What is artificial intelligence (AI) in business?

AI in business helps improve productivity and streamline operations to increase business value.

Artificial intelligence technologies like machine learning, deep learning, and natural language processing (NLP) harness the power of data to enhance problem-solving and decision-making at a scale that exceeds human capabilities. Capabilities such as predictive analysis — which can use data to predict future outcomes and model possibilities based on trends — action the benefits of AI in material ways. From day-to-day productivity to furthering innovation, AI has also revolutionized business. And, for savvy organizations, AI can continue to take business operations to new heights.

OpenAI popularized generative AI — which relies on deep learning, neural networks, natural language processing, and large language models to generate new content — for general business (and individual) use. This democratized AI access instigated a wave of AI adoption in business.

Early adopters are already seeing the material benefits of generative AI. According to McKinsey¹, the average organization uses generative AI in marketing, sales, product development, and service development. Organizations believe the technology will lead to significant or disruptive change in industries worldwide.

AI's versatility allows it to be applied across diverse business functions. From IT to strategy there's a wide breadth of AI use cases.

AI in IT operations

AI in IT operations (AIOps) uses machine learning and big data for predictive analytics and anomaly detection. This enhances IT efficiency and minimizes downtime. It typically uses a scalable data platform to bring together a variety of IT data such as logs, metrics, traces, performance and event data, and infrastructure and network data. AI improves observability practices and overall IT efficiency, quickly analyzing datasets for troubleshooting and general infrastructure management.

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AI in cybersecurity

As digital landscapes expand, so do threat surfaces. AI in cybersecurity relies on machine learning algorithms to analyze vast amounts of security and operations data. By triaging out false positives, detecting true anomalies, and automating incident response, AI helps organizations spend less time sifting through alerts and more time investigating and addressing threats.

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For example, content delivery networks (CDNs) analyze incoming traffic for anomalies. If an IP address is requesting a large amount of data in a short amount of time, it will determine that it is likely a bot or scraper traffic, and then block it for a set period of time. This is how sites like Ticketmaster (attempt to) prevent bots from buying all the Taylor Swift or Oasis tickets.

AI in business analytics

AI transforms business analytics by processing and analyzing large datasets in real time. Like in IT operations, AI uses business data to uncover hidden patterns, forecast trends, and provide actionable insights that inform strategic decision-making.

During the holiday season, for example, AI can alert factories and grocery stores when stock should be ordered. According to past data, how many turkeys will a store need to order to satisfy holiday demand?

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AI in business strategy

AI supports business strategy by facilitating risk management, monitoring competitors, and analyzing operations. It simulates scenarios, evaluates risks, and identifies growth opportunities, enabling leaders to make data-driven decisions with confidence.

For instance, a company might use generative AI models to rapidly generate and analyze new product ideas based on customer preferences, external market trends like social sentiment and buying behavior, and competitor information. This would allow a business to quickly decide which product to build, where to allocate budget, and how many employees should be devoted to the new endeavor. Manual product ideation would be a thing of the past as organizations can use AI to quickly analyze all the factors that influence what products are made.

AI in marketing and sales

AI tools help marketing and sales teams get actionable insights from customer and competitor data. AI monitoring and analytics tools also provide insights into customer behavior, enabling more effective campaigns. By delivering personalized experiences, optimizing ad targeting, and automating lead scoring, AI is changing the marketing and sales landscape — as well as customer expectations.

AI in customer service

Chatbots powered by AI, machine learning, and natural language processing enhance customer service by providing instant, accurate responses around the clock. Grounded in proprietary data using RAG, these chatbots can answer questions for anything from order status on an ecommerce site to assisting with the installation of a video doorbell. These tools reduce wait times, improve satisfaction, and allow support teams to focus on complex issues.

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AI in content generation

Generative AI can create content for blogs, social media, and marketing campaigns. From writing to producing images or videos, generative AI is a useful brainstorming and time-saving tool for creative teams.

Ultimately, AI can help brands develop new and creative ways to engage with their audiences faster, and more consistently. However, regulations are emerging to prevent misleading or false content published by organizations without signifying it's AI-created content. Organizations should have to label what content was made by AI to prevent misinformation.

AI in search

Search applications can be augmented with AI using machine learning and natural language processing to deliver semantic search or conversational search experiences. As a result, AI enables more intuitive search experiences that help users quickly access the information they need. From a customer-facing perspective, AI improves search functionality by delivering more relevant and accurate results based on geo-location, past searches, and more.

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AI is reshaping how businesses operate, offering organizations a wealth of efficiency, decision-making, and growth-related benefits.

• Faster access to insights: AI improves knowledge sharing within organizations. AI’s data processing capabilities also mean enhanced analytics, which result in faster access to information — and faster access to actionable insights.
• Improved productivity: By automating workflows, AI can streamline operations and take over repetitive, time-consuming tasks. Employees can focus on more value-adding activities that further innovation and growth. In fact, 83% of IT leaders believe using AI for data-driven insights will improve productivity.
• Enhanced customer satisfaction: By enabling personalization and relevance tuning, AI can help improve customer satisfaction by providing customers with what they need when they need it. If a brand can solve problems efficiently and create a personalized experience where the customer feels valued, this will naturally lead to improved brand loyalty.
• Competitive advantage: By increasing productivity, reducing human error, enabling faster access to insights, and improving customer satisfaction, AI, when integrated early and efficiently in a tech stack, has the potential to drive competitive advantage.
  

The promises of using AI in business also come with challenges and risks — especially in regards to AI integration.

Technology barrier: Not all organizations are equipped to incorporate AI technologies into their stack. In other words, organizations that have not reached data maturity and/or do not have access to the necessary data architecture and infrastructure may struggle to implement AI technologies.

Skill gap: While AI is supposed to bridge the gap in technological skills facing organizations — often due to increasingly complex digital environments — AI skills themselves are in high demand. Because the technology itself is rapidly evolving, AI experts are scarce. Therefore, proper implementation and execution can be challenging. Then, to get the most out of the technology, organizations have to retrain teams on the new tech and new processes. As updates emerge, the cycle continues.

AI sprawl and technical debt: When organizations implement multiple AI solutions with multiple vendors, the long-term cost will far exceed what was originally budgeted for — resulting in technical debt — and they won't be able to scale with the tool sprawl. As the organization — and its data — grows, the point solutions won't be able to meet new demands. Employees who use the AI solutions will be bogged down with system maintenance requirements, data validation and reconciliation, and data silos.

Job displacement: Though AI is considered a tool to supplement human capabilities, it can represent the automation of many workplace functions across industries — from analytics to creative to manufacturing. As a result, AI has the potential to displace jobs and negatively impact workers in many fields.

Data security: Many organizations are reluctant to adopt AI, or generative AI in particular, due to a lack of trust in data security. AI models can operate like black boxes, so ensuring regulatory compliance and securing proprietary data are top concerns. Despite the emergence of new technologies like retrieval augmented generation (RAG), a technique made for private or proprietary data sources, organizations are mindful of their data's exposure to security threats.

Lack of governance: Rapid AI adoption has led to a lack of proper governance. Navigating international or regional regulations is more complex than ever thanks to AI. Large organizations may be reluctant to implement sweeping changes to their processes if potential AI legislative measures might impact them.

We are already witnessing the transformative impact of AI across industries. In our own homes, we've seen streaming services offer tailored recommendations and smart-home devices help us regulate temperature and lighting with voice commands. AI in business has been more behind-the-scenes; transforming how work happens.

Financial services

In financial services, AI's enhanced analytics capabilities improve fraud detection, security risk management, and customer experiences. AI can streamline loan approvals, personalize financial advice, and improve compliance processes. Robo-advisors that run on AI algorithms provide investors with a low barrier to entry with their personalized and automated investment portfolios.

Technology

In the technology sector, AI powers product innovation, optimizes operations, mitigates security risks, and drives advancements in areas like natural language processing, computer vision, and autonomous systems. It allows organizations to build solutions that offer top-notch personalization, as well as holistic visibility across verticals.

Retail

In retail, AI helps companies personalize shopping experiences, manage inventory, and forecast demand. Retailers can use AI-driven recommendations and dynamic pricing to maximize revenue and customer satisfaction.

Telecommunications

AI enhances network optimization, predicts service outages, and improves customer support. Telecom providers use AI to analyze usage patterns and deliver personalized experiences to customers via relevance engines.

Public sector

Governments and public sector organizations can leverage AI analytics for urban planning, public safety, and citizen engagement. AI-driven tools can help streamline operations, support government workers, improve resource allocation, and streamline public services.

Though there is no one-size-fits-all solution to implementing AI in business, it overall requires a strategic approach that follows one key rule: start small.

Step 1: Identify the problem

AI has many benefits, but you may not need AI in every aspect of your operations. Zero in on the problem you want to solve with AI by running an audit of your operations. This will ensure that you maximize your resources and get the most value.

Step 2: Identify what success looks like

To succeed in implementing AI in business, you'll need to establish a set of KPIs that help you measure what "good" means to you. Understanding how AI is moving the productivity needle in your organization is only one of many performance indicators. Others might include an increase in customer satisfaction, measured by reviews in a customer support context, a decrease in support tickets, or faster resolution times.

Step 3: Choose a model

Many things will influence the AI model you choose. Cost, language, your IT ecosystem, your deployment capabilities and timeline, data privacy regulations, and governance will all come into play. You'll need to decide if you want to pre-train an LLM, fine-tune a model, or use RAG. This will serve as the foundation of your AI architecture.

Step 4: Try fast, fail fast

Once you've tuned your AI model to the right specifications, it's time to deploy. Active monitoring is key at this stage — you'll want to make sure the AI is behaving as trained in a live environment, checking for accuracy, speed, and relevance (depending on the use case). At this stage, you'll want to build a feedback loop, enrich your LLM, fine-tune the user experience, and establish a reference architecture that can scale.

Step 5: Set guardrails

AI initiatives bring their own set of challenges — from data privacy and compliance to ethical considerations, quality control, and risk management. You need to anticipate potential obstacles and ensure that your project aligns with your business objectives. You'll need to take global regulations into account while monitoring things like response sentiment and prevalence of hallucinations.

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Step 6: Set a timeline

Outline a time frame — try one quarter. Within that time frame, set goalposts at the 30-day and 90-day marks. Use the quarter to prove the value of your AI-boosted use case. Your company's specific needs, the makeup of your team, and the tech they're working on or adding to your stack will affect the speed at which you can deploy your first use case and gather insights. This will give you a solid idea of when you can expect to see results based on those KPIs you set up in step 2.

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Stack Overflow uses the Elastic Search Platform to bridge the AI trust gap and empower developers. With Elastic's semantic search and generative AI, the OverflowAI platform combines the strength of Stack Overflow's public content with private enterprise instances to supply relevant, contextual information to developers.

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