Strategizing Innovation With Generative AI

It's clear now that AI is the future of business. For many companies in 2023, we saw the rise of AI as a competitive advantage. In 2024, we're seeing the rise of AI as a competitive necessity. Customers and users of both B2B and B2C products simply expect AI to be integrated on to platforms and because of this, we're seeing a massive shift in how companies are strategizing their innovation.

In this post, I'll provide insights on things to watch out for during integration so that you can avoid the pitfalls that many companies will most likely face due to the lack of foresight, poor planning, or how data is currently being used within the organization.

We'll go over approaches, challenges, and opportunities.

Integrating as a Competitive Necessity

Users expect AI to be a part of their experience. We saw many companies scattered across various industries scramble for AI innovation by latching on to the trend and also saw many startups emerge with AI-first products in attempt to disrupt the status quo.

We also saw many of these features seemed to be more of a gimmick than a true value add. This is because many companies are still trying to figure out how to integrate it into their products and services in a way that is meaningful and valuable to their users.

For users fence-sitting on whether or not to adopt your product, having data-first features can be a deciding factor - especially for those that are uninitiated to the technology and jump on hypetrains. The key is making it a seamless part of the user experience, as native as possible. This is particularly challenging for companies that have been around for a while and have a lot of legacy systems and processes in place that don't have centralization or have data siloed across the organization.

Adopting in these conditions can be difficult, but not entirely impossible.

Enabling Teams to Think Different

The first step to integration is to enable your teams (not just software, but your whole organization) to think differently about how they use, interact, and leverage data. There are tons of untapped insights and opportunities within orgs that are simply not seen because of the current processes in place. Adding structure to how it's utilized and democratized across the org can lead to massive improvements in efficiency and innovation that feel more natural and less forced.

What does this mean for software and data teams? Start with initiatives to share and collab on data across teams. This can be as simple as creating a data catalog that is accessible to everyone in the company. This can be as complex as creating a data lake that is accessible to everyone in the company. The key is to make it as easy as possible for people to access and use data in their day-to-day work.

Weekly reports and dashboards where people can see how their work is impacting the company can also be a great way to get people excited about how their individual teams are contributing to the health of the organization and how it can be used to drive innovation and eventual AI adoption.

Prioritizing Governance and Security

And of course, providing greater access to teams means that you need to be more vigilant about governance and security. This is particularly important when large internal metrics and insights are now being shared. We see governance as a three-way approach.

First, it starts off with development teams ensuring systems are secure in place. How data moves in and out of the network, encryption, and access controls are all important to consider.

Second, it's about the data itself. How is it being used? Who has access to it? What are the tools being used to access it? Are we passing it around in a secure way?

And third, it's about leadership. How are you ensuring that the data is being used in a way that is ethical and responsible? How are you ensuring that the data is being used in a way that is compliant with regulations and laws? Tooling can only go so far and it's important to have a culture of responsibility and accountability which is driven from the top down.

Unfortunately, convenience and speed often take precedence over security and governance. Don't let this be the case in your organization.

Internal Experimentation

If systems are so inherently complex, it might be a good idea to spin off greenfield projects with a few select members across teams to experiment new ways of using data effectively. The benefit of something like this is that it allows for a more focused approach to innovation and experimentation with key players and stakeholders that you want to eventually become data champions at your org.

Creating POCs also allow for buy-in from leadership and other teams that might be skeptical about the value of bringing large change for whatever lingering concerns there may be such as high costs or potential risks. Teams should experiment as often as they can.

Of course, this is not a one-size-fits-all approach. And busy teams might not have the time to experiment. Partnering up with us or other data development firms that offer services can help you flesh out these ideas and strategize how to best approach this transformation safely, effectively, and in a way that is meaningful to your employees and your users.