It’s no secret that this era of Generative AI (GenAI) has presented businesses with an interesting crossroads. We’re seeing the biggest opportunity to inject increased productivity in large enterprise organizations, putting AI into the hands of knowledge workers at scale for the first time. While AI has been embedded into the functioning logic of software for years, such as in photo apps, speech recognition, virtual assistants or translation, it has always been hidden from end-users and tightly controlled.
Companies have been investing in AI prior to this point, and now, in this current GenAI boom, we’re seeing it become user-friendly and applicable to use-cases that are more visible and relevant to the general public. This is something I’ve encountered in my day-to-day, working closely with organizations to determine and expand on use-cases.
Currently, AI is being used in the majority for fetching, summarizing and refining information and content, which can be great for the draft phase when starting content creation. It brings with it a range of benefits, from more time to focus on the strategic aspects of a role, to driving greater revenue, and marks a huge step forward in being able to eliminate manual document work that has slowed teams, and rarely been a highlight of any job.
However, it’s clear that automating the creation of the final deliverable or output is not something that GenAI is able to do well, and it is in these latter aspects where the true value of time-savings and increased quality actually matter.
The competing forces of GenAI that businesses must evaluate
For businesses, there is a lot to evaluate and understand as they weigh up where and how to invest here. There is certainly appeal in being able to maximize employee productivity, but there are significant considerations to navigate when it comes to what this means for the quality and accuracy of the content being produced. Ultimately it comes down to how far organizations are prepared to potentially sacrifice one for the other. The price of maximum productivity is control, so how much is a CMO, CTO or CEO willing to pay for this?
For now, at least, there needs to be a balance struck with the use of AI. It doesn’t mean to say that there aren’t opportunities for organizations to leverage benefits associated with developments in automation capabilities, but recognizing and remembering this balance is important for value to be gained.
What makes AI different to the employee content creator?
In line with this, it’s true to say that the growing availability of GenAI has raised a lot of questions related to the notion of organization control. This is especially true when we consider what AI brings to the table that makes us more curious, uncertain or distrustful when it comes to the content being created and produced.
Ultimately, humans have the potential to make the same mistakes or errors in their work and are likely to be consuming the same resources as AI, so surely there should be the same level of caution in place when it comes to the output?
Achieving the balance to gain the benefits
So, with this in mind, what is the best solution for organizations to be able to reap the incredible benefits of GenAI, unlocking great productivity, revenue and growth, without opening themselves up to risk?
Whichever the way the content is being produced, when it is a business-critical document i.e. one that is client-facing and therefore holds huge value, it doesn’t matter whether it’s a human or AI, having a managed ecosystem in place is critical to give this layer of compliance, and confidence. As we have already explored in this piece, the need to manage the potential for error or inconsistency isn’t a new problem for companies. These challenges have been a part of enterprise document creation for many years, but what AI has done is reinforce the need for – and importance in – having parameters in place.
Enabling a combination of rules-based automation and AI content creation
It has also made companies think about what they don’t want to be created by AI, and where they want to retain control when it comes to the content and information in business documents. This could be related to the brand, writing style, structure, storytelling, visual look or data. In these instances, what’s required is the combination of structured rules-based data and content set by companies, together with AI supported content creation where it makes sense.
As part of the work I’ve done in recent years scoping out AI needs within large companies, this has been a reoccurring theme as organizations wake up to the reality that neither employees nor AI are going to follow guidelines of their own accord.
Whether it’s brand guidelines, a specific tone of voice, legal disclaimers; all of these need to follow predetermined rules, standards, and legal compliance measures, bringing predictability which is crucial for accuracy.
Now with AI in the spotlight and increasingly becoming a part of large enterprise organizations, there comes a fresh opportunity to continue to reinforce these and highlight the critical role that rules-based automation plays, both by itself and in conjunction with AI.
The debate around how businesses can achieve maximum employee productivity and content accuracy when using GenAI is one that is set to continue, but there is a way to approach it and unlock the benefits to bring efficiencies without the worry or loss of control.
Having appropriate governance in place, no matter who is creating the content, is the only way for them to truly benefit from the opportunities to gain greater productivity while feeling confident in the output. Above all, clearly defining the purpose of the content and what it’s meant to achieve is critical for identifying what should come from a company through rules-based automation, and where it is right to use AI.
