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Author's profile photo Gavin Barwell

Should You Create Your Own Enterprise AI Platform?

Artificial Intelligence (AI) and Machine Learning (ML) are two of the most revolutionary technologies businesses have to deal with in the twenty-first century. Unfortunately, they’re also the least understood ones. AI and ML are both culminations of the programmer’s ultimate dilemma – how do I teach a program how to learn? Its core foundations were planted in science fiction films and books of yesteryear, but today’s it’s becoming a reality. The question for most enterprises is if it’s worth investing in AI, and if so, should they be looking at building one or creating a bespoke solution? Enterprise AI is a deep subject that most executives don’t truly grasp enough to make solid decisions.

Understanding What Enterprise AI Is and Isn’t

It’s a common misconception that Enterprise AI is artificial intelligence and machine learning agents scattered throughout a company’s framework. While, in the most basic sense of the word, this can be described as “enterprise-wide” AI, it’s not really Enterprise AI. The most important factor that defines Enterprise AI is its ability to interface with other elements within the company’s infrastructure. Thus, an AI agent on the factory floor could connect to and learn from the AI agent at the retail level and vice versa. Enterprise-level infrastructure like SAP allows for these nuanced interconnections while offering each AI access to the database that makes up the enterprise’s “Single Source Of Truth.”

Build or Buy? What is More Viable?

The choice facing most decision-makers when it comes to Enterprise AI is the type of enterprise AI they should go for. Should they invest time and money into constructing an infrastructure or spending it all to have one built for them? This decision hinges on a few critical elements, including:

Size of the AI Framework and Available Resources: For businesses with the resources to dedicate to building a customized AI solution, it’s more than worth it to put the money into it. Off-the-shelf systems can do generic things, but it will still take a lot of development and tweaking to get a retail AI framework to function the way the business wants. In the case of companies that don’t have a massive resource cache to help them, off-the-shelf AIs offer a great starting point without consuming all the company’s available resources. With time and skilled staff, the company can fit the AI and ML agents to the task they want while adjusting their expectations to what the AI framework can accomplish.

Patience and Goals: A built-from-the-ground-up AI framework can’t be manifested in the space of a few days. It’s not a financial report. It has to be carefully monitored and, for a while, the company may be at the mercy of its competitors who opted for store-bought AI solutions. However, with patience and an end-goal in sight, an AI framework can be more successful than any off-the-shelf AI solution. Tailoring the system to work alongside the company gives it flexibility unlike anything else.

Readiness and Base: The company’s current level of digital evolution plays a significant part in how soon the AI framework will be ready. AI can only depend on inputs from the core database as well as other AI and ML frameworks across the enterprise network. If a department is still lagging in readiness, it could significantly affect the time before the AI framework is fully functional.

Some companies may not want to invest the time and resources into building an AI solution. Depending on what you want the solution to do, you have several enterprise-wide options that work well with SAP’s internal systems. However, let’s not kid ourselves. This development isn’t as simple as a game of baby boomer trivia. It will take time before the AI framework can offer returns. Implementing a ready-made solution has the same drawbacks and will never fit the company like a bespoke solution. However, it will get the system up and running much sooner once the kinks are worked out. Considering the above mentioned factors will inform the business whether it should invest in building an Enterprise AI or buying one.

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