The promise of AI remains spectacular. While working with AI, everyone’s enthusiasm increases, because AI does increase everyone’s speed of work. AI does automate the annoying parts. But where are the truly impactful implementations that generate ROI?
Gartner creates so-called Hype Cycles for all sorts of technology. Likewise, there is a hype cycle for AI. Gartner’s hype cycles tend to split up the denominator for such technology into specific manifestations. Likewise, as we all know, a distinction is made between Generative AI and Agentic AI (amongst dozens of other sub-types of AI).
Hype cycles
Gartner’s hype cycles consist of five stages, identifying the level of adoption by end-users (five stages of where a technology is between experimentation and scaling-impact):
- Innovation trigger (for the true innovators, the geeks).
- Peak of inflated expectations (Agentic AI is at the peak now, ready to tumble).
- Trough of disillusionment (Foundation models (the big LLM’s) are about to tumble, and Gen AI is now halfway its tumbling down).
- Slope of enlightenment (the hype-cycle stage at which users of the subtype of technology have learnt how to use it and reap its benefits).
- Plateau of productivity (the stage at which mainstream people, fixed-mindset team members and laggards start to adopt the technology).
Each subtype of technology on the hype cycle is thought to move slower or faster to reach the widely accepted plateau of productivity (from less than 2 years for full adoption and 100% market penetration; 2 to 5 years; 5 to 10 years; or more). Accordingly, Gen AI is thought to reach maturity in 2-5 years, and the same time span is thought to apply to Agentic AI.
Hype cycles and product lifecycles
The various hype-cycle technologies are also assessed for their level of maturity: where the technology itself can be positioned on another such graph: the product lifecycle, indicating how much users need or have in terms of product features and surrounding support services. A product lifecycle is also used to plot innovators, early adopters, mainstream users and laggards to the respective stages of the technology.
Where are the 'Copilots'?
Interestingly, Copilots do not really have a defined position on the AI hype cycle, but can be considered as Composite AI. Composite AI is the combination of Gen AI and agentic AI, if you would consider ‘agentic AI’ to also encompass decision intelligence (put simply: business intelligence or good-quality business data) and robotised processes.
While Composite AI has not even reached the peak of inflated expectations, the technology is already deemed mature (early mainstream) and would reach full market penetration in less than 2 years (so, while it is well behind Gen AI and Agentic AI, being mature, Composite AI or copilots would likely bypass these two soon).
The AI paradox
The paradox of truly seising the benefits Gen AI and agentic AI is also becoming visible: according to McKinsey research, only 1% would consider their AI strategy as mature, generating substantial earnings thanks to AI. Therefore, at this stage, AI is almost entirely about learning, experimenting and implementing AI in existing processes or redesigning processes to (learn-as-you-go how-to) leverage AI effectively.
How does Weagree stand out?
Weagree differentiates from almost all AI legal tech vendors in two ways:
- Contract creation is still a hard nut for almost all legal tech vendors. Weagree is a global leader where it comes to creating and organising Word-documents, OCR-retrieving contract data from Word-documents (and well-structuring all such data), and managing your contract know-how (your templates and your clause library).
- Deep integration of AI in contracting processes: because Weagree has been developing at a relatively slow pace (19+ years), the Weagree Wizard benefits to a maximum extent: our contracting technology is mature, is optimised for our users (power users, as well as casual users), and AI-features are deeply embedded, only there where you need them intuitively. Easy to use.
