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Gartner recently found that 80% of large financial institutions plan to develop internal AI platforms rather than investing in third-party commercial AI.
The reasons for this development will likely sound familiar. In fact, they are the same reasons behind decisions to choose private rather than public cloud investments or enterprise content management systems rather than cloud-based content services.
Privacy is the deciding factor here, which for sensitive verticals has always been a key consideration.
Building an Internal AI Platform
However, a considerable amount of money is required to build generative AI systems internally, putting such systems out of reach for any but the largest organizations. AI will transform finance departments, but not in the way most managers think, said Tom Zauli, SVP and GM at Softrax. AI will only be effective if systems are built internally and true transformation will only take place with those companies using the technology as part of a long-term strategy.
AI will save human hours and bypass human error, provided the set-up is done correctly. The use of AI in the back office will also likely surface patterns within the data that will benefit the organization.
The principal question, Zauli added, is how organizations put this in place. Organizations need to follow a number of steps to follow this path.
- First, focus on choosing modules to process within functional areas inside the organizations. The goal is zero reliance on custom spreadsheets to source this data.
- Next, focus on the data flow across the workplace, allowing for a transformation layer between modules to handle business-specific cases. Make all integrations resilient at this stage to prevent data from going missing and ensure errors are handled.
- Finally, focus on processing between functional areas. This is the place to establish policies and robotic process automation.
“It's quite often that a better way of solving parts of the problem is out there, but the business leaders are not aware of them, so [they] continue down a non-optimal path,” Zauli said.
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The Upside and the Downside of In-House AI
For large corporations, especially those with specific, niche requirements or those dealing with sensitive data, the allure of internal AI platforms is strong, INTechHouse CEO Michał Kierul said.
These organizations benefit from tailored solutions that align closely with their operational needs and strategic goals. The customization and integration capabilities of internal platforms offer a significant advantage over commercial AI platforms, which, while powerful, may not always fit the unique contours of every business.
However, this strategy comes with hurdles. The initial outlay for developing an internal AI platform can be steep, encompassing costs for recruitment of skilled personnel, research and development, and ongoing maintenance and updates. This necessitates a substantial upfront investment and a long-term commitment to nurturing an ecosystem that supports AI innovation within the organization.
The expertise required to build and maintain these systems is another critical factor. Not every company has access to the talent pool necessary for such a venture, continued Kierul. Established vendors can pick up the slack here, with their ready to deploy solutions and dedicated teams to ensure their smooth operation.
Problems with this approach include the risk of isolation from the broader AI development community and the potential for slower adaptation to new technologies and methodologies. Commercial platforms, by contrast, evolve through competition and collaboration, continuously integrating cutting-edge advancements, Kierul said.
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Will Internal AI Take Off? It Depends
Companies with substantial resources and a forward-thinking approach to technology adoption will spearhead this movement, said Rohit Maheshwari, co-founder of NMG.
The off-the-shelf solutions provided by larger vendors don't offer the customization some organizations require, he said. He cited the case of one global client that needed highly specific financial AI functionalities that were unavailable in commercial platforms.
"Customization is a significant advantage. Experience shows that internal platforms allow for tailoring AI solutions to the unique needs of a finance team. This can lead to more accurate predictions and streamlined processes."
But he acknowledged the substantial investment in time, expertise and money that goes into developing and maintaining internal AI platforms. “It's a careful balancing act between customization benefits and resource allocation,” he said.
The likelihood of internal platforms becoming a trend depends on the industry's ability to address these challenges. As technology advances, a trend may emerge of companies looking for highly specialized solutions for specific tasks, which is only possible internally.
Internal AI Is Not for Everyone
Internal development is not for everyone, said Brosix co-founder and CEO Stefan Chekanov. It could, in fact, hold back development.
At its current state, AI is helpful with automation, but it is not applicable to every business type, no matter how hard some sell that idea.
However, internal development is impractical for small and medium businesses and will widen the gap between them and large corporations in terms of their ability to afford the newest AI technology.
For the moment at least, it requires advanced infrastructure and a whole team of experts dedicated to building and maintaining an internal AI framework, Chekanov noted. The best option currently is to outsource AI development to third-party experts, he said
“I think the only companies that can benefit from an internal AI model are not just those that can afford it, but those whose operations can significantly improve with the implementation of AI,” he said.
Leasing these solutions also has its problems. The thing about using a SaaS solution is that you do not own the product you are using, he said, so suppliers can change their product, however and whenever they want without considering your feedback.
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The biggest advantage to using an internal AI platform is that you have full control over it, and it is specifically tailored to your business needs, he said. This ensures your data remains within the organization for an extra layer of security and privacy.
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3 Factors Influencing the Decision
As AI models become more accessible, the real competitive edge for organizations lies in their unique, proprietary data, Michael Rumiantsau CEO and co-founder of Narrative BI said. There are three considerations here:
1. Data
AI technologies — whether developed in-house or acquired — are only as powerful as the data fueling them. Unique, proprietary data sets enable tailored insights and solutions impossible for competitors to replicate, offering a sustainable competitive advantage.
2. Specialized Applications = Superior Performance
When building AI solutions internally, it is challenging to outperform companies that have sunk billions into AI research and development. However, specialized AI solutions can offer superior performance in niche applications, thanks to better-quality, proprietary training data, he said. This specialization allows smaller players to carve out competitive advantages in specific areas (like Generative Business Intelligence or Generative AI for marketing) despite the overwhelming presence of tech giants, like Google and OpenAI.
3. Cost vs. Benefits Ratio
Building internal AI capabilities requires significant investment in talent, technology and time. For organizations where AI is not a core component of the business model, this path can be risky and bring operational challenges. Third-party AI platforms can offer sophisticated solutions without the complex R&D or long-term commitment to a specialized.