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A Quick Heads-Up: Written for organization executives, leaders, or anyone with an interest in the application of artificial intelligence in business, our article decodes the sometimes-confusing topic of enterprise AI using concrete information and practical examples. Enough of lagging behind your friends and colleagues when discussing enterprise AI!
Remember how AI used to feel like a concept straight out of science fiction novels and blockbuster movies?
Today, the picture is different.
From chatbots that assist customer queries to predictive analytics that fine-tune business strategies, artificial intelligence services and solutions have come a long way.
In the meantime, enterprise AI is taking center stage across various industry verticals. It has become an integral tool of the modern (and future) enterprise ecosystem that empowers organizations to thrive in this data-driven world.
If you look at the recent AI statistics, the global AI market size is going to reach $1811.8 billion by 2030. At present, 56% of enterprises are utilizing AI and 92% believe that AI will have at least some impact on their industry.
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That brings a few important questions to the table – what does enterprise AI mean? What are its distinct advantages? How can you implement it? What challenges should you be ready for? And couples more.
Shall we find the answers?
In layman’s terms, enterprise AI refers to a wide category of AI-based technologies, tools, and systems that get integrated into the core operations and processes of an organization.
Considering the mounting volume of datasets in an organization, we’re seeing the emergence of a new, enterprise-level AI technology that empowers teams to evaluate mounting datasets at scale.
Enterprise-grade AI is also the bedrock of digital transformation, as acknowledged by 95% of executives.
As an omni-use technology, AI can provide data-backed insights and improve the efficiency of almost any enterprise operation, such as sales and marketing, cybersecurity, customer service, etc.
Here’s how global enterprises are expanding the use of AI-based solutions across multiple fields, as IBM reported:
Technically, enterprise AI is based on a blend of technologies and processes to deliver the desired intelligent solution.
Here are the five critical components that define the workflow of an enterprise AI software system:
It all starts with the learning phase where the system gathers both structured and unstructured data from various sources.
This part, aka reasoning, involves cleaning and modifying data. Enterprise AI platforms select the most suitable algorithm to use in that particular dataset.
ML is the cornerstone of enterprise AI applications. These models can learn from experience, improve on their own, recognize patterns, and make classifications and predictions.
NLP technologies analyze and structure human language. This allows AI systems to interpret texts, extract information, and generate human-like responses.
It's a subset of machine learning that has the capability to deal with complex data like speeches, images, and videos. In autonomous robots, this technology works as a building block.
In feature engineering, you can organize and extract raw data to create new features or improve the existing ones.
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From startups to SMEs and large-scale enterprises, every organization has come to realize that AI is likely to play a crucial role in enterprise software development and their overall business operations.
As a result, we’re seeing a growing adaptation of enterprise AI across multiple industries and departments, as shown in the image below:
Let's discuss the popular enterprise AI use cases in detail:
The IT consulting services sector was the first to embrace the power of enterprise AI programs in its day-to-day tasks.
Adoption Rate/Year | Deployment Areas | Common Challenges |
---|---|---|
75% | Automation of IT operations and software asset management, cybersecurity, downtime prevention | Data privacy and security, outdated infrastructure |
As an early adopter of any new technology and trend, the finance industry has been quick in implementing enterprise AI systems for a broad range of use cases.
Adoption Rate/Year | Deployment Areas | Common Challenges |
---|---|---|
61% | Digital security, fraud detection, risk assessment, personalization of services | Data access, biased models, regulatory compliance |
Automation capabilities of AI in healthcare transformed the way this sector works and manages its everyday operations.
Adoption Rate/Year | Deployment Areas | Common Challenges |
---|---|---|
54% | Patient data analysis, clinical workflow optimization, disease diagnosis, patient monitoring | Skill shortage, restricted budget, regulation and governance |
Cost and time efficiency are the greatest benefits of AI integration in manufacturing firms, which has resulted in a steady adoption rate of enterprise AI in this industry.
Adoption Rate/Year | Deployment Areas | Common Challenges |
---|---|---|
42% | Quality assurance, product design, prescriptive maintenance, inventory and route management | Access to clean data, lack of talent, edge deployments |
As retail stores are increasingly moving from their brick-and-mortar stores to online platforms, the need for AI solutions is at an all-time high.
Adoption Rate/Year | Deployment Areas | Common Challenges |
---|---|---|
57% | Demand/supply prediction, customer behavior analysis, inventory management | Security threats, integration with existing systems |
Here are a few important examples of real-life enterprise AI applications (you might very well be familiar with all of them):
1. Autonomous Vehicles
Automobile manufacturers are now building self-driving vehicles with the help of decision/rule engines, edge computing resources, and data-collecting sensors.
2. Chatbots
Enterprise AI chatbot solutions, based on NLP, have become an integral part of any organization providing customer support.
3. Enterprise Decision Management
Powered by the data-analyzing components of enterprise AI, EDM software analyzes huge amounts of data from multiple systems within an organization and helps make data-driven decisions.
4. Voice Assistants
By converting human languages into text, analyzing commands with NLP algorithms, and giving automated responses, voice assistants have become one of the ideal applications of AI in business.
5. Spam Filters
Big companies like Gmail actively use AI spam filters that detect and block junk emails to provide secure access to content.
6. Facial Recognition
The credit for facial recognition solely goes to AI. Companies can use this feature in mobile phones, laptops, and PCs for access control, employee tracking, and security protection.
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Interestingly, the enterprise AI architecture comes with a high satisfaction rate among enterprise leaders, as the majority of them have acknowledged the benefits of deploying AI software in different areas:
Pretty inspiring figures, right? Let's understand the reasons behind them:
As with any technology, organizations might face certain challenges while implementing an enterprise AI platform, as shown in the following image:
Apart from them, take a look at a few other hurdles you might face during the course:
It's critically important to come up with the right enterprise AI strategy for the maximum impact on your organization.
Having said that, we’ve jotted down a few time-tested steps to kickstart your enterprise AI project!
First and foremost, understand the specific operational and business outcomes you want to achieve with AI.
Data is the backbone of an enterprise AI system. You must collect and analyze all the structured and unstructured data to make it machine learning-ready and generate invaluable insights.
Successful enterprise AI initiatives need a comprehensive platform to keep everything running under a single hood.
Cloud is the ideal solution in that regard as it provides data analytics solutions, security tools, data storage, computing power, and so on.
More than the tools and systems, having the right resources and manpower to adopt and operate AI-driven solutions is essential. For that:
Since implementing enterprise AI solutions across an entire organization is a daunting task, you can leverage one or more tools depending on the project scope, skill level, or enterprise size.
Here's the list you can choose from:
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Get Enterprise AI-Ready with RadixwebPeople still freak out a lot of times when we talk about integrating AI/ML solutions into a project. However, it’s high time to understand the much less scary reality.AI is the next wave of revolutionary technologies and its impact on all-scale enterprises will be far and wide. If implemented in a practical, thoughtful way, AI can augment human intelligence and empower us to work at our full potential.Radixweb is here to make it easy for you. We can make AI reliable, affordable, and accessible for your business, even for non-technical users.As an esteemed AI development company, we have an extensive track record of harnessing AI solutions in various projects, with the ultimate goal of streamlining complex workflows, leveraging data-backed insights, and giving a boost to innovation and growth.Drop us a line (or two) to begin your enterprise AI journey!
Dhaval Dave, a Operations Spearhead, and a technology enthusiast at Radixweb. He holds 16 years of experience with proficiency in PHP & frameworks, Node.js, React.js, MongoDB, AWS services, and many other technologies. He is a dynamic leader with exceptional communication skills and has a track record of delivering on-time success for global brands.
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