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The Hype Cycle of Generative AI



Generative AI or GenAI  is very much the latest buzzword, promising to “revolutionise industries and unlock new levels of productivity and innovation”.


Everybody’s talking about it, some are doing it but not very well, and the vast majority are not doing it at all. A bit like teenage sex.


The big consultancies and systems integrators are loving it and ramping up the hyperbole.


They're stirring up the excitement calling for new paradigm shifts and a transformation in how we work to meet the challenges and opportunities posed by this technology.


Some are calling it the age of disruption.

Doesn’t that sound all too familiar?


History has shown that periods of technological change, from Y2K to the dot-com bubble and beyond are more often than not massively over hyped and cause no end of disruption and fail to deliver on the promised outcomes.


Let’s get real here. 


AI has been around since at least the start of the century in commercial environments. It’s nothing new.


It’s the generative, human like responses of tools like Chat GPT that have captured the imagination and set the horses running.


The excitement around GenAI is setting off a flurry of activity, with many organisations feeling pressured to make sweeping changes.


Yet, just like in previous periods of technological upheaval, there's a risk of "throwing the baby out with the bathwater."


At Beyond we feel that businesses need to stop and take a breath before blindly following these advocates of new systems and transformations who stand to benefit the most from this hype cycle.


Why should businesses be cautious?


We see three main considerations for this caution.


Firstly, many companies are still developing their foundational data capabilities and associated data eco-systems.


This means a lot of the existing challenges of integrating legacy systems, managing change and transformations are still around adding complexity to the business already.


Jumping straight to GenAI without a solid data infrastructure and the basics of AI in place is like trying to drive a Formula One car when you're just learning to handle a VW Golf GTI.


In short, your data, systems and people are just learning to drive right now.


Instead spend time to really understand your current capabilities and use this to direct your foray into a new technology.


Secondly, the business landscape is littered with failed data initiatives – nothing wrong with that as it’s all part of learning and innovation – but consider the impact on your teams of more change and more technology when you know people are already tired of the constant upheaval of transformation and missed deadlines and missed expectations.


I have been working in the data space since the turn of the century and one thing that hasn’t changed is that for every advocate of data there is a detractor.


Generative AI has potential no doubt about it, but it's not a silver bullet.


Companies need to understand what they can realistically achieve with their current resources and maturity levels.


Take the opportunity to re-engage with your stakeholders to understand their concerns or frustrations and work with them to set realistic expectations.


Thirdly, as I have already alluded, most if not all our clients, are undergoing some degree of transformation and these are awash with challenges be they;


-Technical integrations or migrations from legacy systems;

-Designing new processes or customer experiences without breaking the business as usual, or

-Simply not having enough of the right resources and talent.


This is having a big impact on the business and teams and many will openly admit to feeling transformation fatigue.


Make time to ensure you get the strategic alignment sorted by aligning new technology investments with your long-term strategic goals, rather than being swayed by the latest trends.


Taking a thoughtful approach to Generative AI


Your business needs to take the same approach to Generative AI as any other investment.


To begin, it should understand the value potential or return on investment from implementing new Gen AI ideas.


Rather than jumping in on wherever there seems to be something of interest, you should start by understanding the potential to reinvent your value chain and develop end-to-end capabilities powered by generative AI.


To do this work through your customer journey and identify the business capabilities that deliver real value, align with your strategic goals and offer the opportunity to differentiate your offer from your competitors.


Secondly a major hurdle that will stop you dead in your tracks if you haven’t got a handle on it is understanding your data ecosystem and whether or not is AI ready.


Generative AI requires a new level of enterprise architecture, with a focus on connecting disparate data sets and technologies through a controlled and well governed operation so you have a forensic view of what you are pumping into your AI solutions.


To do this properly you must evaluate your current technology and understand what “AI ready” means for your organisation and proposed use cases.


Then you will need to develop the necessary capabilities for a data and generative AI ecosystems and ensure your Governance and critical areas like cybersecurity ready and resilient enough to support your plans.


Inherent with generative AI is the reinvention of work and processes.


This will mean different things for different businesses and use cases but for everyone it is likely to involve change for the people who work for your business.


Changes in both roles and responsibilities, but also requirements for changes in capability so new learning and skill development requirements.


You will need to develop your talent strategy to identify how work will change and the skills needed for your generative AI use cases and build the change management competencies to make these transitions a success.


Though it might appear so on the surface, generative AI is not an autonomous solution and requires people to make it a success. Ignore this at your peril.


Finally, while generative AI can drive value, it also poses risks such as bias, compliance issues, and security concerns.


Your plans must factor this into your solution design and deployment to ensure you use AI responsibly.


It’s important you use this opportunity to review your data governance frameworks and embed AI related requirements into your processes. 


Approach with caution


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