How AI-Based Enterprise Applications Can Help You Make Better Business Decisions

Artificial intelligence technologies are being extensively integrated in large retail, supply chain, legal, financial and IT companies
How AI-Based Enterprise Applications Can Help You Make Better Business Decisions
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CEO and founder, FME Extensions
4 min read
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Over the years, artificial intelligence (AI) has moved towards becoming a core component of enterprise applications (EAs) and a key determinant of successful business strategies.

With AI’s recent interventions in the corporate ecosystem, enterprises are now able to accomplish far more in less time, create compelling and personalized customer experiences but most importantly, predict business outcomes to drive greater profitability.

A recent survey shows AI technologies are being extensively integrated in large retail, supply chain, legal, financial and IT companies. According to a 2017 report from McKinsey Global Institute (MGI), tech giants such as Amazon, Apple, IBM, Google and Microsoft spent around $30 billion on AI-based technologies in 2016, with over 90 per cent of the budget allocated towards research and deployment.

The study has also revealed three central factors responsible for driving the development of AI across today’s industries.

Affordable, high-performance computing pool

The abundance and variety of commodity computing pool in the cloud allow easy access to low-cost, high-performance computing power. The time before this development, non-cloud-based and cost prohibitive systems were the only computing environments available for AI.

High volumes of data available

Training AI to make enterprise-level predictions requires a lot of data. With the availability of specialized applications for labeling data, accessibility and affordability storing procedures, organizations are now able to process both structured and unstructured data for training AI algorithms according to their specific environments.

The increasing use of AI enterprise applications worldwide

Enterprises have begun to show a keen interest in gaining a competitive advantage through AI-based enterprise applications. The most popular applications in the market include online shopping and payment processing, computerized billing systems, salesforce automation, process management, IT compliance, office productivity suites, and resource planning. Due to such progressing software, revenue from AI based enterprise applications is expected to grow up to $31.2 billion by 2025.

However, AI manifests in many forms, from machine learning and natural language processing to optimization operations and enterprise applications. Recently, many corporations are moving towards more advanced iteration of AI tools to improve their business strategies and streamline their corporate operations. This often entails deep learning enterprise software, applications that can draw conclusions entirely on their own. Such AI enterprise applications are actively being promoted to online platforms to enhance the efficiency of business operations by making live decisions that surpass the performance of human cognition.

Adaptive Intelligence in Enterprise Applications

AI can also make sense of data on a scale that no human ever could. This is where adaptive intelligence comes in. Adaptive intelligence is a subset of artificial intelligence that functions as an analytics layer for AI and machine learning and serves as an intersection of human cognition and machine automation. With the rising deployment of adaptive intelligence enterprise applications, organizations are now able to make better business decisions by combining the potential of decision science and scalable computing infrastructure, with real-time internal and external data.

So from the future perspective, human-fed databases ought to be replaced by adaptive intelligence in enterprise applications.

Adaptive intelligence enterprise applications can mimic independent thoughts through automation, cloud-data and predictive reporting, helping corporations attain a faster time to value, boost productivity, minimize costs, and improve merchant-customer relationships.

Such EAs are able to identify patterns in data, rendering predictions and automating complex or mundane tasks. Their deployment can potentially recode business ergonomics, empowering enterprises to integrate smarter corporate models and re-skill their workforce for higher performance and greater yield. Moreover, organizations that add adaptive intelligence EAs to traditional business processes can greatly improve user experience and produce compelling touch points by adding a distinct value proposition to its products and services.

 

Despite AI’s immeasurable power, many organizations, corporates and enterprises are yet to realize the full potential of machine learning and decision management. Why? Ironically, the problem’s larger part resides in the people. Lack of corporate innovation and inefficient workflows can be detrimental to any enterprise’s face value. For its lesser part, very few corporations have the necessary in-house talent and expertise to design, build and deploy AI capable solutions.

Even though, artificial intelligence is progressively evolving to become a key ingredient of contemporary applications. Despite its intermediate hurdles, large enterprises are hugely investing in AI technologies to pave the way for SMEs in leveraging from its innumerable benefits in future.

 

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