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How, When, and Why to Prepare for Using AI in Your Business

Far Reach blog AIML

AI is the word (well, the acronym) of the year. Everyone talks about it and everyone tells you it’s high time you started using it unless you want to get left behind.

I’m going to do the opposite: I’m going to tell you that FOMO is the worst possible reason to adopt a new technology in your business. When you act out of the fear of missing out, you’re bound to jump on the wrong bandwagon and add AI where AI has no business being.

We’ll get into all that later. First, a quick primer.

What Is AI and Why Is Everyone Obsessed with It?

AI (artificial intelligence) is a technology that enables computers and other machines to emulate human intelligence and aid in problem solving.

You’ll often see it mentioned along with machine learning (ML) because AI needs to learn from existing data sets before it can solve problems. Essentially, AI is fueled by pattern recognition: after it learns enough from the data it’s been fed, it recognizes common problems and offers solutions to them.

One thing AI is not capable of is generating its own original data. Everything that AI generates is based on existing information that is spun and re-arranged by the LLM (Large Language Model) your AI application uses.

Despite a common misconception, AI is notis not a new technology a new technology: researchers started looking into it in the 1950s. The recent boom is owed to OpenAI’s ChatGPT and other similar applications that run on their API.

AI isn’t new in business or in everyday life either: you’ve surely seen it in action before 2022 on your phone’s predictive text, in automatic grammar and spell checkers, or on your GPS.

The recent uptick in adoption is fueled by the fact that it’s now easier to add it to business processes and to use it to analyze large amounts of data.

Now that it’s more popular, should you start using AI?

Yes, but not haphazardly.

Preparing for Future AI/ML Integration

Before integrating AI and ML into your custom software applications, it’s important to have a solid foundation. Here are steps to prepare your systems and data.

Data Collection and Management

AI/ML models thrive on high-quality data. Begin by:

  • Collecting Diverse Data: Gather comprehensive and representative data that reflects the various scenarios your application may encounter.
  • Ensuring Data Quality: Implement data validation techniques to maintain accuracy, consistency, and completeness.
  • Storing Data Efficiently: Use robust database systems that support scalable storage and efficient retrieval. Consider cloud-based solutions for flexibility and scalability.

Data Labeling and Annotation

For supervised learning models, labeled data is essential:

  • Automate Data Labeling: Use tools and platforms that facilitate automatic labeling and annotation.
  • Involve Domain Experts: Ensure that labels are accurate by involving experts who understand the context of the data.

Data Privacy and Security

Maintaining data privacy and security is paramount:

  • Compliance with Regulations: Ensure your data collection and storage practices comply with relevant data protection regulations (e.g., GDPR, CCPA).
  • Encryption and Access Control: Implement strong encryption methods and access controls to protect sensitive data.

Scalable Infrastructure

Your infrastructure should support the demands of AI/ML workloads:

  • Cloud Integration: Use cloud services that offer scalable computing power and specialized AI/ML tools.
  • Distributed Computing: Implement distributed computing frameworks to handle large-scale data processing.

API and Integration Readiness

Ensure your application can seamlessly integrate AI/ML features:

  • Develop Flexible APIs: Create APIs that can easily accommodate new AI/ML services.
  • Modular Architecture: Design your application architecture to be modular, enabling the easy addition of new AI/ML components.

User Experience (UX) Design

AI/ML features should enhance, not hinder, the user experience:

  • Intuitive Interfaces: Design interfaces that intuitively present AI-driven insights and functionalities.
  • User-Centric Design: Involve end-users in the design process to ensure the AI/ML features meet their needs and preferences.
  • Clear Communication: Provide clear feedback and explanations for AI-driven actions to build user trust and understanding.

Continuous Learning and Adaptation

AI/ML models need to evolve over time:

  • Model Training Pipelines: Set up automated pipelines for model training, validation, and deployment.
  • Feedback Loops: Implement feedback mechanisms to continually improve model performance based on real-world data.

Skill Development and Team Readiness

Equip your team with the necessary skills:

  • Training Programs: Invest in training programs for your team to learn AI/ML concepts and tools.
  • Hiring Experts: Consider hiring AI/ML specialists to guide the integration process.

AI Business Uses Cases and Limitations

Much like any other technology, AI comes with a lot of limitations. It’s important to look beyond the hype and the promises of AI-powered apps before you add it to your business processes. As you form a foundation for AI/ML in your organization, think about how you want to use it.

Here are some example applications.

1. AI for Faster Customer Support

You can use an AI chatbot that acts as the first contact point for when your customers reach out to your support team. You have to train it on your products using your current FAQ and Wiki sections, and then it will be able to handle basic requests from customers or at least point them in the right direction to find the answer themselves. 

With customers expecting an answer within 10 minutes, an AI chatbot can help you improve customer experience. At the same time, it can help you cut down on your support department costs.

Limitations: AI customer support is very hit-and-miss. For it to offer proper answers, the customer has to use the same language you use in your technical documentation. Moreover, it won’t be able to handle complex requests.

If the AI chatbot is not properly implemented, it can enrage customers rather than offer them a better, faster experience.

2. AI for Meeting and Other Summaries

Sometimes, wading through vast amounts of data is too time-consuming. You can use AI to generate summaries of complex documents or even your meetings.

A good example here is data from sensors (like electric utilities) that can be processed through AI so that you can easily see insights from them. 

Limitations: AI, especially if it’s not a local model, should never have access to sensitive data. So choose what data you feed it carefully as you risk exposing customer data or proprietary information.

3. AI in Marketing and Sales

Sales reps and marketers spend a lot of time qualifying leads. This is why, these days, most CRMs are enhanced with AI. After a learning period, the AI can spot patterns and pinpoint which leads are ready to convert into customers and which need more nurturing.

Limitations: Cybersecurity and data privacy are the biggest concerns here. Make sure that the CRM or the AI app you use doesn’t have access to sensitive data.

AI Is Not a Cure-all Solution–Use it Cautiously 

Large-scale use of AI is still relatively new and most experts expect to see some regulations and guardrails for it soon. While security is the biggest concern for business applications, copyright infringement (remember, AI doesn’t really generate anything brand-new, it just mixes and matches data from existing sources) is also a sensitive issue.

Are you in a highly-regulated industry like finance or healthcare? Then tread very carefully and make sure you establish sound company policies around it. 

Lastly, if you feel like AI makes sense for you and you are certain that you can protect your data, start implementing it gradually, one process at a time. Give your staff time to get used to it and analyze its ROI before you implement it company-wide.

Preparing your custom software and its underlying data for AI/ML integration involves strategic planning and investment in data management, infrastructure, security, UX design, and team capabilities. By taking these steps today, you can ensure a smoother and more effective adoption of AI/ML technologies in the future.

Do you have more concerns about the benefits and the risks of AI? Reach out; we’d love to help you figure it if it makes business sense for you to use it.