How generative AI can boost your business

Generative AI requires two key components of trust for adoption: trust in data sources and trust in outcomes.

Generative AI engines are trained using a vast expanse of data that is largely available to the public. Not all publicly accessible data sources are reliable, though. Nor, in most cases, do they include all the data individual organisations will need to leverage the technology.

These shortcomings illustrate the importance of building a connected data infrastructure that will allow generative AI to thrive and deliver focused insights, drive better decision-making, spot risks, and capitalise on opportunities that might otherwise go overlooked.

Importantly, a connected data infrastructure that supports cross-functional data can help your organisation learn more about your customer and their end-to-end journey. Building a connected data infrastructure will also create opportunities for your organisation to use other advanced AI capabilities more effectively in the future.

Many organisations are already using AI in some form and we believe it is important to create and maintain a framework for the responsible use of AI which should include governance and ethical intentions, taking into consideration regulatory compliance, the impact on customers, employees, and other stakeholders.

The introduction of generative AI into your strategy will make this responsible AI framework more important, as it will be critical for maintaining an authentic relationship with customers, gaining their trust, and, in the process, protecting your reputation for responsible use of their personal data.

You will want to ensure that the technology is being used not only ethically, but also legally. You will want to make certain you understand the risks around using generative AI relating to inaccurate results, fraud, protection of your own intellectual property, and potential infringement on someone else’s intellectual property.

And you will want controls in place to protect against those risks. In addition to mitigating risk, building a responsible AI framework will allow your organisation to develop and use AI in a more optimised manner, allowing for faster speed to value for the business.

As part of a broader data and AI strategy, be sure to clearly state that generative AI is worth exploration and investment. Develop a clear strategy that identifies roles and responsibilities, including the people and teams that will be responsible for decision-making around your generative AI agenda and for the technology’s adoption. In the meantime, don’t wait to start experimenting.

Educate your teams, work closely with the people that run your business and identify use cases that will make a difference to them, and generate wider value to the business. Establishing a roadmap and conducting proof of concepts or pilots using generative AI will help you ramp up quickly and prepare the organisation for future adoption.

As this technology’s capabilities are expected to transform rapidly over the coming years, having a technology strategy and performing a continual scan of market capabilities against the requirements of your use cases will be crucial.

Generative AI holds significant potential to improve business processes and create a more engaging experience for customers. However, underpinning its success is the design and management of robust data principles and trusted data sources.

Like most organisations, your company may have already embarked on this journey to effectively manage and organise data for more effective use. Now, to take full advantage of that data, you’ll want to deploy an enterprise-class generative AI engine, one that you can train in a secure environment on your own proprietary data that is uniquely applicable to your business.

In addition to safeguarding your data while sharpening the value of your generative AI output, this also will allow you to safeguard the questions or “prompts” you give the engine to trigger new outputs. Those prompts or questions have inherent value and are best kept away from public platforms that can be accessed by competitors.  

For organisations to properly execute on their generative AI strategy and implement use cases they will require the right people and talent. These positions will include experienced data scientists, software engineers, data engineers, and others with industry and domain expertise, all of whom have been important to leveraging AI in the past.

But now you also may need new specialists, e.g., prompt engineers, who can help test and validate generative AI models and outputs. Ensure these teams are empowered and supported to explore this game changing technology to the benefit of your business.

Regardless of your organisations generative AI status, we would advocate for testing, refining, and validating AI solutions. In doing so you will revolutionise your current ways of working and drive enhanced business outcomes. 

This article originally appeared in The Examiner and is reproduced here with their kind permission.