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Is it worth involving AI and machine-learning in your business model?

AI and machine learning have become part of a new tech boom that’s expanded since the successful introduction of ChatGPT. Now, companies abroad have released numerous types of AI models to hopefully catch a piece of the big picture. We’ve seen waves of chat models similar to GPT, and today, AI-generated content is a major point of contention depending on its use.

With so much development, buzz, and offers surrounding AI, any business owner and enterprise leader will no doubt make serious considerations for it. If AI can improve operations and facilitate better performance, it’s worth looking into, right? But like all technological solutions, the “next big thing” or newest development is not always worth onboarding. Like anything, careful considerations have to be taken.

Key benefits of AI and machine-learning services

Just about everyone wants to offer some kind of AI functionality. But what service is worth onboarding, and what’s just an attempt to grab the coattails of a popular topic? Well, let’s take a look at a few known, tested uses of AI in the business realm.

Workflow automation and customer service

One of the ideal advantages involving learning machine models is the automation of redundant tasks. This has been a foundational characteristic of machine learning before the term “AI” entered the field. The idea is to use AI to identify repetitive tasks and automate them without human involvement, saving time and money.

That can range from simple email campaigns to data insights gathered from market analytics, creating better decision outcomes.

Customer service is another high point of AI. Simple chatbots existed for years, even before ChatGPT. It was a handy way to assess potential questions and customer concerns without direct human assistance. Or, the chatbot was a handy way to direct queries to the correct resources. Whatever the case, it’s an example of a redundant task that simple AI models can manage. When they do, it allows staff to focus on meaningful, important tasks and avoid burnout from repetitive work cycles.

Data Analytics

No surprise, data and information are incredibly valuable to any enterprise requiring marketing metrics. AI and machine learning can play a vital role in that. For example, AI can aggregate demographics and user metrics to create comprehensive reports. As mentioned, these reports can turn into meaningful business actions. AI can also examine client and customer behavior to generate better responses and gauge brand relationships.

With these examples in mind, it’s alluring to want to involve AI and machine learning in your enterprise operations. But it’s not as easy as it sounds. AI is not right for every organization, and it’s a misnomer to add tech solutions to your business just because it’s the “new thing.” Any tech solution requires monitoring, maintenance, and onboarding capital. Therefore, you have to look at your organization’s workflow and understand whether AI can help.

Onboarding AI and its Challenges

So, we’ve covered in brief a few benefits of AI and machine learning to emphasize what it can do. You will notice a lot of conventional machine-learning solutions involve automation, reduction of repetitive tasks, and analytics. That is because these are some of the most common and popular use cases for AI. While AI models can be trained on complex tasks, enterprises start with the basics.

However, there are challenges involved with AI integration.

Time to implement

As mentioned, any form of technology or service takes time to fully integrate with an enterprise. That involves user training, familiarity, software compatibility, security, and adding infrastructure to support the addition of hardware.

Depending on the needs of the business, this process can take too long to be practical.

Compatibility problems

The discovery of compatibility problems becomes apparent during implementation. How well do users understand the AI functionality? Does it work in congruence with existing infrastructure and software? If not, implementation can create more problems, not solve them.

Complexity

AI implementation can introduce new levels of complexity existing IT resources cannot account for and maintain.

Mandate Requirements

AI is still rapidly changing. While useful, the entirety of AI as both a concept and technology also presents serious security concerns. It’s those concerns encouraging mandates, reporting requirements, and strict use of AI to prevent serious cybersecurity problems.

Security and Transparency

One of the mounting challenges involving AI deals with client and user data when interacting with an AI-facing service. If a chatbot (or similar) collects data about a customer’s needs, where does it go? Is it also managed and stored by a providing AI vendor? Does this collected data violate cybersecurity norms and requirements?

Transparency is a big part of modern IT architecture. Cybersecurity policies involving fintech require in-depth reporting, including the use of AI and machine-learning models. It also impacts customer privacy if their data is compromised, and the security of AI models is not iron-clad.

If using services from an AI vendor or model, said model must always be updated with the latest security patches, updates, and awareness.

Ethical Concerns

While AI is a useful tool with wide applications in business, it does have serious ethical implications too. How AI will change the labor market remains to be seen. There are serious topics to consider regarding the use of AI in job markets such as:

Like any technology, it’s a double-edged sword, capable of both good and bad use cases.

In Summary

As you consider adopting an AI model for your enterprise you must think of it as a long-term solution. Is your infrastructure prepared for this adoption? Are you ready to handle any challenges and changes with the technology and its use? If so, an AI solution could be right for you

For additional IT assistance and business solutions, contact Bytagig for additional information.

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