HomeSENEN Group CEO Ronnie Sheth Advocates for Practical Enterprise AI Implementation
Before embarking on your AI journey, always assess the state of your data—because poor data quality is one of the biggest risks that can undermine your efforts.
According to Gartner, organizations lose an average of $12.9 million annually due to poor data quality, resulting in wasted resources and missed opportunities. While that’s concerning, the positive side is that more organizations are recognizing the critical role of data quality and are becoming less vulnerable to such pitfalls.
That perspective comes from Ronnie Sheth, CEO of SENEN Group, a firm specializing in AI strategy, execution, and governance. The company focuses on data and AI advisory, operationalization, and literacy. Sheth notes that she has been involved in data and AI "since [she] was a corporate baby," bringing substantial real-world expertise to the table. Her firm also boasts impressive results, with a 99.99% client retention rate.
"Speaking practically, one common issue I observe is that companies rush into AI adoption before they are truly prepared," says Sheth. She explains that executive mandates often push for AI implementation, but without a clear blueprint or roadmap. This can lead to high user engagement but no measurable outcomes.
Even in 2024, Sheth encountered organizations whose data was "nowhere near where it needed to be." "Not even close," she emphasizes. However, the conversation is shifting toward more strategic and practical approaches. Companies are increasingly aware of this and are approaching SENEN Group first for data assistance, rather than immediately pursuing AI adoption.
"When such companies come to us, our first priority is to address their data issues," Sheth explains. "Only then do we move on to developing AI models. This ensures they build a solid foundation for any future AI initiatives."
"Once their data is in order, they can develop as many AI models and solutions as they want, and they will achieve accurate results because they now have a strong base to build upon," Sheth adds.
With extensive expertise, SENEN Group helps organizations correct their course. Sheth shares an example of a client who initially sought data governance support. Ultimately, they needed a data strategy—clarifying the purpose, approach, and desired outcomes—before incorporating governance and establishing an operational roadmap. "They progressed from raw data to descriptive analytics, then to predictive analytics, and now we're developing an AI strategy for them," says Sheth.
This focus on practical initiatives will be central to Sheth’s discussion at the AI & Big Data Expo Global in London this week. "Now is the time to get practical with AI, especially in enterprise adoption, rather than focusing solely on innovation, pilots, or experimentation," she states. "This isn’t the moment for that. It’s about making AI deliver tangible value. For enterprises, this is the year to make it happen."
Watch the full video interview with Ronnie Sheth below:
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![ScottMartinez]()
Der Artikel trifft einen wichtigen Punkt! In meinem Team sprechen wir seit Monaten über KI, aber bei der Umsetzung hapert es oft an der Datenbasis. Die genannte Summe von Gartner lässt aufhorchen - klingt nach einem Weckruf für das Management. Wirklich praktische Perspektive, weniger Hype, mehr Realitätscheck. Das mit der Datengrundlage checken ist wohl der wichtigste, aber oft langweiligste Schritt, bevor es losgeht 😅
Before embarking on your AI journey, always assess the state of your data—because poor data quality is one of the biggest risks that can undermine your efforts.
According to Gartner, organizations lose an average of $12.9 million annually due to poor data quality, resulting in wasted resources and missed opportunities. While that’s concerning, the positive side is that more organizations are recognizing the critical role of data quality and are becoming less vulnerable to such pitfalls.
That perspective comes from Ronnie Sheth, CEO of SENEN Group, a firm specializing in AI strategy, execution, and governance. The company focuses on data and AI advisory, operationalization, and literacy. Sheth notes that she has been involved in data and AI "since [she] was a corporate baby," bringing substantial real-world expertise to the table. Her firm also boasts impressive results, with a 99.99% client retention rate.
"Speaking practically, one common issue I observe is that companies rush into AI adoption before they are truly prepared," says Sheth. She explains that executive mandates often push for AI implementation, but without a clear blueprint or roadmap. This can lead to high user engagement but no measurable outcomes.
Even in 2024, Sheth encountered organizations whose data was "nowhere near where it needed to be." "Not even close," she emphasizes. However, the conversation is shifting toward more strategic and practical approaches. Companies are increasingly aware of this and are approaching SENEN Group first for data assistance, rather than immediately pursuing AI adoption.
"When such companies come to us, our first priority is to address their data issues," Sheth explains. "Only then do we move on to developing AI models. This ensures they build a solid foundation for any future AI initiatives."
"Once their data is in order, they can develop as many AI models and solutions as they want, and they will achieve accurate results because they now have a strong base to build upon," Sheth adds.
With extensive expertise, SENEN Group helps organizations correct their course. Sheth shares an example of a client who initially sought data governance support. Ultimately, they needed a data strategy—clarifying the purpose, approach, and desired outcomes—before incorporating governance and establishing an operational roadmap. "They progressed from raw data to descriptive analytics, then to predictive analytics, and now we're developing an AI strategy for them," says Sheth.
This focus on practical initiatives will be central to Sheth’s discussion at the AI & Big Data Expo Global in London this week. "Now is the time to get practical with AI, especially in enterprise adoption, rather than focusing solely on innovation, pilots, or experimentation," she states. "This isn’t the moment for that. It’s about making AI deliver tangible value. For enterprises, this is the year to make it happen."
Watch the full video interview with Ronnie Sheth below:
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Der Artikel trifft einen wichtigen Punkt! In meinem Team sprechen wir seit Monaten über KI, aber bei der Umsetzung hapert es oft an der Datenbasis. Die genannte Summe von Gartner lässt aufhorchen - klingt nach einem Weckruf für das Management. Wirklich praktische Perspektive, weniger Hype, mehr Realitätscheck. Das mit der Datengrundlage checken ist wohl der wichtigste, aber oft langweiligste Schritt, bevor es losgeht 😅
