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The question of leveraging artificial intelligence (AI) to improve business processes is no longer one of if or why, but of how. The benefits of AI are clear and proven. Companies around the world and in every industry understand that AI, while currently a competitive advantage, will soon become the industry standard due to the benefit of improving both efficiencies and the bottom line.
But as an increasing number of companies are in a race to deploy AI, they are learning that building AI solutions in-house is exceptionally challenging. For this reason, 60% of enterprises are choosing the vendor-supplied route (Deloitte, State of AI in the Enterprise, 2nd Edition) to deploy AI solutions. But once the decision has been made to use vendor-supplied artificial intelligence software, the type of vendor is still crucially important. Up until recently, the buy option was mainly limited to off-the-shelf AI software, but now, with the emergence of highly customizable AI, the buy decision is becoming more complex.
Off-the-shelf artificial intelligence software offers enterprises a great deal of advantages and benefits, such as reducing costs, shortening the timeline to production, decreasing the amount of development, higher resiliency, and eliminating the need to build an in-house AI center of excellence. And with such a large variety of off-the-shelf AI options to choose from, with each offering a different type of use case, enterprises can very quickly and easily find a relevant solution.
However, off-the-shelf artificial intelligence software also has a number of significant drawbacks. One of the first drawbacks that enterprises often notice about off-the-shelf AI is that while it is designed as a one-size-fits-all solution, this is often not how AI works in practice. The one-size approach often does not meet the very specific needs of different data or decision-making processes.
Typically for AI to truly make a difference and transform a company, it will require a more customized and tailored approach. That is not to say that use-case specific artificial intelligence software does not work. For instance, companies can have success with off-the-shelf chat boxes or other cookie-cutter style AI tools. But, once a company is looking to meaningfully expand its AI goals from one use case to the entire organization, these off-the-shelf, use-case specific AI tools are incredibly limited. They create a piecemeal, Frankenstein-esque type of approach to AI, in which a bunch of separate AI solutions are stitched together but provide no continuity of data or process.
This is how the customization approach differs from off-the-shelf AI software. Customization is a new approach to AI that has recently been introduced to the market. Being universally applicable, this approach is no longer industry or use case specific, but the technology is highly customizable so it can still support specific use cases. So, how is this possible?
The customizable approach to AI is based on a platform that has all of the foundational elements of AI already embedded, along with pre-developed suites of AI capabilities, such as natural language processing, computer vision, time-series, and speech processing. With all of this technology already layered into the platform, these customizable platforms are a completely separate approach to deploying AI than the platforms that data scientists use to actually build AI solutions. These customizable platforms typically include technology to ensure that it will be robust and stable enough to perform under all types of extreme data and requirements in production. Lastly, the high-quality customizable platforms will include an intuitive GUI that provides explainability, confidence, monitoring, and smart feedback functionalities that can be used by a variety of stakeholders – not just data scientists.
With this type of customization approach to AI deployment, enterprises are able to receive all the benefits of the off-the-shelf option, while also eliminating any of the drawbacks. But more so, this approach provides an additional benefit of supporting enterprises in achieving a full AI transformation using only one platform. Customizable AI platforms allow enterprises to scale up and scope out their AI goals, adding new, specific use cases for different business processes, while still only needing a single platform to transform an entire organization. This approach ensures continuity of data throughout all departments and processes.
With the benefits that this new customization option offers, it has the potential to productize AI on a global scale and accelerate the pace of AI adoption for individual companies. It can support enterprises in competing with the biggest and largest companies, both in terms of direct competition, but also in the race towards AI transformation and help close the gap that many tech giants have created. Just as platforms like Salesforce made CRM accessible to the majority of companies, customizable AI can make AI accessible to nearly any enterprise.
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