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How can manufacturers ensure a smooth integration?
For Nicholas Lea-Trengrouse, Head of Business Intelligence at Columbus, success is dependent on the operational approach taken.
AI projects must deliver value at every level to be a success.
So how can manufacturers achieve this result?
AI experiments are a thing of the past.
Head of Business Intelligence at Columbus.
So, whats behind this?
Gartners hype cycle for AI shows we are currently at the peak of inflated expectations.
So, how can manufacturers avoid falling into the AI void?
Whats the secret to AI success?
AI adoption success lies in a product development roadmap that helps organizations scale effectively.
Consider this scenario: a manufacturer aims to increase its profit margins by 10% next year.
Organizations often collectdatafrom multiple disparate sources, including Excel spreadsheets, manual entries, on-premise servers, andcloud-based systems.
To overcome this challenge, manufacturers need a robust data strategy that ensures seamless data integration and accessibility.
Organisations are inundated with data, but quality matters as much as quantity.
Manufacturers can then use these insights to optimize operations, improve decision-making, and drive better business outcomes.
Employee pushback is to be expected AI is there to empower not replace!
One of the initial hurdles manufacturers face with AI adoption isemployeeresistance.
So, what can be done to increase acceptance?
At this stage it is vital to involve employees in the AI implementation process to boost acceptance.
User adoption rates can provide valuable insights.
A growing data skills gap is holding AI implementation back whats the answer?
This highlights a substantial gap between the demand for these technologies and the ability to implement them effectively.
So, how can manufacturers address this skill divide?
The skills are already at a manufacturers disposal they just need to realize it!
One approach is to invest in training programs to upskill current employees.
Additionally, manufacturers can look to hire new talent with expertise in AI and smart manufacturing.
First, they need to break down their adoption into manageable stages.
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The views expressed here are those of the author and are not necessarily those of TechRadarPro or Future plc.
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