A recent survey by Gartner predicts, “By 2021, 40% of new enterprise applications implemented by service providers will include AI technologies.”
The world of business is undergoing a massive change owing to the rapid emergence of artificial intelligence (AI) for enterprise applications. Indeed, artificial intelligence has the power to solve several organizational problems as it offers functionalities that humans cannot practically perform at the same rate and accuracy.
AI has quickly changed status from a “technology to experiment” to a “technology to deploy.” By 2025, most enterprises will be using AI-enabled apps to gain a competitive edge from streamline operations, more incredible product innovation, and improved customer satisfaction.
Drivers of this shift include an unprecedented growth of enterprise data, advances in machine learning (ML), natural language processing (NLP) capabilities and the need to accelerate digital transformation journey.
Below, we present you with the latest insights into how AI is transforming enterprise software apps.
1. Embracing conversational AI to simplify data analytics consumption
While data analysis is critical, it is extremely time-consuming to sift through multiple business dashboards and reports and find relevant data. To overcome such limitations, AI-enabled virtual assistants are integrated with business intelligence apps.
AI-enabled virtual assistants, leverage the NLP technology to converse with users in natural language. By merely initiating a chat on the enterprise messaging app, and sending simple messages like “What is the sales of product A for 2017?”, employees and business leaders can procure in-depth insights in the most granular form of data, without switching between multiple tools and dashboards. Users need not manually filter data to analyze information and arrive at crucial decisions.
This, AI is transforming the consumption of business intelligence and analytics, especially for on-field employees (for example, sales agents) or CxOs, who need quick access to information without having to dig through heaps of data.
2. Securing Every Aspect of Enterprise IT through AI
With the rise of remote working across the globe and as IT decision-making becomes more democratic, enterprises cannot ignore the increased threat of cyber attacks.
To combat the threat and secure every aspect of the IT infrastructure, organizations are scrambling to deploy applications that integrate machine learning to detect possible threats and vulnerabilities in real-time.
These tools use ML techniques to spot anomalies in network traffic, emails and user activities. Hence, they can quickly identify a potential attack and take steps to mitigate it, even if the threat is unlike anything the organization has witnessed before.
3. Transforming IT through AIOps
AIOps, an emerging variation of DevOps, uses machine learning (ML) algorithms on IT operative data to derive insights that optimize and improve operations.
While DevOps automates and simplifies IT operations, AIOps goes a step further by extracting information that is useful in overseeing IT activities.
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Sometimes, it can automatically take requisite action based on such insights, thus enabling IT personnel to supervise larger IT environments than otherwise possible.
4. Making Intranet Smarter with AI
AI can make your digital workplace more intelligent — right from content management and collaboration to information accessibility, employee communication, and social networking.
Modern intranets, equipped with AI technologies, serve the following purposes –
- Cognitive enterprise search: Cognitive enterprise search is an AI-enabled smart search tool that connects all the internal and external enterprise systems, thus acting as a one-stop-search engine for enterprise-wide knowledge and information It understands natural language phrases and enables shows personalized search results based on the users’ roles, locations, interests and past search activities
- Automated metadata management: Enterprises can use AI bots to automate metadata generation, data-tagging, classification, and organization and generate good-quality taxonomy recommendations. As a result, organizations can illuminate and maximize the value of unstructured data
- Personalized employee experiences: AI empowers the intranet to break the clutter and deliver personalized content recommendations to the users, based on their interests, locations, and job profiles
- Improved collaboration: AI analyzes the user’s persona to suggest the right subject-matter experts to connect with within the organization; thus fostering a collaborative workplace culture
- AI-powered analytics: AI-powered analytics help in harnessing and analyzing intranet users’ data. This provides insights into how employees across departments are engaging with the intranet.
5. Combining AI with CRM
The benefits of integrating AI into your customer relationship management are manifold. Here are a few reasons to start contemplating an AI-driven CRM software –
(i) Automating data entry
(ii) Simplifying the data updating process
(iii) Allowing users to access information faster
(iv) Sending personalized updates automatically.
- Prediction of future customer behavior: Artificial intelligence can draw learnings from the customers’ past decisions and historical engagement to generate valuable sales. Moreover, AI can analyze customers’ sentiments, to predict their future behavior
- Automated segmentation: AI segments customers automatically into groups with similar characteristics and ensures your messages reach the right audience at the right time
- Price optimization: AI can analyze past client data to predict the ideal discount rate and pricing that is most likely to lock the sales deal
6. Optimizing Supply Chain Management Through AI
Several enterprises are investing in AI-powered supply chain management apps. Such applications help improve just-in-time deliveries, anticipate potential issues, reduce costs, and recover from supply disruptions.
By leveraging AI-powered analytics, businesses can generate valuable recommendations and forecasts to build a resilient supply chain. Some of these scenarios include — demand forecasting, stock visibility, detecting out-of-stock situations, and supplier risk analysis.
Speaking volumes about the utility of these apps, a study by Mckinsey has found, “among organizations that were using AI for supply chain management, 61% experienced cost decreases, and 63% saw revenue increases.”
7. Simplifying Vendor Billing through AI
AI is capable of simplifying financial operations within the enterprise.
Moving a step ahead from traditional Optical Character Recognition (OCR) systems that extract data from templated documents, an AI-embedded invoice management software can look at any document and extract all the critical information.
For example, by just feeding the software with invoices from different vendors, AI can figure out who the invoice is from, the due date, the amount to be paid, etc., without any human intervention.
AI is and will continue playing a significant role in transforming enterprise software applications. As seen above, one cannot deny the essential role of AI-enabled apps in driving improvements in quality, speed, and efficiency within organizations. While it can be overwhelming for organizations to transform their operations and systems overnight, beginning the AI journey with low-hanging fruits can be a good start. Via Botcore.ai