3 major reasons that prevent the application of AI on a large scale!

And yet, now is the time for decision makers to capture data that is still unused and often scattered across disparate systems.

The approach of companies towards integrating artificial intelligence (AI) into applications is changing radically: not so long ago, everyone was fumbling, leaving data scientists to work in their corner to develop evidence of feasibility that would succeed or not. This period of experimentation, even of relative carelessness, is coming to an end. Companies are now convinced of the contribution of AI and want to apply it on a large scale in order to derive benefits, such as improved customer service of course, but also the beginning of a return on their investment. The pandemic has had an accelerating effect on digital transformation, what was planned to be done over several years is done in a few months. And the most successful companies in the future will be those that have bet on AI and data.

In a way, AI must become a subject like any other, a technology perfectly integrated into the corporate network, nothing more. But we are still far from it: until now, reflections around AI have focused on data analysis strategies, partnerships and the ethical conditions for large-scale application. But the main ingredient is missing: people.

Employees must understand and be trained in AI. They must also be convinced that the technology will produce synergies without threatening their position.

Paradoxically perhaps, humans have a major role to play in bridging the gap between AI, data and business applications. If AI was able to be isolated from the rest of the business during the study and development phase, now is the time for decision makers to seize the data and unleash its potential.

In other words, for the use of AI to be generalized quickly – avoiding the common pitfalls of scaling up – data professionals and operational staff must work hand in hand.

I see today, three major reasons that complicate the adoption of AI:

1. Data science/AI and business are too disconnected

We must stop distinguishing between AI and business functions. For example, in the case of customer experience, AI should be an integral part of the CX team. For the adoption of AI to be successful, data scientists must work with the operational staff, that they speak like them, that they understand the strategy pursued and share the same objectives. If the team is working on developing projects to increase sales, that should also be their goal. AI can only deliver results if everyone is on board.

2. The AI ​​model is only a first step

When AI is isolated from the rest of the business, data scientists don’t necessarily realize that they are just a cog in the value chain. Their model must be used in real conditions and 90% of their time must be devoted to refining this model to gain in efficiency. But for the AI ​​model to produce results, the operational staff using it must have reporting and auditing capabilities, an intuitive interface and security guarantees. Data scientists must therefore absolutely listen to users, ask them questions and understand the use that must be made of the tool to obtain the best results.

3. Pre-existing systems slow you down

Let’s say you’ve successfully implemented an AI-based CX project. The data and business teams have together defined the expected benefits of the project, perfectly aligned with the targeted strategy. The CX team sees the value of using AI and everyone agrees that the customer experience will only be better for it. But there is still a step to take: to feed the AI ​​model with the data that will produce results on the website, there must be a general modernization of the old siled systems. However, companies generally do not have the means. And that’s where intelligent automation (AI) comes in, helping to automatically migrate data between disparate systems, more efficiently and quickly.

Companies have moved from the study and development phase of AI to the heart of the matter: the integration of AI into businesses. But without onboarding data scientists and business teams, without an intuitive interface reproducing reality and without intelligent automation capable of migrating data, AI will not produce the expected effects. And it is only in this way that AI will offer its full potential for an infinite range of applications and that this technology will become a tool like any other at the service of companies.

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3 major reasons that prevent the application of AI on a large scale!


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