The hype around the rise of generative AI technologies makes huge promises about the potential of the technology. Yet it would be fair to say the vast majority of organizations are only experimenting with the technology or using it in isolated use cases.
The problem with such as rapidly maturing and expanding field of technology is the number of established vendors and enterprise-worthy products is low. In addition, proven adoption frameworks and journeys are rare, leaving a lot of uncertainty for new adopters.
This article explores some of the things organizations should do to ensure success when considering implementing generative AI at scale.
1. Crafting a Generative AI strategy
Understanding the Generative AI landscape
Generative AI, typified by advanced models like ChatGPT, is rapidly evolving and comprised of many different categories of technologies. These technologies have potential to generate high-quality content across many use cases (emails, blogs, product descriptions…), improve customer service via automation, boost sales leads via CRM and sales integrations, or facilitate market research.
However, to leverage these benefits on more than an individual level, businesses need a generative AI strategy that addresses how to drive adoption and effective use of the selected technologies.
Setting the framework for Generative AI
A robust Generative AI strategy is comprised of:
- Understanding the goals that drive the organization
- The performance measures of employees within the organization
- Identifying use cases to enable the organizations' goals and improve the performance metrics of employees
- Identifying the right technologies to address the use case
- Preparing a roll-out program with tech support, change and comms
2. Building a convincing business case
Why a business case is necessary
Generative AI involves a significant investment of time, resources, and capital. Hence, it's essential to build a compelling business case that highlights the potential benefits and returns on investment. A well-structured business case will secure buy-in from the leadership team to get a program up and running.
Quantifying the benefits of Generative AI
Generative AI is focused on creative tasks most frequently completed by domains such as product design, customer service, sales and marketing. When building your business case, identify specific use cases where generative AI can deliver substantial value. A simple way to do this is to identify the relevant processes suitable for automation, the steps within the process which can be automated and quantify the value of the possible automation.
For example, sales staff may spend a significant portion of their time generating leads via email. Generative AI can be used to generate emails, email campaigns, and can be integrated into CRM and sales tools to distribute the emails.
3. Choosing the right technology
Understanding your technology needs
The choice of technology is crucial in the adoption of generative AI. Businesses should consider fundamental factors like ease of integration, scalability, security, and compliance with regulations. However, specific to generative AI there are also extra concerns such as the technology's ability to handle enterprise-specific data securely, or a decision between a prebuilt product such as Jasper a solution native to a business application or a completely bespoke solution.
Choosing a suitable generative AI tool
There’s a valid assumption that the use of raw ChatGPT is not a good solution, both on the individual level and to scale across an organization.
Better tools will be tuned to the specific use cases that you’d like to apply generative AI to. However, there are many on the market, to understand a few examples:
- Microsoft is developing copilot tools that integrate with their business applications, Outlook and more to perform common tasks such as writing a sales email or even predicting disruptions in your supply chain. These copilots will be available and integrated into the tools employees use every day and some of them may not require any separate licensing.
- Jasper, Writesonic and other generative AI assistants offer a multitude of templates to use generative AI to write an email, research and write a blog or many other common tasks employees complete. These tools require minimal technical foundation and come at a relatively low cost.
- Bespoke applications of generative AI can be produced for an organization for purposes such as AI-augmented chatbots, querying massive knowledge bases and more. For example, using Azure OpenAI you can develop a solution built specifically for your business accelerated by OpenAI’s models. These tools can be highly tuned to the requirements of your organization but are intensive in terms of the investment of time and money required.
Where possible we lean towards the first two options to begin a generative AI journey, with the third being best suited for large, technologically proficient and AI-experienced organizations.
Picking a tool that can address many different use cases with minimal customization allows for large-scale programs to be launched with minimal complexity from the technology.
4. Rolling out the technology incrementally
The importance of a gradual rollout
Implementing generative AI is a complex process that involves significant changes to business processes and workflows. A gradual, phased rollout can help manage this complexity and minimize concurrent support requirements. It allows businesses to test the technology in a controlled environment, gather feedback, and make necessary adjustments before wider deployment.
Designing your rollout
Common approaches to technology adoption will work here, for example starting with a small group of tech-savvy employees to implement the tool and to act as champions for the rest of the organization. The goal is to segment groups of users to ensure you have the appropriate capacity to do things like customize training to the groups' roles and responsibilities, ensure sufficient tech support and more.
5. Driving adoption through Change Management
The role of Change Management
Change management plays a crucial role in driving the adoption of generative AI. It involves preparing the organization for the changes brought about by the new technology, managing resistance, and facilitating a smooth transition. Your rollout should be designed with fundamental change principles in mind.
What are some fundamental principles?
Rather than provide change management 101 here are some of the core things I always think about to drive the adoption of technology solutions:
- Clearly planned communication and roadmaps
- Differentiated learning and development
- Full documentation and training material
- Ongoing and iterative support, both technical and user-focused
- Employee reward
- Visible leadership buy-in
Combining these things is a simple means to drive success at scale.
The successful adoption of generative AI across an organization is more complex than the simplicity of the tools would suggest. Organizations won't succeed relying on ChatGPT alone and require a well-planned strategy and implementation roadmap to be successful.
If you’re interested in how you can deliver this for your organization, Columbus’s Generative AI strategy framework runs through the tips in this article and more to get you on the right path.