The wider adoption of artificial intelligence and machine learning is “mission-critical” in businesses, according to a majority of CIOs responding to a survey by MIT Technology Review Insights. However, tech leaders say “internal rigidity” within their organisations threatens to stall progress.
The survey explored ways companies are bridging the gap between business intelligence and artificial intelligence, including a survey of 600 C-Suite executives working across 14 industries between May and June this year. All of the respondents worked in organisations with $500m or more in annual revenue.
The majority of those polled said their companies were in the process of scaling up the use of artificial intelligence through greater investment in data foundations and that multi-cloud and open standards were integral to any progress.
Just over half of those surveyed expect AI use to be widespread or critical to business functions by 2025, particularly to IT, finance, product development, marketing, and sales. In total 94% of senior leaders said their companies were already using AI in their line of business and would increase that use in the coming years.
One of the biggest barriers to the widescale adoption of AI within an organisation is “internal rigidity”, the survey found. This includes rigidity of organisational structures and processes, as well as budget constraints for new technologies that are needed to scale.
Despite the difficulties, most see scaling up the use of AI from current experiments and small-scale projects as a major priority, outlining its importance to data strategies, but explaining the issue of poor data management has to be addressed first. This will include investing in unifying data platforms that merge analytics and AI. Almost every leader said this was critical.
“Data issues are more likely than not to be the reason if companies fail to achieve their AI goals, according to more than two-thirds of the technology executives we surveyed,” says Francesca Fanshawe, editor of the report. “Improving processing speeds, governance, and quality of data, as well as its sufficiency for models, are the main data imperatives to ensure AI can be scaled.”
Content from our partners
AI in business: good quality data is vital
Mike Maresca, global chief technology officer at pharmaceutical retailer Walgreens Boots Alliance, said data was firmly at the top of the list of AI challenges the company needs to address, even after having upgraded its data infrastructure.
“We now have the right data platform, the right quality tools, and the right governance in place,” Maresca said. “But ensuring the data quality remains high, while we enhance our algorithms over time to continue driving the right business outcomes, is a key challenge as we scale.”
(edited)The level of investment will vary by industry, with the most money expected to be spent within the financial services sector, dwarfing other areas of the economy. For example, spending on data governance will increase by 74% by 2025 in finance compared to 52% across all other sectors.
The other two sectors that will see growth rates above the average of 52% are retail and automotive. Executives in these sectors were among the most ambitious about becoming AI-driven.
“Many companies don’t really know what they have in terms of their entire data estate or how they measure quality across it,” explained Jeremy Pee, chief digital and data officer at Marks & Spencer to the authors of the report.
“We’re addressing this in our new platform by putting cataloguing, searchability, data quality management, and other capabilities around every data element. We need to do all of this at the speed necessary to respond to customer and business needs.”
Chris D’Agostino, field CTO at cloud database vendor Databricks, which sponsored the report, said the insights are consistent with what the company is hearing from clients. He said that AI-ready data is no longer “nice to have” but critical to solving real-world problems and driving business outcomes.
Making AI work at scale will be dependent on multi-cloud and open standards, the leaders explained, with organisations turning to service providers to facilitate AI strategies – often using the services of two or more public cloud vendors.
“Having a multi-pronged, multi-cloud approach, and then incorporating APIs and microservices as part of our data architecture, are key for us,” says Rowena Yeo of Johnson & Johnson in an interview with the researchers.
In their conclusion, the researchers wrote that future success in innovating with AI will rely on the data, insights and tools they can source externally. “Data technology that favours open standards and open data formats is well placed to facilitate such collaboration.”
