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Regardless of the field/industry people are working in, the majority are interacting with AI in some ways. A 2024 Work Trend Index by Microsoft and LinkedIn confirms this too. It highlights that three in four knowledge workers (75%) now use AI at work.
Generative AI tools such as ChatGPT, GitHub Copilot are being used to:
- Write code
- Create content
- Conduct research
- Design websites and do much more
Sure, this output requires human supervision and sometimes may not align with the objective or goal you one has in mind. But we must treat AI as a tool to assist humans. It’s not a replacement for years of technical expertise. Human emotional capabilities. And empathy. But instead, what AI does best is take over routine tasks.
For instance, imagine how much time it would have taken you to understand a complex 30-page technical report in a pre-GPT era. But now you can copy/paste the content on any Gen AI tool and get the summary in just a few seconds. This is just one of the million possibilities.
Therefore, it should be acceptable to infer that AI brings unmatched speed and precision. Most importantly, it can free up teams for higher-value tasks. And if organizations want to stay ahead of the competition and improve employee engagement, integrating AI is a necessity. It’s not optional anymore.
Secondly, the pace, demand, and intensity of work, which have accelerated post-pandemic, have not yet eased. As more people become digitally advanced, the services sector will only boom. And the speed at which consumers would demand the service would also grow. Here, adopting AI only makes sense. Not just to speed up deliverables, but also to boost ROI. So, the question is not whether to integrate AI or not, but when. How ready is your enterprise for AI? In this blog, we will explore what enterprise AI is and the five key steps to building it.
What is an Enterprise AI Solution?
An enterprise AI solution is software designed to incorporate AI-driven innovative technology into large organizations. And it’s more than just advanced machine learning algorithms. It’s a system that can understand, adapt, and integrate within the complex processes of an organization. Enterprise AI is applied to real-world business problems, such as:
- Streamlining customer support
- Detecting insurance fraud
- Automating employee onboarding
Let’s consider an example of a multinational bank. To optimize the processes, it can utilize an AI chatbot. This solution can be integrated across the website and apps to address regular high-volume customer queries, such as opening a bank account, reissuing a debit card, or reporting a lost credit card. And the ones that require urgent human attention, such as reporting of fraudulent transactions, can be routed to human assistants. It drives significant benefits such as:
- Making people feel heard and important
- Reducing overhead costs
- Increase employee productivity
- Accelerate innovation
Now that we have understood what Enterprise AI is and how it can help your business, let’s dive into how to get started.
Five Key Steps to Building an Enterprise AI Solution
Identify the Challenge
Is it human resources? Core operations? Or Customer Experience? You need to identify and consider various factors or areas in which AI applications can prove effective. This step will help you map out better strategies to design an AI-powered solution for your enterprise.
Collect and Assess Data
Data is critical. Data is key to operational continuity and success. It needs to be unbiased. So, AI tools can provide more accurate and up-to-date results. That’s why it’s important to assess the data’s quality, relevance, volume, and structure.
Choose the Right AI Technologies
There are multiple AI technologies, such as natural language processing (NLP), computer vision and image processing, predictive analytics, and robotic process automation (RPA). Each of them addresses a specific business challenge. This is a critical part of this journey. If you’re not sure about how to begin, get help from an AI development company.
Train and Deploy an AI Model
This step lays the groundwork for enabling your AI-driven app to identify and process data. Recognize the patterns so that you can make more informed decisions. The quality of training defines the capability of AI to perform accurately and reliably. Prioritize governance, ethics, and compliance. Based on these evaluations and feedback, you can integrate AI solutions into an enterprise app.
Monitor performance
This process doesn’t end with deployment. You need to assess what’s working. What’s not working. And identify areas where there is an improvement. Track key metrics such as accuracy, relevancy, speed, and reliability.
Conclusion
AI is everywhere. From Apple’s Siri to Amazon’s Alexa, Tesla’s self-driving cars, it’s powering almost everything around us today. AI is also becoming increasingly common in critical industries such as healthcare, finance, insurance, and energy. And with time, we can expect more innovative and groundbreaking AI applications.
That’s why organizations that invest in AI today will gain an edge tomorrow. With careful planning and assessments, AI integration shouldn’t be challenging. If done right, enterprises can unlock meaningful transformation with AI. The question is not whether you should adopt enterprise AI but when and how. Are you ready to lead the wave?