Enterprise AI: Your Guide to How Artificial Intelligence is Shaping the Future of Business

What is enterprise AI?

Enterprise AI combines artificial intelligence, machine learning and natural language processing (NLP) capabilities with business intelligence. Organizations use enterprise AI to drive decisions and expand their competitive advantage. Implementing AI helps enterprises facilitate large-scale processes that generate business value, such as automated workflows and improved data management. Enterprise AI can help organizations grow revenue, streamline processes, increase customer engagement, create new business opportunities and more.

How beneficial is AI in the enterprise setting?

Enterprise AI leverages large datasets and advanced algorithms to optimize operations, streamline workflows and drive innovation at scale across an enterprise — while integrating with existing enterprise systems and tools.  Examples of enterprise AI use cases include:

Data Intelligence

A key component of enterprise AI is data intelligence. Data intelligence takes analytics to the next level by using generative AI for better insights and strategic decision-making. It democratizes data and turns it into actionable knowledge, allowing organizations to adapt more quickly to a changing business landscape and drive innovation.

Fortifying cybersecurity

AI brings multiple benefits to cybersecurity applications and enhances regulatory compliance. Thanks to AI’s ability to process huge amounts of data and see patterns humans might miss, AI-powered solutions can spot and quarantine malicious users or code quickly, preventing data breaches. If a breach or leak does happen, AI can also help identify the origin of the threat and learn what to look out for in the future.

Improving customer service

AI-powered conversational customer service tools such as chatbots can understand intent and customer sentiment, resulting in more personalized conversations while reducing the load of human agents. Integrating AI into customer relationship management (CRM) platforms can help you build more personalized customer experiences to improve loyalty and retention.

Accelerating operations

Enterprises can use AI to accelerate operations in multiple ways, including:

• Reducing operations costs
• Using predictive sales analytics to grow revenue
• Speeding up research and development
• Optimizing inventory management
• Lowering risks
• Improving staff retention and lowering hiring costs

This acceleration offers enterprises a competitive edge as the business world evolves.

Optimizing decision-making

Big data is big business—and enterprise-size companies have a lot of it. AI is made to process and analyze vast amounts of structured and unstructured data. Deep learning solutions monitor and manage it in real-time, simultaneously looking for patterns and trends to improve decision-making.

By utilizing data intelligence with generative AI, companies can catch patterns that may go unnoticed by human eyes. While data science experts are required for subtle insights, AI helps non-technical employees make better-informed decisions

The risks of adopting an enterprise AI strategy

Enterprise AI brings exciting opportunities and multiple benefits. However, AI is a highly complex and evolving field, and adopting this technology comes with challenges, such as:

• Lack of AI expertise: Implementing AI requires highly specialized engineers and data scientists.
• Interoperability issues: Existing legacy systems may present barriers to integrating AI with business intelligence.
• Regulatory compliance: Depending on the region, AI may introduce additional requirements or complexity for meeting regulations.
• Transparency: Many machine learning algorithms work as “black boxes,” meaning not even their creators know exactly how they work. This means organizations need the tools to check to see how or why an AI decision is made.
• Data quality: Most AI platforms require high-quality data for training or learning purposes. An organization may not have enough current and historical data to properly implement an enterprise artificial intelligence system.
• Hype and expectations: AI technologies like NLP carry lots of buzz. Stakeholders and team members may expect more from implementation than is currently possible.

Current enterprise AI applications

Organizations have a multitude of enterprise AI applications to choose from today. Some of the options include:

Chatbots and virtual assistants

Utilizing enterprise AI for customer support can improve efficiency, cut costs and improve the customer experience. Studies show chatbots can reduce customer service costs by as much as 30%, and 87% of customers feel OK or happy using chatbots. Voice assistants can help differently abled people access information, improving your company’s accessibility and reputation.

Predictive maintenance and risk management

Predictive maintenance helps enterprises stay ahead of problems before they happen, helping keep airline customers safe and carbon emissions lower, for example. Using predictive models to assess risk helps employees focus on issues in real-time — or even before they arise — avoiding life-threatening mistakes in some cases.

Personalization and customer experience

AI personalization is ubiquitous in the content and products users consume. For example, people watching Netflix, shopping on Amazon or listening to Spotify are guided by AI. Consumers not only welcome this type of help—they expect it.

Financial reporting and accounting

With staggering amounts of financial data to compute, companies are increasingly turning to enterprise AI for help. Large language models (LLMs) help reduce repetitive tasks for teams, freeing them up for other work and reducing errors in areas such as data entry, transaction categorization and invoice processing.

Process optimization and automation

Repetitive tasks such as data entry are time consuming and pull workers away from more important and interesting work. Not only that, but manual mistakes can cost businesses revenue. Automating these types of tasks with AI can streamline processes and redirect resources. For example, automation within data intelligence platforms can transform how companies handle their data, reducing errors and improving the overall data management experience.

The future of AI enterprise software

AI is constantly evolving — and it’s only going to grow in importance for enterprises. Here are a few of the top enterprise AI trends to keep an eye on.

Democratization

The rise of generative AI means that AI no longer needs to be for IT specialists only. Platforms like Databricks help any employee search, understand and query data in natural language and LLMs. Using generative search, developing new data and applications can be accelerated through natural language assistance to write code, fix mistakes and find answers.

Generative AI

Generative AI is any type of AI that can interpret or create new content by itself. With bigger and more powerful models continually emerging, generative AI capabilities are expanding. Not only are they encompassing design, video, audio, speech and more, but they’re trending toward multi-modal models that can simulate multiple types of expression simultaneously.

Ethics

AI tends to raise ethical concerns as humans grapple with the implications of intelligent machines and the technology rapidly evolves. Concerns range from biases and authenticity to privacy and accountability. Since AI learns from data, if the data contains biases, those biases can be perpetuated. Until there is sufficient regulation and government oversight to grapple with these issues, companies have an ethical obligation to be proactive in using AI transparently and responsibly. This provides an opportunity to exhibit leadership, help support responsible AI development, and build trust with customers.

Examples of success with enterprise AI solutions

Databricks helps organizations in a wide range of industries succeed in enterprise AI with its Data Intelligence Platform. The platform is built on a lakehouse to provide an open, unified foundation for all data, AI and governance needs. Examples include:

SEGA delivers next-level gaming experiences that keep players coming back

With 30 million customers, SEGA Europe has delighted gamers for decades. However, the Covid-19 pandemic shook SEGA’s legacy infrastructure when 25,000 events per second jumped to 50,000. The company needed a platform that could make sense of this massive increase in datasets. Having unstructured and streaming data in dispersed environments made it difficult to process the data and required inordinate amounts of time accessing and importing data from the various sources.

With the Databricks Data Intelligence Platform on AWS, SEGA was able to store all the data in one location and provide data teams with real-time access. SEGA can also now track key metrics and glean better, deeper gaming insights. It has integrated machine learning infrastructure for sentiment and behavior analysis and continues to build a loyal and engaged community.

Walgreens personalizes pharmacy care to improve patient outcomes

Walgreens manages 825 million prescriptions across 9,000 locations each year—that’s 10,000 transactions per second. However, the enterprise’s legacy solution used costly, on-premises technology that was unable to scale to support new business requirements. On top of a 48-hour turnaround, data operations didn’t have an efficient or effective methodology.

Walgreens adopted Microsoft Azure and the Databricks Data intelligence Platform as part of its digital transformation. Bringing Walgreens’ data into the Lakehouse enabled lightning-speed data intelligence insights, allowing the team to collate data in real-time in one space. With better scalability, Walgreens‘ productivity increased by 20% thanks to smarter algorithms and efficient collaboration across data teams.

Use Databricks’ capabilities for an enterprise AI platform that works for you

The Databricks Data Intelligence Platform helps you to make the most of your data and stay ahead of the curve, powered by a data intelligence engine that understands your data’s uniqueness. This unified data platform eliminates data silos, makes data searching easier, speeds up data tasks with automation and offers better accessibility into data insights for better decision-making. It provides powerful security to protect data and ensure compliance, improves ROI and more.

Your business’ unique data is invaluable—and worth optimizing. Learn how enterprise AI and data intelligence can impact your organization with The Data Intelligence Platform for Dummies.