Enterprise AI

Enterprise AI in 20249 min read

December 11, 2023   |   6 min read
Shyam Ravishankar

Manager - Content and Digital Marketing

Enterprise AI

Enterprise AI in 20249 min read

Reading Time: 6 minutes

What is Enterprise AI?

When you think of Enterprise AI, what comes to mind first? Is it fear of the unknown, excitement to do something new, just another buzzword everyone uses, or even worse, something that never works?

Most companies today say they use “AI” in some form or the other. But does this work for them? However, A KPMG report shows that most business leaders across a wide range of sectors say AI is “moderately to fully functional” within their business or organization. KPMG says that “The survey findings are based on in-depth interviews with senior leaders at 30 large-cap companies that represent a significant portion of the overall AI market.”

Does this mean that AI doesn’t necessarily work? How are you expected to deploy AI successfully if 30 enterprises couldn’t agree that it worked out very well for them with all the resources and manpower at their disposal? The truth is, the answer is not as straightforward. There are several factors that determine AI success for an organization. They range from what problem AI was used to solve, what the metrics of measurement were, whether the data fed into the algorithm was clean, the number of people in the team using AI in their business processes, whether ROI was accurately measured, and a lot more.

Overwhelmed already? Let me break one such factor down for you.

The same report by KPMG also shows that AI scaled across the enterprise is not the norm with only 17 percent of the companies interviewed reporting the use of AI at scale. This means that 83% of the companies were using AI to solve one or more specific problems. One small sub-function using AI to solve a specific problem does not mean that the whole company has adopted AI. What’s even worse is that AI is used in silos by different teams without any communication between them.

Small teams using tools powered by AI to solve specific problems are a long way off from the entire organization adopting AI to transform their business by automating their workflows, enhancing their customer experience, and measuring metrics at both a granular and a holistic level.

So what does AI deployed at scale across the entire organization look like? 

Enterprise AI in a box

Enterprise AI deployed at scale not only helps grow those all-important top-line metrics for your organization, but also helps the same small sub-function achieve their weekly, monthly, quarterly, and annual goals. Sounds impossible? Well, there are many companies across the globe that have successfully done this already.

Wait, I know what you are thinking. You think I am talking about small startups based out of Silicon Valley that operate in a small niche that requires AI to be built into their DNA. Nope. I am talking about some of the biggest companies in the world that employ hundreds of thousands, if not millions of people, and operate across several industries. Still don’t believe me? Here is one such company.

The Tata group in India has successfully transformed its 150-year-old organization that employs close to a million people across 11 industries with AI. This is a great example of a traditional company that has completely revolutionized how Enterprise AI is deployed and used effectively. 

Enterprises today need to integrate AI into every aspect of their business to successfully deploy it at scale. The AI needs to work for its specific processes and peculiarities. AI needs to be baked in every step of the way across functions and teams. It needs to impact every team’s KPI, every function’s metrics, and every organization’s growth. This is certainly simpler said than done. This requires a complete shift of mindset across the organization. Every team needs to be able to measure how the AI is impacting their KPI instead of being told that it only impacts top-line metrics. 

We have a ready-made solution for you with all the right tools and the right roadmap. Here’s what you get through our Enterprise AI in a box solution.

1. Fix your ugly data problem

Bad data slows down your organization and pipelines. With businesses collecting over 75,000 data points for a single customer and up to 30% of data becoming inaccurate every year, data science teams spend hours on time-consuming legacy processes and manually executing mundane tasks such as looking, cleaning, normalizing, and organizing data – time that could be better spent on extracting clear insights that could determine critical business decisions.  

With the right data, businesses can uncover inefficiencies, optimize operations for maximum profitability and even cut down on unnecessary spending. For instance, transactional and engagement insights can help sales teams qualify seller leads more accurately, and target and personalize their sales efforts to improve conversions. Marketing teams can launch smarter campaigns and double down on the specific attributes of high-performing campaigns while growth teams can identify the scope for new markets, and offerings.

Vue.ai systems use a variety of AI-enabled APIs to detect, recognize and extract data across media sources including text, images, and audio. The raw data consisting of images, numerical, structured, and unstructured text is enriched and labeled with attributes that are relevant to your segment, business, and use case. The labeled data is then structured into formats that are compatible with various internal systems and organizable for queries. The structured data can now be used by various teams across your organization to build and maintain AI models that solve their KPIs.

We fix the ugly parts of your data and organize it into structured formats so that your teams can seamlessly integrate it into their systems, uncover data-backed insights, and build & maintain models that solve their KPIs.

2. Pick the right AI tools for your teams

There are a wide variety of AI-powered tools available today. Some of them are specific to a task while others cover processes across the organization. It can get overwhelming to pick the right tool for your specific business processes. This is where we come in. Here is an example of a problem solved by our AI-powered tool.

Data science teams spend hours on time-consuming legacy processes and manually execute mundane tasks such as analyzing, cleaning, normalizing, and organizing data. This is time that could be better spent on extracting clear insights that could determine critical business decisions.

We have data extraction tools, data organization tools, search engines, personalization engines, analytics stacks, and a lot more. The data extraction tools use AI-powered by image processing, optical character recognition, video extraction,  and natural language processing to extract data from a source. Our data organization tools can use our taxonomy or create a custom taxonomy for your business by using intelligence from your data. Our search engines can help you find your data easily. We also have a personalization engine that provides dynamic recommendations to your customers based on this data. Along with this, our analytics stack can help you measure metrics relevant to your business goals. All of this can be controlled on a single dashboard with levers to customize them to your needs. 

3. Customized by solution experts for your unique processes and problems

Our team of AI solutioning experts has helped brands like Tata, CoLearn, Hepsiburada, Mercado Libre, Zenyum, Cars24 and more solve their biggest challenges with AI. They have helped these businesses achieve 35% cost savings, 60% user retention, and more.

Our AI solutioning experts first understand your business processes in-depth to identify gaps through multiple brainstorming and workshop sessions. There are problems that can be solved by a simple process fix and others that require reengineering. Our AI experts will suggest the right solutions and find ways to help you achieve YOUR goals with AI and not try to force-te AI into your business. A phased approach is then used to solve problems while measuring the impact of the solution.

4. Choose from a vast library of APIs to integrate

The problem with having different tools for different teams is that the data is often siloed. These tools don’t talk to each other which results in teams working disjointedly. Each team works exclusively towards its own KPIs without an eye on the big picture. For them to come together, they need to use systems that talk to each other and data that flows between teams and functions. They need their newest AI tools to integrate with their existing systems in place to harness the data that is already present instead of trying to create this dataset from scratch.

This is where APIs come into the picture. Vue.ai offers a large library of APIs to integrate our AI-powered tools to your existing system. This ensures that data flows seamlessly across the entire organization and decisions are taken with an eye on the big picture. 

5. Create measurable impact that grows with time

AI is often associated with a long-term goal that can be measured only after years. This makes it hard for business leaders to approve the large cost it incurs without seeing any tangible benefit in the short term. Even if the impact is seen, it is often not measurable. The way AI impacts revenue is often a dotted line and this can further deter a serious investment. 

With Vue.ai, you can measure the impact of the AI-powered solution using our analytics stack. This gives you a clear picture of what exactly our solution is changing in your organization. With different metrics for different teams, our solution helps each person measure AI’s impact on their own KPIs instead of a big number that matters only to CXOs. Each person sees the number most relevant to them and they can make decisions backed by this data.

With time, the AI learns to use your data better and the impact grows exponentially over time. This again is a long-term goal, for which our AI experts work as a part of your team to help them make the right decisions.


AI is now a necessity and not a luxury for enterprises in 2024. Businesses need to start leveraging the power of AI just to survive. Our enterprise AI in a Box is a solution that can show measurable impact in just 60 days. It can completely transform your business, reduce costs, boost revenue and grow your business when implemented right.


Shyam Ravishankar

Manager - Content and Digital Marketing

A professional musician and a foodie, Shyam manages content and digital marketing at Vue.ai. He enjoys marketing almost as much as he enjoys cracking the worst jokes. You can find him at the best parties in town either performing at or attending them.