Driving Innovation Across Your Organization with Generative AI
One thing that has always been true about the business world is that it is largely about incumbents fighting to keep their edge against new entrants. Whoever can innovate the best is very likely to be the long-term winner. Generative AI, especially with user-friendly tools like ChatGPT, offers perhaps the greatest opportunity seen yet to effectively spread innovation across an entire organization. Larger companies should see this as a great opportunity to keep or extend their advantage, or perhaps a chance to catch up. However, a thoughtful approach is required to make the most of this potential while staying on top of risks, ensuring that, over time, it brings benefits even down to the core business model. Based on our experience with several major companies, we've put together a blueprint that shows what works—and what doesn't—when using Generative AI as a source of grassroots innovation projects.
Step 1: Make Tools Accessible to Everyone
The first and most essential step is to ensure that everyone in the organization has access to Generative AI tools. Providing broad access removes barriers and invites creativity from every corner of the company. Measures might need to be put in place adhering to individual companies’ policy on data confidentiality and ownership. Handling of these issues can include steps like setting up an enterprise service and communicating clear guidelines on using internal data. The key is not to let the initiative get stuck in administrative loops, but instead focus on the goal of distributing the service to employees.
By making these tools readily available, employees will be empowered to explore how Generative AI can be integrated into their daily tasks. Early use cases for individuals often include text summarization and generation, sentiment analysis, and customer interaction improvements, but the possibilities range far beyond that.
Be mindful of: Not hiding access behind complicated onboarding procedures or budget restrictions. However, set up cost tracking to ensure that high volume API usage is identified as soon as it has reached a high enough level (perhaps €500 to €5,000 depending on company size).
Step 2: Set the Tone at the Top
For meaningful adoption, clear direction from leadership is essential. Leaders should encourage employees to use Generative AI in their daily work and explore its potential, even if it means a slight drop in concrete output during the initial period of learning the ropes. By setting the expectation that innovation is everyone’s responsibility, leadership can help create a culture where experimenting with AI is not only allowed but encouraged and rewarded.
As Generative AI usage in this stage typically moves from purely individual exploration to a more team-based approach, new use cases might emerge, such as: automating routine communications, structuring and preparing planning sessions and workshops, or even generating creative content for marketing campaigns. However, the real value often emerges when employees begin experimenting with AI in unexpected ways.
Be mindful of: Leaders speaking to the benefits of Generative AI without truly understanding the concept of it. Employees will see through a presentation full of buzzwords and superlatives and may become disheartened instead of inspired. Consider hiring an external speaker or getting educated first (feel free to read more about how we can help) if you feel you might fall in this category.
Step 3: Encourage Experimentation
Most innovations do not follow a straight path from start to finish (think: Gantt chart) – they come through performing short iterations based on early hypotheses,facing setbacks, and doing small or major pivots in approach as you go along. The process embodies an “experimentation mindset”, which is a prerequisite for successful innovation within Gen AI.
The experimentation mindset will be most effective with access to ongoing feedback and a sounding board. One approach we have seen working well is to create a common space—like a weekly or bi-weekly all-hands meeting—where the latest news can be shared and where employees can showcase their AI-driven projects. These sessions should be interactive, with time for questions and discussions for example if Generative AI is a good fit for different tasks. While it’s helpful to have an experienced AI practitioner lead these meetings, it’s not always necessary.
In between meetings, a dedicated Slack or Teams channel can serve as a hub where employees across departments share ideas, solve problems, and suggest new use cases to explore.
Be mindful of: The community driven approach losing its momentum. Typical signs are decreased attendance in company-wide meetings and decreasing number of posts in Slack/Teams. While sometimes this engagement will come in waves, consider if administrative support should be provided to the community to collect demos and spark discussion.
Step 4: Measure Progress and Encourage Friendly Competition
The saying "what gets measured gets done" is true also in this case. In the early stages, the key metric should be the adoption rate—ideally something along the lines of “percentage of employees using Generative AI on a weekly basis”. Breaking down this metric by department can reveal where AI is gaining the most traction and where it might need additional support.
Regularly publishing these metrics can inspire some friendly internal competition, further driving adoption. It is also important to analyze why certain departments are more engaged with AI and to share these insights across the company to help others ramp up their usage.
Be mindful of: Expecting adoption to grow equally everywhere. The use cases of Generative AI are more concrete and easily discoverable in certain competencies and departments than others. It would not be unexpected to, in the first weeks, see 80% adoption in one place (perhaps among software engineers) and 30% in another (perhaps in the HR department). The key to look for in the early stages is a growing number.
Step 5: Track Impact and Expand Success
The initial benefit of Generative AI may appear as improved internal efficiency and putting a monetary value on this could be challenging. As the company gets more comfortable with the technology, one metric that is simple to understand and track is how many internal projects or products are using Generative AI. Given the broad applicability, you would hope to see such a number continuously increasing during the next few years, or else it’s time to assess whether momentum might have stalled.
Over time, measuring the business impact of AI-driven initiatives will become increasingly feasible, as they will more closely resemble a “classic” business initiative where the incremental cost and revenue can be estimated in a business case. Whether AI is playing a leading role or supporting role in these projects, the ability to link its usage to concrete business outcomes will be crucial for justifying further investment and understanding which initiatives are benefiting the most from the technology and in what way.
Be mindful of: “Shoe-horning” of Generative AI into any given project to get priority and resources. While this might be an effective way to build a career in some organizations, the reality is that with current technology, some projects are still more suited to Generative AI than others. When less obvious, leaders should clarify the expected improvements the technology would bring to a project compared to traditional approaches, and be inclined to approve smaller proofs-of-concepts over large initiatives.
Conclusion: Unlock the Potential of Generative AI
Generative AI has the potential to spark a new wave of innovation within large companies, but its benefits won’t come for free. By making AI tools accessible, setting clear expectations, encouraging experimentation, measuring progress, and tracking impact, companies can start unlocking the full potential of this powerful technology. There will undoubtedly be challenges along the way, but the benefits—greater efficiency, increased creativity, and a stronger competitive edge—are well worth it.
Data Edge has a proven track record of helping organizations evolve by employing the latest technologies and Generative AI is no exception. If this article has sparked your interest and you think we can achieve something together, let us know!