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Gen AI for All

AI is everywhere; on our cell phones, on our computers, and frequently in the headlines. But behind all the headlines, the use of AI in business has become irreplaceable and there's no sign of it going anywhere in the future.

So, how will the future of data intelligence affect AI for businesses? We explore the ways that AI is used today, its potential future uses in different industries, and the ins and outs of data management systems, as well as their unique challenges, to answer this question and understand how data intelligence can help you revolutionize the use of AI for your business.

Current top uses of AI in business

From streamlining workflows to analyzing data, the use of AI has become mainstream in businesses of all sizes and across all industries.

1. Smarter risk management

Having a clear risk management strategy is a must for modern companies, but there's only so much individuals can plan for. With the quantity of available data, it can be tricky to know what you're looking for - and even harder to find it! Luckily, AI can help streamline the process.

Machine learning models can be used to carry out predictive analytics and identify trends and patterns for operational risk management. For instance, if your model can spot potential market fluctuations or operational disruptions, you can start making changes in advance to minimize their impact.

Let's say you source materials from a country that's prone to natural disasters, like hurricanes. Using a machine learning (ML) module to monitor weather reports will help you identify any upcoming events that could impact your supply chain. You can also analyze past solutions to similar problems, seeing what worked best in order to have a solution that will work in the future.

Similar to risk management, security (both physical and cybersecurity) is key to avoiding threats. One common use of AI is detecting anomalies in financial transactions. By training models on data from past incidents of fraud, you can reduce the chances of financial loss.

Additionally, AI can search through data for anomalies in network traffic or system activities that might indicate a security breach. This means you can identify security threats sooner, avoiding data breaches or ransomware attacks.

2. Faster product development

With consumers constantly looking for the next big thing, developing new products quickly can be make or break for a business. Many companies are now using AI to support their design teams and speed up the development process.

One way AI can help is through predictive modeling. This lets you estimate the potential performance of a product based on data such as market trends, and historical data on similar past product launches. Rather than getting to the soft launch stage and finding out there's no interest, you can quickly spot gaps in the market and design products to fit them.

Analyzing past data can go beyond information about the product, too. It can also help you market it - for instance, is there a particular month during which you tend to get high sales? Why not release it then? Are there particular elements that work best in different countries and is localization required? These are all questions that predictive AI modeling can help answer.

As well as this, many design tools now offer AI-powered options. These can help streamline the design and prototyping phases of product development, reducing your time-to-market and increasing your profitability.

3. Increased efficiency and revenue

AI can increase efficiency across your entire business by streamlining a huge range of processes. To highlight just a few:

  • AI can help with lead generation and qualification, letting your sales team focus on making connections rather than data entry
  • Virtual assistants can provide call center agents with relevant information as needed, making it easier for them to resolve customer queries
  • The IoT allows for preventative maintenance, where AI monitors machinery and ensures they remain at optimal performance levels, reducing downtime

However, it's not just efficiency that increases. Streamlining processes leads to reduced costs and consequently increased profits. On top of this, some AI solutions actively increase income. For instance, sales and marketing departments can use data to predict customer behavior and identify market trends, leading to more effective marketing tactics, better lead generation, and increased sales.

Another example of AI-powered sales is personalized product recommendations. AI can analyze a customer's behavior and browsing history, then recommend products they might be interested in based on what they looked at before. It's an effective upselling and cross-selling technique that a lot of e-commerce businesses use.

4. Optimized customer experience

You can use AI in your customer services department to provide fast and effective support. Customers with simple questions can often find themselves waiting in a call queue for a long time, leading to frustration and bad experiences. By providing AI-powered tools, such as customer support chatbots, you can answer these basic questions immediately. Plus, if a query is more complicated, it can be escalated to a member of your team as needed.

However, it's not just speed that matters to customers now. Personalized service is also increasingly important, and AI can help you to provide it. Data on customer behavior and past interactions can be analyzed to provide insights about customer preferences, allowing you to provide relevant, personalized customer support.

Finally, you can use AI to analyze customer feedback data. Surveys, social media posts, and third-party reviews can all be quickly analyzed to spot trends and potential pain points that you can preemptively fix.

What could the benefits of AI in business be?

AI is already widely used by a variety of businesses and new applications are being discovered all the time. Here are a few of the potential applications of AI in business that are being discussed right now.

Financial services

Businesses operating in the financial services sector have already found multiple uses for AI, from using AI chatbots for banking customers to identifying potentially fraudulent activities in financial transactions.

However, many organizations are still hoping to further invest in AI. In fact, research shows that 'financial service providers display the strongest investment growth intentions'1 when it comes to AI, outpacing even retailers and manufacturers.

One of the ways that financial institutions intend to use AI is in developing more complex investment strategies. By modeling market trends and stock price shifts, investors will be able to better predict winning investment opportunities.

They'll also be able to execute transactions more quickly through the use of AI technologies like robo-advisors. These are AI-powered tools that can provide automated, personalized investment advice that can also assist in allocating assets and managing portfolios.

AI also has applications when it comes to the insurance industry. Tokio Marine, Japan's oldest insurance company, has steadily been introducing AI across their organization. Their plans for the future include using AI to reduce the risks associated with claims assessments following natural disasters.

Masashi Namatame, group chief digital officer and managing executive officer explains, "existing claims assessment procedures conducted by humans are extremely time-consuming and dangerous when it comes to typhoons, flooding, and other natural disasters. We are now looking to feed drone and satellite data into our models to assess claims from such events."2

Healthcare and life sciences

The healthcare and life sciences industry has also become increasingly focused on AI in recent years. In fact, it's one of the industries where AI leaders are most prominent, third to only retail/consumer goods and automotive/manufacturing.3

In the future, there are hopes that AI may become a standard diagnostic tool. One of the leading applications of AI in healthcare is computer vision; a field of AI that allows computers to derive meaning from digital images. This technology has a variety of applications during diagnostic operations, such as detecting potential signs of cancer in patient scans.

AI can also be used to analyze health data gathered from wearable tech and IoT devices. Patterns and trends can then be identified, which can inform healthcare planning decisions, such as where to base clinics, and how to distribute medicines to where they're most needed. Plus, on an individual level, it means patients can have an accurate, up-to-date picture of their health with appropriate alerts if something changes (for instance, if a diabetic person's blood sugar drops suddenly).

Pharmaceutical and medical technologies corporation Johnson & Johnson intends to use AI to streamline the development of new drugs, using molecular modeling in drug discovery, and accelerating clinical trials. The company also intends to utilize enhanced chatbots to interact with their customers and employees.4 A more capable version of a standard chatbot, enhanced chatbots incorporate more sophisticated Natural Language Understanding (NLU) and Natural Language Processing (NLP) to understand the context of queries better. This allows them to return more relevant responses that are more beneficial to the user.


AI can be used in lots of ways in the manufacturing industry. It can be used to optimize supply chains as well as shorten product development cycles.

AI-powered robots can perform repetitive manufacturing tasks with precision. Using them helps to streamline workflows and cut down production times. It also reduces the risk of human error.

As mentioned earlier, manufacturers can also use AI for predictive maintenance. Some quality control systems can even use AI to automatically detect defects quickly and accurately. For instance, computer vision systems can detect chips and cracks in everything from electrical components to self-assembly furniture.

The global consumer products company Proctor & Gamble (P&G) already uses AI throughout its business. They've also got plans to develop even more use cases in manufacturing.

"We need to automate the entire AI lifecycle, including data integration, model development, and model maintenance," says Vittorio Cretella, Chief Information Officer at P&G. "Automation will allow us to deliver more models with consistent quality while effectively managing bias and risk."5

P&G also has plans to use AI to reduce its environmental impact. By 2025, the business aims to be using AI to optimize its energy and water consumption in manufacturing.6

Retail and consumer packaged goods

Retail is one of the leading industries when it comes to AI utilization; it's one of the industries where AI leaders are most common.7 This is unsurprisingly when you consider how AI can be used to personalize the shopping experience for customers, leading to higher conversion rates and increased total sales.

This application of AI in business is one that retailers like Marks & Spencer plan to expand upon in the years to come. They hope to expand product personalization across their omnichannel network, but that's not all. They also have their sights set on using AI to optimize the promotions they run, and the way that products are marked down.8

Walgreens Boots Alliance also plans to use AI to secure its foothold as a leading retail pharmacy. Their goals for the future include optimizing their behind-the-scenes processes, such as using AI to better predict their inventory needs, and even operating micro-fulfillment centers that are powered by robotics and AI.9

There are plans to use AI to truly bring the shopping experience into the next century for customers. AI-powered tools like visual search use computer vision to enable shoppers to search the web for a particular product just by snapping a picture of it.

Augmented reality (AR) is also being used by retailers to allow customers to try on clothes without even leaving their homes. If a customer is shopping for clothes online, they'll be able to use their cell phone's camera to see what a product looks like on them virtually. Brands like Ikea are applying similar technology to furniture retailing. Customers can use AR to see what a virtual representation of a sofa or coffee table would look like in their own living room.

Media and entertainment

The use of AI in the media and entertainment industries is a hot topic, with much discussion around AI-generated content. There have been many discussions around the use of AI-generated content when it comes to articles, scripts, and even art, although these aren't always warmly received by their audiences.

In theory, using AI to write scripts, animate scenes, and generate special effects may speed up the production of content, and reduce the associated costs. However, many have raised concerns about how AI sources its work in these areas, as well as the ramifications to human artists. One of the key battlegrounds in the 2023 Writers Guild of America strike was over the use of generative AI, and one major outcome of the strike was tighter controls over how studios can use AI in the writing process for film and television.

Under the new agreements, studios can't simply use AI to generate scripts and then bring in writers on reduced wages to finish them off. They're also prevented from using AI to edit scripts that have already been written by a human writer.

The ethics of AI-generated content aside, it's likely that the role of AI in the business of entertainment and media will contribute to the continuing personalization of user experiences. Streaming platforms will continue to use algorithms to recommend content to their audiences, analyzing user preferences and behavior to make suggestions. As AI evolves, these algorithms are likely to become more predictive and more accurate.


For many people, a chatbot is their first experience of AI being used by a business. While they're great for simple questions, they currently struggle with more complex issues. However, as natural language processing evolves and becomes more sophisticated, they'll be able to understand and respond to more and more queries. Technologies such as sentiment analysis and speech recognition will also evolve, improving the performance of contact centers with features like real-time assist cards.

It looks likely that advances in the field will also lead to more accurate automated translations. While there will always be difficulties with parsing slang, reliable translations can help people all around the world communicate more easily.

Communication isn't just about the act of talking to each other - it's also about the systems that allow it. AI will be able to optimize network performance, and even predict potential failures or bottlenecks. It could also help ensure more reliable service by dynamically allocating resources based on predicted demand.


Similar to the communications industry, the energy industry will benefit from AI's predictive abilities. Models trained on years of data will be able to optimize energy distribution by predicting demand, managing fluctuations, and dynamically adjusting supply to match real-time needs. You might have heard of Britain's 'TV pickup' phenomenon, where a surge in electrical demand can be predicted based on TV content (for instance, half-time in a soccer match!). Imagine this, but at scale and based on multiple factors.

The security of the energy grid can also be reinforced using AI-powered cybersecurity systems. These will help to protect energy infrastructure from cyber threats; by detecting them early through the use of predictive analytics.

It's not only beneficial to energy providers, however, AI-driven systems will be able to analyze energy consumption patterns and recommend strategies to optimize usage and reduce waste. Plus, it can be used in planning the location of renewable energy sources. Historical weather data can provide useful information about where wind turbines or solar farms should be built to maximize their output.

It can even go into more depth than that - for instance, we may be able to work out the ideal tilt angles of solar panels depending on the time of year, or the blade angles of wind turbines for maximum output.

What are the current challenges to using data management systems for business AI?

Many organizations use data management systems (DMS) to manage their data. This is a must for any company that manages large volumes of data, otherwise, it's easy to lose track. This is both a security risk (especially if the data is personal and/or sensitive) and a waste of potentially valuable information.

Generally speaking, AI DMS is made up of various technologies and processes. It focuses on storing, retrieving, securing, and manipulating data. When it comes to business AI, they allow for the data collection, integration, cleaning, and storage processes that make it possible.

Using data management systems for business AI isn't without its challenges, however.

1. Technical skill barrier

There can be a steep learning curve when using data management systems for business AI. Specialized language knowledge, such as SQL, is often required. Due to the increase in AI and data management, data scientists, engineers, and analysts who have the right expertise are in high demand.

You may be able to provide training internally, but this can incur extra costs and require a large time commitment, which can cause delays.

2. Data accuracy and curation

Any data you keep and use must be of good quality. Issues like mixed formats, incomplete datasets, or missing metadata can hinder the accuracy and effectiveness of AI algorithms. If you're integrating data from legacy systems and disparate databases, it can be a difficult and time-consuming task.

Making sure you have complete, high-quality, and well-organized data is vital to implementing AI and should always be done in advance. While this can slow you down, it will prevent more problems in the long run.

3. Management complexity

Data management systems are often pretty complex. Even with high-quality data and the right staff, it's easy to get overwhelmed. Most businesses will gather huge volumes of data, at great speeds, and without the right solutions in place, it's easy to end up with a disorganized mess. Failure to manage your DMS properly can quickly lead to increased costs and poor performance.

4. Governance and privacy

Governance requirements across the world can change quickly. Different countries will have different regulations, meaning if you operate in multiple markets you need to make sure you're meeting many different sets of rules. Establishing data governance policies, setting access controls, and ensuring compliance with regulations are complex processes.

Things get even more complicated when it comes to data in healthcare and other sensitive industries. Protecting sensitive data from breaches or unauthorized access requires robust security measures. Unfortunately, these can be costly and time-consuming to implement.

The advent of AI has only amplified concerns around data lineage, security, and privacy. Data governance must now also focus on ensuring that AI systems are developed and used ethically.

For example, robust data lineage must be established in order to track data sources and transformations. Data governance must ensure that AI processes comply with privacy regulations and that sensitive data is handled securely.

5. Emergence of AI applications

Traditional data management systems often struggle to support the processing, storage, and analysis requirements of AI algorithms.

For example, in order to enable generative AI applications that answer domain-specific requests, organizations have to develop and tune large language modules (LLMs). This must be done in platforms that are separate from their data, which must then be connected through manual engineering.

You'll need a high-performance, scalable data management system if you're using it for AI applications. Developing complex algorithms and handling large datasets in real-time requires a lot of computing power. A good data management system needs to feature an adaptive infrastructure that can be changed to accommodate the changing needs of AI applications.

How to improve the application of AI in business with data intelligence

Many of the issues impeding the use of data management systems for AI arise because the data platforms do not fundamentally understand an organization's data and how it is used.

Fortunately, while AI is partly the cause of the problem, it can also provide the solution. Generative AI presents a powerful new tool that can help to address these challenges.

Data Intelligence Platforms, like the one offered by Databricks, employ AI models in order to deeply understand the semantics of enterprise data. This is known as data intelligence.

Data intelligence can help to improve the application of AI in business in several ways.


Data intelligence platforms combine the power of generative AI with the storage and unification benefits of a lakehouse. This combination enables the performance of a Data Intelligence Engine; a powerful tool that understands the unique semantics of your data.

By utilizing the Data Intelligence Engine, performance can be automatically optimized to best suit the unique needs of your organization. Not only that, but your unique infrastructure can also be managed in the most efficient way possible.

This helps to maintain a high standard of data and ensures that metadata is managed effectively.


As we mentioned before, management complexity is a big challenge when using a DMS for business AI. You can simplify things with the help of a data intelligence platform.

A Data Intelligence Engine understands your organization's language, making data much more searchable and discoverable.

Natural language can also help when writing code or resolving errors. Streamlining these processes speeds up the development of new data and applications.

Because the entire user experience is simplified with a data intelligence platform, the barrier to entry is much lower. This makes it possible for a wider range of team members to effectively use the platform, boosting data democratization efforts.


Generative AI has complicated the way we interact with data. These days stronger governance and tighter security measures often need to be implemented in order to compensate.

Databricks' Data Intelligence Platform provides an AI development solution that's built around a strong, unified approach to governance and security.

This means that a wide variety of AI initiatives can be pursued, without compromising data privacy and IP control.

Revolutionize the use of AI in business with the Databricks Data Intelligence Platform

Using AI in business isn't always straightforward. While many use cases have been spearheaded, there are often many challenges to be overcome, especially where traditional data management systems are involved.

Data intelligence platforms use AI to tackle many of these challenges, providing solutions that are intelligent, simple to use, private, and secure.

The Databricks Data Intelligence Platform is one such platform. It allows your entire organization to use data and AI efficiently and effectively.

An open, unified foundation for all your data and its governance is made possible thanks to the lakehouse architecture it's built on.

Meanwhile, the Data Intelligence Engine it's powered by works to understand your unique data, enabling you to more easily analyze, discover, and build applications.

With Databricks' Data Intelligence Platform you're able to better share, engineer, store, and secure your data, allowing you to unlock its full potential.

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