In the past few years, we have witnessed the growth of Generative AI tools at breakneck speeds. Enterprises and individuals are trying to incorporate them into their daily workflows—some very successfully.
Content generation has been an area of promise, and image and video generation has also gained momentum. But what about enterprises? What are they using it for? Generative AI holds immense potential for improving workflows and enhancing productivity.
For many organizations, incorporating Generative AI is a strategic play – to keep up with the changing times, while for others, it is an incredibly powerful tool that makes them successful.
For enterprises, Generative AI brings many possibilities and challenges. The uncertainties have kept many skeptical about whether Gen AI is a worthy investment.
Does it provide enough value to shift strategy and incorporate Gen AI? What improvements/advantages will it provide? Will these advantages warrant navigating the technical complexities associated with this change?
With this thought running through our heads, we have six steps that we feel are essential to fruitfully leveraging Gen AI—especially for enterprises.
We are basing our suggestions on studying successful implementations of Gen AI and the industry’s best practices that seem to be working as of date. From cultivating an innovative environment to scaling initiatives purposefully, each step is designed to guide organizations through generative AI adoption.
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1. An environment fit for innovation
Executive buy-in is a crucial milestone to ensure the whole enterprise is on board with AI adoption. It will help break down silos between teams and foster collaboration towards a common goal.
Within the scope of laid-out best practices, creating an environment for experimentation and failure-led learning will free the teams and allow iterative AI adoption.
Centers of excellence dedicated to AI adoption push individuals towards creative ideas and foster cross-functional collaboration within the organization.
2. A problem to solve
Implementing Generative AI just because everyone is doing it is an unwise approach. Before doing so, ask yourself and your teams about the problems an Artificially Intelligent system might be able to help with.
Depending on the business, an AI system might not make sense in your processes or make excellent business sense to incorporate it enterprise-wide.
Remember, Generative AI learns off training data, which, in the case of an enterprise, can be a significantly strictly regulated entity. Be sure of the level of exposure your organization is ready to provide to an AI model for training.
The quality and quantity of data will significantly enhance or reduce the effectiveness of your final AI solution. An AI solution can only be as good as the data it is trained on.
To identify all of the above, you will need to hire experts to audit your business process, identify areas where AI can bring about a change, identify data resources required to create a useful AI model and hire people to implement the solution.
All of this should be tested with a Proof-Of-Concept initiative to understand how a more involved effort to implement Generative AI enterprise-wide might work and what level of access the enterprise needs to provide.
Before doing any of it, however, enterprises need to identify a problem to solve, a strategy for implementing, a partner who has done this before (can be in-house), and setbacks/losses that you are ready to accommodate.
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3. Scaling the PoC
Encouraged by your successful PoC, you might want to implement Generative AI enterprise-wide. A word of caution here: Slow down. A strategic plan is essential to choosing your targets, emphasizing long-term targets and business objectives.
Establishing dedicated teams, refining the implementation, and auditing future relevance are also necessary.
These will help prepare an enterprise for regular incrementation improvements to the existing systems while creating an internal blueprint for responsible AI practices within the company.
Enterprises need to think about deployment pipelines, management of associated risks, contingency plans, risks, and the operational overhead associated with a more widespread implementation.
A simple mantra here is to risk as much as you can bear to lose.
4. Upskilling and hiring
While a Generative AI tool can help boost your business, its results are only as good as those of the humans using it.
Two different people using the same tool can generate vastly different results based on their level of understanding of using a Generative AI system. Prompting is a crucial aspect of Generative AI, and prompt engineering skills define the quality of results a person can achieve.
Ensure that the team members using the implemented Generative AI solution know how to use it well and what limitations the system has (and how to work around them). Enterprises might even need to hire resources trained for this specific situation.
5. Monitor everything
There might be better ways to measure the impact of your AI implementation than traditional ROIs. Beyond the metrics, enterprises should also consider competitive advantage, agility, and long-term values created by the AI system.
Organizations can evaluate the impact of their AI investments by focusing on broader business outcomes, such as operational efficiency improvements, customer satisfaction, and innovation acceleration.
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Embracing AI and the Future
Successful leveraging of generative AI requires a strategic and systematic approach. The steps outlined above are a great starting point, or you can engage with an AI Strategy Consulting partner to help you. As AI continues to evolve, businesses that embrace it stand poised to thrive in a competitive landscape.
