GenAI Experimentation Is Over. GenAI Business Transformation Is Just Beginning.

For as many articles that have been written about generative AI, we are only scratching the surface of understanding the long-term business implications. 

This makes sense. New GenAI tools (and GenAI controversies) seem to pop up every day, making it difficult for CIOs and other tech executives to gain a foothold. As an industry, we’re still largely focused on short-term experimentation — understanding the limits of certain tools, working to refine prompts, etc. — all to avoid being last on the AI table.  

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While this is a necessary step in the process, I fear that many businesses will get stuck here, failing to move toward the type of implementation that makes GenAI an integral business function. Even in their nascency, GenAI tools are already transforming businesses, and the rewards will only grow more lucrative from here. Ultimately, a short-term outlook on GenAI will catch up to businesses sooner rather than later — if it hasn’t started to occur already. 

FOMO Isn’t a Sustainable GenAI Growth Model

Much of the GenAI hype machine has been fueled by FOMO, the fear of missing out. As with any groundbreaking tech innovation, executives have been thrust into making decisions on a technology that is impossible to fully understand yet. A rushed GenAI implementation can have unintended consequences.

For one, there’s the threat of vendor lock-in. It would be unwise for a CIO to make a long-term commitment to a specific GenAI model or platform this early in the game.

Consider how quickly the technology has evolved in just over a year. There’s no guarantee that the top GenAI model providers today will be the top providers a year from now.

Going all in on a single platform now will only make transitioning to a different provider more challenging down the line. 

On the flip side, there are also risks for companies that move too slowly in their GenAI journeys — among the biggest is the risk of shadow AI. Shadow AI (which takes its name from Shadow IT) refers to the unauthorized usage of GenAI tools by employees. The threat of shadow AI can lead businesses to rush their GenAI implementations — and understandably so. Shadow AI presents significant data privacy risks for enterprises. For example, if an employee mistakenly inputs confidential company information into an LLM-based application, the application could very well store this information to further train the next iteration of the LLM, in turn making the confidential company data public. Major tech players like Amazon and Apple have already announced bans or restrictions on unvetted GenAI tools for exactly this reason. 

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Both threats can and do prevent business leaders from strategically approaching long-term GenAI implementation. The fear of being left behind is a powerful one, strong enough to drive action but too weak to sustain it. Soon enough, CIOs will have to tie GenAI implementation to clear business results to be successful. Simply focusing on experimentation won’t be enough.

A Transformative Model for GenAI Implementation

So, what does a sustainable adoption model look like?

In the macro, it moves beyond optimization toward true business transformation. Many business leaders are still focused on optimization — finding incremental ways to drive efficiencies with GenAI tools. Transformation, on the other hand, focuses on how to use these tools to help meet large-scale business goals. 

There’s no tried-and-true blueprint for sustainable GenAI implementation, but I’d like to offer some thoughts that can hopefully serve as a jumping-off point.

First, it’s important to recognize that your organization may not have the resources, employees, or expertise to handle GenAI implementation alone.

Yet, at the same time, you shouldn’t rush to fully outsource this responsibility elsewhere, as you might miss the opportunity to develop and train your employees on GenAI usage. The ideal setup is one in which you have a reliable partner who can focus on managing your GenAI tech stack, allowing you to focus on upskilling your employees and experimenting with potential use cases.

Prompts and LLMs are only a few of the required building blocks of an Enterprise GenAI tech stack. Companies moving from GenAI toy applications and experiments to Enterprise-grade solutions will quickly realize many more components must be integrated and managed. Outsourcing this responsibility, however, allows you to go to market more quickly, knowing that the integration part is taken care of.

What Does an Internal GenAI Experimentation Tool Look like in Practice?

Neither unauthorized GenAI usage nor commitment to a single GenAI provider offer the flexibility needed to reach full transformation. Fortunately, better options exist. 

The strongest adoption model for your organization is one where the CIO implements a safe and private GenAI Conversational Agent that has clearly defined terms of use and guard rails, allowing the company to maintain control of its information. In simpler terms: an internal tool that uses a ChatGPT or Bard/Gemini as its blueprint. 

This approach significantly limits the threat of shadow AI.

With an approved GenAI tool in place, your employees are less likely to feel the need to use an external GenAI Chatbot on the job. It also limits the threat of vendor lock-in, as you’ll have greater freedom to maintain control of your information and experiment with potential use cases. In short, it offers a more sustainable approach to GenAI — one that promotes data privacy, flexibility, and expedited development processes.  

In addition to tech stack management, your tech partner can also assist with the creation of this internal GenAI tool.  This private chatbot will help curtail instances of shadow AI and provide a solid foundation for further GenAI exploration. 

Two Useful Models for Use Case Ideation

In terms of other potential GenAI use cases, it’s important to have a solid understanding of how to land on use cases that are the right fit for your company. There are two approaches to consider for use case ideation: top-down and bottom-up. 

  • Top-down. A top-down approach is simple enough — set up workshop meetings with your key stakeholders (and perhaps a trusted technology partner) to discuss potential GenAI use cases that you can pursue. Consider what gaps exist within your industry for GenAI innovation and begin to narrow down which makes the most business sense for you. 
  • Bottom-up. A bottom-up approach empowers citizen developers and other employees to innovate and solve problems for your organization. Consider different methods of engaging your citizen developers, such as creating formalized contests or crowdsourcing initiatives that reward participation, like Hackathons. Also, consider how low-code and no-code technologies can help citizen developers drive innovation without direct involvement from IT. 

There are a range of potential GenAI use cases out there to consider, including well-established data analysis, modern customer service, and software development use cases. Fortunately, your tech partnerships can give you the flexibility to attempt these experiments and explore new use cases with minimal risk.

The End of the GenAI Hype Cycle

The GenAI hype cycle will only last for so long. Soon enough, the novelty of the technology will fade, and CIOs will be graded on how much long-term value they can glean from this technology. That grading process doesn’t start a year from now — it starts today. 

It starts with viewing generative AI through a transformative lens and implementing it into core business processes. CIOs will need to cultivate a culture of AI innovation and invest in the people who are on board with this mission. This is all easier said than done, requiring the vision to see beyond the instant gratification of a short-term implementation.

However, the businesses that can navigate the early days of GenAI adoption will be better equipped to reap the rewards two, five, and ten years down the line. 

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