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Artificial intelligence(AI) is transforming industries, driving efficiencies, and enabling new business models.
For enterprises, embracing AI is no longer a strategic optionit’s necessary for survival.
AI encompasses a broad range of technologies and applications, each with their own uses and benefits.
Generative AI and the use of large language models (LLMs) likeChatGPTcan automate and simplify content creation andcustomerinteractions.
This includes drafting emails, generating reports, and providing customer support.
Alternatively, predictive AI leverages complex data sets to make recommendations and support decision-making processes.
An example of this is using predictive analytics to accurately forecast customer payments and optimize cashflow.
Director of Solution Marketing at Certinia.
Most organizations are here, developing isolated GenAI projects using fragmented datasets.
-Stage 3: Controlled This stage is like cooking a full meal from a detailed recipe.
Organizations have established a unifieddatastrategy, consolidating transactional and operational data into a single repository.
-Stage 5: Continuous Improvement This is the Michelin-star stage.
Ensure clean dataBefore jumping in, teams must take a temperature check on their current assets.
A clear signal a business might not be AI-ready is if it lacks clean data.
This plan should include policies for data collection, storage, access, and use.
It is also essential to have a process for monitoring and updating AI models as the data changes.
It also ensures that governance issues are not considered in a piecemeal fashion, but at an organizational level.
This means the solution should seamlessly connect with the companys existing systems and tools.
Teams must be sure to track attributable cost savings, efficiency gains, and revenue growth.
Identify your deployment team membersIdentify who will help to roll out the technology at each step.
Which stakeholders will be involved and when during the rollout process?
Consult the expertsAI is a rapidly evolving technology and leading edge skills are in short supply.
Companies should determine where they need to supplement their in-house resources with third-party expertise.
Is the technology easy to use, is it beneficial, is the training and roll-out efficient?
These are all factors that should be considered through feedback, so the deployment team can adjust accordingly.
The Pragmatic AI Maturity Model highlights this journey from random ingredients to Michelin-star organisation.
As companies progress through the stages, the focus naturally shifts towards building a culture of continuouslearningand adaptation.
This ensures they stay ahead of the evolving AI landscape and unlock the technology’s full potential.
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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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