The Framework for Positive AI Impact

Technology has become an ever-present feature of life and business — we might even forget that it is there. However, technology is integrated into our day-to-day activities and business operations and despite how organic our interaction is with its different manifestations, it has an impact, and that impact promotes outcomes, whether for people or for businesses.

In a previous installment of these reflections, I concluded that we must strive to develop and implement technology that derives positive impact. But what exactly is positive impact? How can we define or measure it? Can we measure it at all? There is no single answer to these questions, and, in fact, it has been the subject of much introspection for philosophers looking at technology since the times of Ancient Greece. 

Often, we can determine the impact of something in relation to the effect or influence it has on something else. It seems like a basic definition, but if we extrapolate to today’s B2B technologies, we can trace a parallel between “effect” and “progress.” In business, progress should always lead us to an objective or a result – one that is ideally aligned to our mission and purpose. This demands the question: how can companies establish processes, systems and guidelines that reflect their roadmap toward creating positive impact through technology?

The Framework for Positive Impact

The best example of tech innovation that is keeping leaders busy today is artificial intelligence. There are no questions as to why this is: the arrival of new consumer-facing tools and the rapid development of business-focused AI solutions have revolutionized the landscape.

For example, IBM recently announced watsonx – a new generative AI platform that helps businesses capitalize on the opportunities of generative AI and foundational models. The platform has three main components that focus on traditional machine learning, added to the new capabilities of generative AI, as well as the tools that help store, manage, and analyze data. Watsonx is also equipped with a governance suite.

While every aspect of this platform is a key element of a strong AI-strategy for any corporation today, in this article, I will zoom in on governance. What does it mean? In a nutshell it is a framework to support businesses, and drive responsible, ethical decisions across the implementation of AI into their workflows. It also allows companies to direct, manage, and monitor the organization’s AI activities, and employs automation software to strengthen their ability to mitigate risk, manage regulatory requirements and address ethical concerns without the excessive costs of switching their data science platform, even for models developed using third-party tools.

This approach is also supported in the four key beliefs of IBM’s approach to AI: it is based on the best open technologies available; it is trusted by being responsible and governed; it is targeted and designed for enterprises and business domains. And last, AI should be empowering, not meant only to be used, but to create value.

We are at a tipping point in the journey of AI innovation. It is the perfect time to ethically and under a purpose-driven approach introduce its many possibilities into business operations: the technology, the framework for governance, and the tools to manage them are available to help transform this momentum into not only unprecedented growth for companies and the economy but positive impact and outcomes for all.