7 Tips For Implementing Generative AI In Your Organization – Forbes

7 Tips For Implementing Generative AI In Your Organization
There is no doubt about it: generative AI is a transformative technology – a revolutionary tool that will change how we work. However, adopting any transformative technology requires careful thought. It’s not just a case of “futz around and see.”
If there’s one thing I’ve learned from my long career consulting with organizations, it’s that transformation never starts with the technology itself. You can’t just tell everyone in your business to start experimenting with ChatGPT right away because there are very real challenges and limitations. (For example, you can’t have your sales team uploading customer data to a tool like ChatGPT because that could potentially expose people’s personal data.)
So, yes, you want people to be using these sorts of tools as quickly as possible, but thoughtfully. Thoughtfully, carefully, and with all the support they need to get the best out of new technology. With that in mind, here are my tips for successfully implementing generative AI in your business.
Generative AI won’t do our jobs for us. Instead, we will use generative AI to do our jobs more effectively. To automate or streamline the more repetitive and mundane tasks. And to free up time for more value-adding tasks. As such, generative AI will not replace the need for very human talents like creativity, problem-solving, and relationship building (more on skills coming up) – but it will hopefully make work better for humans.
Adopting generative AI successfully requires a shift in culture and mindset. It requires a mindset that embraces curiosity, humility, adaptability, and collaboration – what I call the generative AI mindset.
The generative AI mindset has to start at the top and filter down across the organization. This means you need an organizational culture where people continually challenge the status quo, are comfortable with change (and failure), are not afraid to experiment, and are open to learning new things. I’m talking about a culture where people constantly ask questions like, “How can we create more value for customers?” “How can we create more value for the world?” and “How can we use technology to do that?”
In terms of skills and talent, AI delegation – the art of working out what we still need to do for ourselves and what’s best left to machines – will become a crucial skill. AI delegation, whether by managers or individuals, can vastly increase efficiency by automating routine elements of work such as data entry, processing and analysis, reviewing documents, scheduling and time management. And as with any form of delegation, the value lies in what can be done with the time saved – more time for tasks that require original thought, strategic thinking, decision making and relationship building.
What about other skills for success? You essentially want people to cultivate complementary skills that help the organization get the best out of both machines and humans. This will increasingly place an emphasis on softer human skills like empathy, complex decision-making, collaboration, and critical thinking – basically, the areas where humans have the edge over machines.
Of course, you will also need to build AI knowledge and skills across the organization. But this doesn’t necessarily mean recruiting AI talent. For the average business, upskilling your existing people and partnering with technology companies are likely to be the most accessible ways to tap into AI talent.
I strongly recommend organizations appoint a chief AI officer (CAIO) – a board-level position that promotes a better awareness of AI across the business (especially the leadership team), oversees the organization’s AI strategy, and ensures AI is being used ethically and effectively.
What if appointing a CAIO isn’t an option in your organization? Some organizations are appointing AI experts as non-executive directors to help the board understand AI technologies and how to implement them. In fact, this is a role I perform for a number of companies, and it’s a great option if you don’t have (or can’t have) in-house knowledge.
AI is nothing without data, so if you don’t already have a data strategy in place, now is the time to create one. And if you do have one, it will no doubt need updating.
When considering the data that will help you harness generative AI, I recommend focusing on the data that will solve your biggest business problems and help you accomplish your strategic goals. Also, think about the shelf-life of that data. While it’s useful to understand what happened in your business last week or last year, it’s even more important to understand what’s happening in your business right now. So be sure to think about how you can capture and act on information as it happens – or as close to that as possible.
Consider also the balance between internal and external data. Proprietary data is obviously incredibly valuable because, by its very nature, only your business has that information. However, external data (such as social media data) can be very useful.
Generative AI has big implications for businesses. As such, you will need to review your overarching business strategy to ensure it’s still relevant and update it in line with the amazing possibilities of generative AI. This means you will need to carefully examine the potential impact of generative AI on your business operations, your products and services, and maybe even your underlying business model – and then update your business strategy accordingly.
There are three fundamental elements that will help you leverage generative AI successfully: the first is fast, secure networks and connectivity (not just in your offices but also on the go); the second is data infrastructure (i.e., the technology to collect, store, and access the data you need); and the third is cybersecurity (the technology to keep your business safe from threats like ransomware, phishing, and breaches).
Your generative AI tools then sit atop that bedrock. You can tap into readily available tools like ChatGPT or its underlying GPT-4 language model, but you’ll have to consider the potential privacy implications. (At the very least, always be aware of whether the information you enter will be used for training language models and potentially shared with other users.) Lots of organizations that I work with choose to create their own secure version of GPT-4, but that may not be feasible, depending on your budget and expertise.
The great thing about generative AI is there are so many options for different companies – those on a shoestring and those with deep pockets. If you need in-house knowledge to help you decide which tool or tools are right for your business, it’s well worth consulting with an AI expert.


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