How entrepreneurs can think differently about AI

Artificial Intelligence

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Developing an AI strategy, setting the agenda and establishing readiness

At the outset, entrepreneurs need to develop an AI strategy and agenda that can differentiate their business and help them determine where they are on the AI journey. This process begins with an assessment of current operations and business performance to identify three to five core areas where AI and other emerging technologies, such as robotic process automation and machine learning, can bring business value. Organizations that are unsure about how to select these core areas should consider this thought exercise: if you could increase your headcount by 10%, where would you invest? How could you best drive value? This can help illuminate target areas. The key is to find what will actually move the needle for a rapidly growing business, as opposed to looking into the litany of possibilities that exist across the organization.

After selecting their targets for focused AI deployment, business leaders will need to take a few preliminary steps to establish readiness to move forward. First, facilitating interoperability across company systems, applications and platforms will enable AI models to access and integrate diverse data sets from multiple sources. This activity, in turn, yields better cross-functional visibility and more actionable insights into company operations.

Creating a secure IT environment is another crucial step, and startups and other high-growth businesses that are preparing to deploy AI strategically should consider working with a technology provider to protect their intellectual property and other sensitive information. Additionally, because individual members of the workforce are often the best (and sometimes the only) source of critical institutional knowledge, companies need to codify this into machine-readable information so they can build more agile, complete knowledge management architecture.

The AI talent equation

At its core, the strategic AI journey is also a people journey centered on augmenting human intelligence, enabling the workforce to focus on higher-value tasks and on deploying the technology responsibly. Knowledge workers in particular will see significant changes in their day-to-day work due to the advent of AI, but most of those shifts will be around what they do and how they do it rather than a widespread elimination of positions. “Jobs will evolve as opposed to not exist, and we’ll be able to do more with the same amount of resources,” says Darko Matovski, CEO of causaLens.

In addition, we’ll soon see the arrival of new skill sets that leverage AI to solve a host of niche business challenges. As such, entrepreneurial businesses should bring a whole-of-organization approach in designing AI-related processes and training initiatives with humans at the center. “There needs to be a group of people at your organization or company that have a sandbox and license to test and play around with all the new stuff that’s coming out,” says Alex Kotran, CEO of aiEDU.

Key risk and regulatory considerations in AI technology

As a recent executive order (EO) from the Biden administration implies, companies will need to develop strategies around AI safety and security from the very beginning. While the EO isn’t prescriptive, it does offer a glimpse into how governments and other authorities are thinking about the eventual regulation of AI. This is why forward-thinking entrepreneurs are focused on fairness, bias mitigation, transparency, audit readiness and other key features in establishing guardrails and other protocols. “Having strong frameworks and governance is very important to have in place as soon as you think about doing something with AI. It doesn’t matter how big or small it is,” says Samta Kapoor, EY Americas Energy AI and Trusted AI Leader.

Looking ahead, Matovski notes that the rise of causal AI, a new type of AI that uses algorithmic models to infer causality from data, will go a long way toward building overall trust and confidence in AI by enabling better decision-making across society, enterprise and government. Causal AI will also help reduce bias because, as Matovski says, “for the first time, humans and machines can speak the same language — the language of causality.”