K
Kathleen Martin
Guest
Artificial Intelligence (AI) is on the cusp of becoming mainstream. But research suggests that barriers such as a lack of data quality and technical expertise in the deployment of live applications are creating an implementation gap that will leave some organizations lagging behind in the AI race.
In the 2020s AI looks more and more like the internet did in the 1990s. Every day it’s becoming less of a fringe topic discussed and understood only by the tech-savvy and more like one that businesses across all sectors and of all sizes can’t afford to ignore. The next five years will see more and more businesses use AI to deliver insights and automation more widely across their operations.
However, shifting from AI talk to trials and then onto effective implementation is no easy task. Even for IT literate, technology-focused businesses, successfully implementing AI so that it makes a significant impact on the organization can be an uphill struggle. For other businesses it can feel like a mountain to climb.
Moving from AI talk to trials
Recent research conducted by Opinium for an AI in Business report captures this difficulty and dichotomy – talking the talk is one thing, but effectively using AI is quite another. So, while four out of ten firms are investigating the benefits of AI, only one in 30 is actually actively using AI in at least part of their business. True, this figure is driven by low rates of adoption by micro businesses, but even amongst the largest businesses only ten percent are currently using AI widely, while 35 percent are using AI in a limited way.
The distinction is even more marked when looking at SMEs. Only one percent of small businesses and two percent of medium-sized businesses are widely using AI within their organizations, while 18 percent of small businesses and 32 percent of medium-sized ones are testing it in a limited way. And around a quarter of SMEs and a little under a fifth of large businesses are still just at the stage of talking about AI.
Looking at this data, it’s clear that there’s an implementation gap - and not just across smaller businesses. The willingness is there, but many organizations are struggling to move beyond investigating AI technology.
Only 16% of large businesses have strong data foundations
Overcoming this AI implementation gap means tackling a number of issues within an organization, including technical and cultural. But the most important step is arguably getting the data foundations for AI within a business right including data quality and governance. Research shows that even among large companies only 16 per cent have the right data foundations in place for AI.
Strong data foundations give a business a single reliable version of the truth on which decisions can be made, and around which AI-powered solutions can be built. A data-driven business has three key characteristics:
In the 2020s AI looks more and more like the internet did in the 1990s. Every day it’s becoming less of a fringe topic discussed and understood only by the tech-savvy and more like one that businesses across all sectors and of all sizes can’t afford to ignore. The next five years will see more and more businesses use AI to deliver insights and automation more widely across their operations.
However, shifting from AI talk to trials and then onto effective implementation is no easy task. Even for IT literate, technology-focused businesses, successfully implementing AI so that it makes a significant impact on the organization can be an uphill struggle. For other businesses it can feel like a mountain to climb.
Moving from AI talk to trials
Recent research conducted by Opinium for an AI in Business report captures this difficulty and dichotomy – talking the talk is one thing, but effectively using AI is quite another. So, while four out of ten firms are investigating the benefits of AI, only one in 30 is actually actively using AI in at least part of their business. True, this figure is driven by low rates of adoption by micro businesses, but even amongst the largest businesses only ten percent are currently using AI widely, while 35 percent are using AI in a limited way.
The distinction is even more marked when looking at SMEs. Only one percent of small businesses and two percent of medium-sized businesses are widely using AI within their organizations, while 18 percent of small businesses and 32 percent of medium-sized ones are testing it in a limited way. And around a quarter of SMEs and a little under a fifth of large businesses are still just at the stage of talking about AI.
Looking at this data, it’s clear that there’s an implementation gap - and not just across smaller businesses. The willingness is there, but many organizations are struggling to move beyond investigating AI technology.
Only 16% of large businesses have strong data foundations
Overcoming this AI implementation gap means tackling a number of issues within an organization, including technical and cultural. But the most important step is arguably getting the data foundations for AI within a business right including data quality and governance. Research shows that even among large companies only 16 per cent have the right data foundations in place for AI.
Strong data foundations give a business a single reliable version of the truth on which decisions can be made, and around which AI-powered solutions can be built. A data-driven business has three key characteristics:
- Data insight is easily available at all levels within an organization and is used to back up every decision
- Data is combined in one single architecture to enable deeper analysis
- Advanced analytics and AI guide actions across multiple business processes
