3 Key Takeaways for Effective Generative AI Implementation

3 Key Takeaways for Effective Generative AI Implementation
3 Key Takeaways for Effective Generative AI Implementation

Since the release of ChatGPT two short years ago, Generative AI has made remarkable strides, permeating everyday life, and becoming increasingly integral to a swathe of industries. These tools, which leverage ‘Large Language Models’ (LLMs) are particularly adept at  processing and generating natural language.  The development of ‘transformer architectures’ gave these products superpowers, allowing LLMs to perform far better than ever before. Despite its versatility, this technology is still in its infancy. 

 

In a recent webinar, Neota explored some recent developments in the AI space, considered some of the primary risks, and discussed strategies that you, and your organization can readily implement when experimenting with this technology to maintain safe and effective use. 

 

Generative AI Is Not A Magic Wand 

 

As history has proven with most new technologies, they rarely are all pros and no cons. Gen AI has proven that it is not a perfect technology or magical genie. Consider hallucinations – instances of the technology generating convincing but false information. If bias is present in training data, this will distort outputs, raising ethical questions about fairness and representation. Other risks, around the misuse of Gen AI, pose significant security risks, complicating the distinction between truth and plausibility. If overlooked, these risks could create harmful and costly mistakes, even impacting business reputation in some cases. Therefore, when considering use cases for Gen AI, we must remain risk-conscious, sensible and vigilant. 

 

Key Takeaways 

 

When considering the implementation and utilization of Generative AI, consider these 3 things to leverage the true value of this technology in a risk-conscious and responsible way: 

1. Problems First, Solutions Second 

 

It’s critical to approach Generative AI with the right mindset. 

 

Allegedly, a Black and Decker executive once uttered the (now famous) line: ‘we don’t sell drills, we sell holes’. What matters to someone facing a problem is the solution to that problem – how it is achieved is less important to them. 

 

Given the level of hype and marketing, it can be seductive to believe that AI can solve any business problem. But it’s a classic example of focussing on the drill, and not the hole. 

 

AI is not a solution to a business problem by default, no matter how powerful and impressive it is. Unless the problem is clearly defined and directly resolved, there is always a risk that you end up buying a power drill when a screwdriver would have done the trick.

 

We still don’t have a consensus on what the best use cases for AI are in a business context, so the thinking, planning and design stages are more important than ever. Only through thoughtful experimentation and solution design will great use cases for the technology slowly emerge.

 

So, key takeaway #1: It is almost always better to begin with the problem or challenge you are facing, before settling on solutions. 

 

To design and create a solution that works best for your business or team, it’s important to involve and communicate with all relevant people, such as those who will inevitably use this solution, and those the task currently touches. This thinking and designing process will ensure a ‘technology’ solution isn’t created just because it can be or one that doesn’t actually work for the problem you have. 

2. Understand the limitations

 

Whilst the technology is constantly evolving and developing at what seems, from the outside, to be a rapid pace, there is still a long road to maturity. It is not a tool which can replace a person; risks and weaknesses exist, which, depending on the use case, can be significant if something goes wrong. 

 

Therefore, approach Gen AI as if it is an aid to augment your workflows, not a replacement. Rules-based systems, which use a predefined set of logical rules to determine decisions or actions, remain important. Combining the two will ensure the most risk-conscious output for you and your business. 

 

Additionally, human oversight is essential as part of the process, as the technology can hallucinate. With a human-in-the-loop approach, harmful or inappropriate outputs can be better detected. 

 

However, no matter what guardrails your team puts in place, it is important to always re-evaluate, adjust, and even pivot your approach as needed. Gen AI will continue to evolve, and your use of AI should also be continually reviewed and evolve alongside to ensure persistent safe and effective use. 

 

So, key takeaway #2: consider what the system knows, not what it tells us it knows. Taking the tools for what they are will mitigate big errors or gaps as processes can be designed around what the tools are capable of.

 

3. Identifying Use Cases 

 

Using this technology is not appropriate for every solution – implementation means a sensible and strategic use case for you and your business. 

 

Identifying suitable use cases for this technology involves understanding the unique capabilities and limitations of LLMs, and reviewing this as the technology continues to evolve. 

 

For example, Gen AI can automate repetitive tasks efficiently, which can lead to increased productivity and cost savings; however, its output may not always align perfectly with human or business expectations. Therefore, using this technology needs to be weighed against the potential risks for each use case, and how much is at stake when this happens. Whilst Gen AI can certainly augment processes, it is not a replacement for existing ones. 

 

So, key takeaway #3: when solving a problem, focus on the solution which best supports the overall needs/goals of your business or team. Gen AI and broader technology has to work for you, not the other way around. You may find implementing this technology is not the most effective or cost-efficient solution, and that’s why rules-based systems and traditional solutions must be factored in.

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