile. I’ve no doubt that the organisations that win in this new era of business will be those with the most unique data sets combined with the highest levels of human expertise.
The data differentiator
AI models are constantly learning from every engagement and striving to drive continual improvements for the people using them. When the volume and depth of insights being used are inadequate to meaningfully reflect the program or issue they’re trying to represent, there can be a detrimental impact on the solutions offered.
In contrast, when AI models are fully enabled with the right data, they are more likely to be optimised for the task at hand because they have a deeper understanding of what is happening, why it is happening, and what needs to be done to resolve it.
Data-powered AI in action
Some of the most impactful and desired use cases for AI involve improving experiences for customers and employees—from reducing wait times, offering personalised products and services, and automating mundane tasks so that workers can provide greater strategic value. In fact, research from Qualtrics shows 60% of customer experience leaders believe AI is going to give them a competitive advantage.
When we look closer at some of these use cases, the importance of having the right data to train AI models quickly becomes apparent.
In organisations where attracting and retaining talent is a key strategic priority, using feedback from employees can help AI identify departments with the highest flight risks, and provide recommended actions focused on retaining high performers and improving well-being.
For brands wanting to provide more meaningful, personalised customer experiences, AI can help them to quickly make sense of every piece of customer feedback they receive across every channel, and then take targeted and focused action in the moment. Similarly, a consumer researcher can use AI to review this feedback and rapidly identify the product and service improvements that will have the biggest positive impact for customers.
AI is accelerating and optimising outcomes across every business function. If businesses are going to rely on AI models for business-critical tasks, it’s therefore essential they are using the right datasets. Because if they’re not, the negative impact will be significant and costly, from fragmented and laborious customer experiences to ineffective and irrelevant employee experiences—the complete opposite of what they’re trying to achieve.
Addressing AI concerns and uncertainties
Among the excitement for the capabilities enabled there are uncertainties and concerns surrounding AI, which is why its critical organisations take a considered and targeted approach with their rollouts.
First and foremost, data security and privacy need to be a core component of every AI model. This includes complying with data regulations and—importantly—extends to ensuring models do not reinforce existing biases, misbeliefs, or misconceptions. To achieve this, organisations will need to apply a values-driven approach to AI.
Identifying the areas where AI can have the biggest impact on business outcomes, prioritising use cases, and being transparent with employees will also be hallmarks of successful AI programs. When this approach is taken, leaders ensure AI is being used to help the business achieve its goals while ensuring teams are enabled and invested to deliver success.
Lastly, as companies look to expand their datasets, it’s important to include structured and unstructured data, such as insights from call centre transcripts, social media posts, chat, and review sites. When you consider up to 90% of an organisation’s data is unstructured—and thought to be growing exponentially year on year—and that just 18% of companies are able to take advantage of it (according to IDC), tapping into the insights it affords is a key opportunity for businesses to differentiate themselves from the market.
AI will make business more human
The capabilities enabled by AI will help companies accelerate their ability to take the right action quickly, decisively, and with greater precision. It will enable the automation of many tasks, creating more meaningful, relevant, and human interactions.
Through the power of AI, every business and government has an incredible opportunity to deepen relationships with their customers and employees, which is crucial for long-term success. But this can only be unlocked when organisations deeply understand and deliver on the wants and needs of their customers and employees.
