Gartner: How to Succeed With AI Adoption in Industry
Gartner's annual Hype Cycle has brought to light the struggles organisations face with Gen AI.
It categorises the current state as a 'Trough of Disillusionment', explaining that "Interest wanes as experiments and implementations fail to deliver. Producers of the technology shake out or fail. Investments continue only if the surviving providers improve their products to the satisfaction of early adopters."
While some companies are benefiting from AI, most are not meeting expectations.
This suggests a strategic shift is needed in utilising Gen AI to avoid it becoming obsolete.
The Gartner Hype Cycle
The Hype Cycles from Gartner illustrate how innovations are being adopted, highlighting their ability to solve industrial challenges.
These insights help businesses decide whether to invest in new technologies or wait for more advanced developments.
The risks and rewards are evaluated to guide whether early adoption is wise or if technologies need more testing to prove their value.
- Innovation Trigger - a potential breakthrough arises, with significant media interest but no usable product or demonstrable results
- Peak of Inflated Expectations - Early success stories, alongside a share of failures. Some companies begin to pick the product up
- Trough of Disillusionment - Interest lags as experiments fail to deliver real results. Investments can sometimes continue, but only if providers can improve their products
- Slope of Enlightenment - More examples of success stories begin to occur. Second- and third-generation products appear, with pilots funded by more enterprises
- Plateau of Productivity - mainstream adoption takes off, with clearer criteria thanks to broad market applicability and relevance
Gen AI in manufacturing
Investment in suitable technologies could greatly enhance manufacturing processes.
Companies can achieve cost savings, time efficiency and process optimisation.
Still, premature adoption presents risks of underperforming expectations.
Gartner's application of the Hype Cycle to Gen AI investigates its impact on organisations.
Kaitlynn Sommers, Senior Director Analyst at Gartner, says: “Gen AI is proving to deliver process efficiency, better data insights and cost savings for procurement organisations.”
However, the integration challenges due to fragmented and low-quality data across procurement systems impede accurate results.
Integration complexities with various technical specifications also exist, yet its applicability across the source-to-pay spectrum continues to drive strong interest and adoption.
Benefits and barriers
In manufacturing, Gen AI aids by reducing repetitiveness and freeing resources, lowering operational costs.
It can help to enhance efficiency through automation, particularly in tasks like project scoping and contract management.
Yet challenges remain, such as data fragmentation, resistance to change and job security concerns.
Kaitlynn adds: “Organisations that delay action on integrating Gen AI into procurement processes risk falling behind as early adopters overcome these challenges and realise tangible benefits. Gartner projects that Gen AI for procurement will become a fully productive technology within five years.”
For manufacturing executives, Gartner's advice is clear: invest in robust data infrastructure, analyse vendors, tailor AI tools to specific needs and adapt existing processes.
Monitoring regulations and enhancing team skills will be vital for success.
While navigating Gen AI’s hurdles is crucial, early action by executives can yield significant savings and improvements in productivity across manufacturing operations.
