Are you ready for Generative AI?
Generative AI is accelerating a global workforce disruption, the likes of which we have not seen since the Industrial Revolution. This is a grand statement – one that may not seem real in your experience yet. However, in the technology and data space, we are experiencing an AI gold rush with the rapid development of Generative AI.
There is not a single aspect of work that has not already been touched by the explosion of new tools, with OpenAI’s ChatGPT leading the initial explosion in use cases. This has led to a huge number of new AI-driven tools entering the market, and a rush to be the first to market with AI-powered products.
But where do you start? How do you ensure you are using AI in legal, responsible, ethical ways? How do you decide you are ready to start employing Generative AI?
What is Generative AI?
Let’s step back for a moment and consider what Generative AI is in the context of business. Put in it’s most basic concept, Generative AI is an AI tool that can create new content from natural language requests.
Using a prompt – like “What can Generative AI create?” – ChatGPT will give a conversational reply to the prompt. It’s incredibly useful, correct at a glance but not without risks.
In the above video, you can see the answer returned is what we would expect. A list of the types of content that Generative AI can create and a blurb about each is returned. What is really important is the disclaimer at the end of the reply.
“While Generative AI has shown remarkable capabilities in creating content, it's important to note that the quality of generated content can vary, and human oversight is often necessary to ensure accuracy, ethics, and appropriateness, especially in critical applications.”
Generative AI is not an independent resource that can utilised now to start creating copy or newsletters or product designs for businesses. To use it as such would be a massive risk to any organisation.
What Generative AI is ready to do for organisations who have the correct foundations in place is speed up existing timescales, enhance productivity for workers and improve the customer experience.
Generative AI has huge impacts on existing chatbot functionality, improving the conversational quality for customers. It cuts down on the development time needed to create new products through things like code generation. By having Generative AI give the first iteration of software code, a product design or a marketing email, the skilled individuals normally creating these from scratch can take, validate and improve upon the outputs.
What does readiness look like?
When we discuss readiness with our customers, we are really talking about change management. In the change management framework, it’s important to consider the three pillars of People, Process and Technology. Though, in the context of Generative AI, Data is the fourth pillar necessary to ensure readiness.
People
It’s vital to consider the people in the organisation that will be directly or indirectly impacted by the adoption of Generative AI. Let’s walk through some of the potential issues:
Lack of AI Literacy – Is a program of AI literacy designed ahead of a Generative AI rollout? To enable users in the organisation, they have to understand how best to use Generative AI, or there is a risk it will be unused by many, or ineffectively used with poor prompt design, biased outputs being disseminated or security risks as users haven’t been educated.
Legal & Ethical Concerns – While using Generative AI, the business may experience data protection violations or ethical concerns around bias, fairness and responsible AI use. Do you have an existing ethical AI framework?
Fear of Hallucinations, Deepfakes & Misuse – As ChatGPT disclaimed in its own response to our earlier prompt, the quality of content generated can vary and requires human oversight to ensure it’s accuracy. Hallucinations, where Generative AI returns content that doesn’t align with reality or the data it used to create the content are one example of this. With deepfakes, it’s possible to create content essentially put words in a real someone’s mouth. This is a legal and ethical issue. Of course, this leads in to misuse in general.
Fear for Job Security – With all of the hype surrounding Generative AI in the mainstream, there is bound to be some fear associated with this revolutionary technology. Organisations have a duty of care to employees to adequately educate them on how they will be using Generative AI and impacts to ways of working or resourcing.
Process
Process, or governance at its core, it really about protecting the business legally, by providing the frameworks and policies to manage the entire Generative AI lifecycle within the business. Let’s pull out some important considerations:
Use Case Prioritisation – Is their a Steering Group and organisational alignment to incorporating Generative AI in the business with strong use cases? Is their organisational buy-in to support these use cases?
Risk and Compliance – Has the impact of Generative AI on risk and compliance been considered and folded in to existing policies and processes?
Data Security – Does the business understand the data security risks associated with Generative AI? Have processes been implemented to support data safety with a Generative AI rollout?
Copyright Impact – Copyright and privacy concerns should be considered when implementing Generative AI. This is an area of concerns that is growing as Generative AI adoption grows. Copyrighted material may be accidentally created, or existing content plagiarised. Legally, an organisation has to have processes in place to mitigate this risk.
Technology
Technology is the foundation upon which we build Generative AI, and without ensuring we have the infrastructure to support it’s use, it leaves the business open to wasted resources. Let’s consider the potential risks:
AI Maturity and Management – Is the organaisation mature enough to adopt and manage Generative AI as a sub-service of AI? Without robust AI frameworks and infrastructure in place, there is a risk to Generative AI adoption.
Tool Selection – Do you have the knowledge to adequately assess and select the tools required for Generative AI? Weekly, there are new tools being developed and released; how do you pick the best one for your business needs?
Integration Complexity – Has the integration of Generative AI in to existing infrastructure been considered and assessed? Is their an understanding of the work needed to integrate as well as the cost?
DATA
Data is the fuel that powers the Generative AI engine, without quality data available in large quantities, Generative AI cannot provide deep value.
Data Availability – Has the Generative AI project secured access to data within the organisation that is relevant to the use case? Is this data available continuously and can this data access be automated? Is the available data diverse enough to generative robust outputs from Generative AI?
Data Quality – Is the data available for Generative AI consumption accurate and clean? Is it validated to ensure the data is not bias or unethical? Is the data available reliable enough to ensure Generative AI learns accurate and meaningful patterns?
Conclusion
Generative AI is a new product, but due to the hype (sometimes) surrounding it, organisations may want to jump in and get the latest technology without understanding the repercussions.
Advancing Analytics can stop you from being left behind with our Generative AI Readiness Assessment.