Many companies rush to adopt AI, particularly Generative AI, but can falter due to poor data prep and challenges with integration. As Inbenta CEO Melissa Solis explains, clean, accurate data is essential, as is any solution’s ability to integrate with a company’s existing systems. By focusing on specific goals and working with adaptable AI providers like Inbenta, companies can overcome many of these issues to effectively implement AI solutions and improve how their business operates.
While many companies are eager to adopt AI solutions, they can face significant hurdles in implementing them.
“There was a race for businesses to adopt AI, particularly Generative AI, because of marketplace pressure,” says Melissa Solis, CEO of Inbenta. “Everyone wanted to say they were using AI.” This rush led many organizations to stumble in their AI initiatives.
Everyone wanted to say they were using AI.
The importance of AI-ready data
One of the most significant hurdles in implementing AI is the lack of AI-ready data. Companies often underestimate the importance of clean, accurate data as the foundation for successful AI projects. “If your data isn’t clean and accurate, then the information pulled won’t be accurate,” Solis says.
Companies must ensure their data is properly prepared before attempting to build AI models. This involves consolidating data from multiple sources and verifying its accuracy. Inbenta AI’s Knowledge helps organizations unify and organize their data, making it AI-ready and accessible across the enterprise.
Challenges integrating AI with existing systems
Often, companies have invested heavily in their current tools and infrastructure, making it difficult to integrate new AI solutions smoothly. Yet Solis advises against believing it necessary to replace entire systems to accommodate these new technologies. “If a vendor tells you to replace everything with their solution, keep looking.” Instead, she recommends understanding your company’s specific goals and finding AI providers that can work within your existing environment.
If a vendor tells you to replace everything with their solution, keep looking.
Implementing AI successfully requires solutions that can fit into an organization’s current workflow. Inbеnta’s platform is designed to integrate with existing systems while still able to adapt to changing requirements without necessitating a complete overhaul.
Integrating AI into business processes
To effectively integrate AI, companies need to identify the specific problems they’re trying to solve and the processes they want to automate. As Solis recommends, “Don’t get caught up in the hype. Identify what you’re trying to accomplish.”
Inbenta’s approach focuses on understanding customer needs and tailoring solutions accordingly. Our AI solutions are designed to address specific use cases such as customer service, employee support, and operational efficiency.
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Navigating the AI implementation terrain
Even after companies adopt AI, they may struggle with measuring its success and justifying the investment. Solis recommends setting these markers from the beginning. “Identify what success looks like upfront to avoid moving targets,”she says.
As AI moves from experimentation to operationalization, companies need to consider long-term strategies. Solis advises to “address current issues but also look at where you need to be in 12 to 24 months.
It's crucial to slow down, understand your goals, and select a partner that can grow with you and adapt to change."
Successful AI implementation in enterprise organizations requires careful planning, AI-ready data, integration with existing systems, and a focus on solving specific business problems. By addressing these challenges and partnering with experienced AI providers like Inbenta, companies can unlock the full potential of AI to drive efficiency, improve the customer and employee experience, and gain a powerful competitive advantage.
In Brief:
- Ensure data is clean, accurate, and properly consolidated before AI implementation.
- Choose AI solutions that integrate with existing systems rather than replacing them entirely.
- Focus on solving specific business problems and automating targeted processes.
- Set clear success metrics and use analytics to track AI performance and ROI.
- Plan for long-term AI strategies that can adapt to future technological advancements.
