Vision and culture: The hidden complexity in AI and ML

The artificial intelligence (AI) and machine learning (ML) race is on – bringing opportunity and impact to business, society, and government. As this continues to accelerate, teams and organizations are challenged with how to drive and support innovation in this space. 

But, what if the technology is not the biggest obstacle? The buzz surrounding technology advances can make it easy to overlook some fundamental considerations. As you navigate your organization’s AI and ML pursuits, consider revisiting these building blocks, which from my experience, can help set you up for success.

Aligning your pursuits to vision and value return

One specific challenge facing organizations: are you proactively or reactively pursuing AI and ML? For most businesses, including those in the financial services sector, your digital strategy will explore AI and ML. Still, it’s equally as important to pressure test what you want to achieve from this technology. Consider: How do AI and ML align with my vision? And, why is this particular application critical to my customer, business, or market?

For my team, these questions were essential in guiding our digitalization strategy. We set a vision for where we want to digitally advance our business – as a way to reshape current market dynamics – and AI and ML were one of the vehicles for this pursuit. The AI and ML journey is a learning process though, and you have to be willing to embrace that, while also being open to experimentation, allowing for the flexibility to innovate.

Fostering a culture of innovation

With that said, do you have an underlying culture that will enable this journey? This goes beyond organizations merely talking about innovation, and requires them to dig deeper. The ability to create and maintain an environment that fosters innovation will be a determining factor in how effectively an organization can implement its digitalization strategy. This will influence whether you can maximize the collective possibility of the smart, driven, and capable people hired into your organization. 

Innovation is not specific to one particular type of company, institution, or body of people. And while it’s easy to link innovation to start-ups, we need to overcome that tendency because innovation is everywhere – it’s just not always harnessed effectively. At its core, innovation is when talent meets culture in an environment where new ideas are supported; barriers for progress are eliminated; and failures are embraced as learnings to create that next success around the corner. While a cultural evolution will require varying levels of effort and time for each company, it is a cross-organizational responsibility to inspire a culture of entrepreneurial freedom. This creates opportunities for everyone to drive innovation, regardless of your function in the organization. 

Continuing the dialogue

This week Amazon Web Services is hosting re:Invent in Las Vegas, where I will be speaking about the deep learning applications we’re pursuing at Siemens Financial Services for new AI and ML use cases, from strategy to concept development. I look forward to connecting in person to learn more about how your teams are driving AI and ML within your organization.

In the meantime, how is your organization balancing innovation with culture? What are some of the ways your team is working with AI and ML for the betterment of your business or industry? 

I look forward to hearing your thoughts.