How Artificial Intelligence Will Transform Businesses

What is AI?

AI is a broad term that refers to computer software that engages in human-like activities, including learning, planning and problem-solving. Calling specific applications “artificial intelligence” is like calling a car a “vehicle.” It’s technically correct, but omits important details.

AI’s most prevalent business use cases now involve generative AI, machine learning (ML) and deep learning, with generative AI experiencing explosive growth in the past few years.

Generative AI

Generative AI represents the most common AI development for businesses in recent history. Tools like ChatGPT, Claude and Google Gemini have transformed how companies create content, automate processes and interact with customers. 

Key business applications of generative AI include:

• Content creation and marketing: Automated generation of marketing copy, social media posts, product descriptions and email campaigns.
• Code generation and software development: Accelerating programming tasks and debugging with tools like GitHub Copilot.
• Customer service automation: Advanced chatbots that can handle complex queries and provide personalized support.
• Document analysis and summarization: Processing legal contracts, research papers and business reports.
• Product design and prototyping: Creating design concepts, mockups and technical specifications.

According to Gartner’s 2024 AI survey of 644 businesses in the U.S., U.K. and Germany, 29 percent of respondents have already deployed generative AI solutions. Amidst this rising adoption, the implementation of retrieval-augmented generation (RAG) has become particularly important to enhance accuracy and reduce AI hallucinations by grounding responses in company-specific data.

Machine Learning

ML is primarily used to process large amounts of data quickly. ML-based AI includes algorithms that appear to “learn” over time. In other words, if you feed an ML algorithm more data, its modeling should improve.

ML can put vast troves of data — increasingly captured by connected devices and the Internet of Things (IoT) — into a digestible context for humans.

For example, if you manage a manufacturing plant, your machinery is likely hooked up to a network. Connected devices feed a constant stream of data about functionality, production and maintenance needs to a central location. Unfortunately, it’s too much data for a human to sift through — and even if they could, they would likely miss most of the patterns.

ML algorithms can rapidly analyze the data as it comes in, identifying patterns and anomalies. If a machine in the manufacturing plant works at a reduced capacity, an ML algorithm can catch the problem and notify decision-makers that it’s time to dispatch a preventive maintenance team.

Deep learning

Deep learning is an even more specific type of ML that relies on neural networks to engage in nonlinear reasoning. It is critical to perform more advanced functions, such as fraud detection, because it can simultaneously analyze a wide range of factors.

Deep learning has excellent promise in business. While simpler ML algorithms can plateau after capturing a specific amount of data, deep-learning models continue improving performance as more data is received. They are far more scalable, detailed and independent.

For example, for self-driving cars to work, several factors must be identified, analyzed and responded to simultaneously. Deep learning algorithms help self-driving cars contextualize information picked up by their sensors, such as the distance of other objects, the speed at which they are moving and a prediction of where they will be in five to 10 seconds. All this information is calculated at once to help a self-driving car make decisions like when to change lanes.

How AI is transforming business

AI isn’t a replacement for human intelligence and ingenuity — it’s a supporting tool. While the technology may not be able to complete commonsense tasks in the real world, it is adept at processing and analyzing troves of data much faster than a human brain. AI software can take data and present synthesized courses of action to human users, helping us determine potential consequences and streamline business decision-making.

“Artificial intelligence is kind of the second coming of software,” said Amir Husain, founder of ML company SparkCognition. “It’s a form of software that makes decisions on its own, that’s able to act even in situations not foreseen by the programmers. Artificial intelligence has a wider latitude of decision-making ability [than] traditional software.”

AI’s abilities make the technology a valuable business tool, particularly in the following areas:

• Cybersecurity
• Customer relationship management (CRM)
• Internet and data research
• Digital personal assistants
• Predictive analytics and forecasting
• Supply chain optimization

Cybersecurity

AI is an indispensable ally in preventing and avoiding network security threats. AI systems can recognize cyberattacks and cybersecurity threats by monitoring data input patterns. After detecting a threat, it can backtrack through your data to find the source and help prevent future threats. According to IBM’s 2025 Cost of a Data Breach Report, organizations using AI and automation in security saved an average of $1.9 million per breach compared to those without these technologies.

“You really can’t have enough cybersecurity experts to look at these problems because of scale and increasing complexity,” Husain noted. “Artificial intelligence is playing an increasing role here as well.”

CRM

AI is also changing CRM systems. Typically, CRM software requires significant human intervention to remain current and accurate. However, today’s best CRM software uses AI to transform into self-updating, auto-correcting systems that do much of the background work of managing customer relationships. For example, modern CRM platforms now integrate generative AI capabilities to draft personalized emails, analyze customer sentiment and predict customer churn.

A great example of using AI in CRM can be found in the financial sector. Dr. Hossein Rahnama, founder and CEO of AI concierge company Flybits and visiting professor at the Massachusetts Institute of Technology, worked with TD Bank to integrate AI with regular banking operations.

“Using this technology, if you have a mortgage with the bank and it’s up for renewal in 90 days or less … if you’re walking by a branch, you get a personalized message inviting you to go to the branch and renew [your] purchase,” Rahnama explained. “If you’re looking at a property for sale and you spend more than 10 minutes there, it will send you a possible mortgage offer.

Internet and data research

AI is also significantly impacting online data research. It can sift through vast data troves to identify search behavior patterns and provide users with more relevant information. As people use their devices more and AI technology becomes even more advanced, users will have even more customizable experiences. These abilities will help small businesses reach their target customers more efficiently.

“We’re no longer expecting the user to constantly be on a search box Googling what they need,” Rahnama noted. “The paradigm is shifting as to how the right information finds the right user at the right time.”

Digital personal assistants

AI can transform internal business operations through AI chatbots that act as personal assistants, helping to manage emails, maintain calendars and provide recommendations for streamlining processes. Additionally, chatbots can help you grow your business by handling customer inquiries online.

By offloading various tasks to chatbots, you improve customer service while gaining extra time to focus on strategies to grow your business.

Predictive analytics and forecasting

AI excels at analyzing historical data to identify patterns and predict future outcomes, making it invaluable for business forecasting. AI-powered predictive analytics can forecast sales trends, anticipate customer demand, predict equipment maintenance needs and identify potential market shifts before they occur. This allows businesses to make proactive decisions rather than reactive ones.

For example, retailers use AI to predict seasonal demand fluctuations and optimize inventory levels accordingly, reducing both stockouts and excess inventory costs. Manufacturing companies leverage AI to anticipate equipment failures through predictive maintenance, scheduling repairs before breakdowns occur and avoiding costly production downtime. According to a McKinsey report, companies using AI-driven forecasting have reduced forecasting errors by 20 to 50 percent compared to traditional methods.

Supply chain optimization

AI is revolutionizing supply chain management by optimizing every stage from procurement to delivery. AI algorithms can analyze multiple variables simultaneously — including supplier reliability, transportation costs, weather patterns and demand forecasts — to determine the most efficient supply chain strategies. This level of analysis would be impossible for humans to perform at the required speed and scale.

Companies are using AI to optimize inventory levels, plan efficient delivery routes, predict supply chain disruptions, and automate warehouse operations. For instance, AI-powered route optimization can reduce fuel costs and delivery times by analyzing traffic patterns, weather conditions, and delivery schedules in real time. DHL reported that its AI-driven supply chain optimization reduced logistics costs by 15 percent while improving delivery times. These improvements translate directly to cost savings and enhanced customer satisfaction.

How to implement AI for business

Successfully implementing AI requires careful planning and strategic execution. Here’s what businesses need to consider:

Budget considerations

According to IDC’s 2024 Worldwide AI and Generative AI Spending Guide, global spending on AI will reach $632 billion by 2028, with businesses allocating budgets across several key areas: 

• Software and platforms
• Infrastructure and hardware
• Services and consulting
• Training and talent development

Before adopting any AI tools, consider the specific tasks you’d like to automate and investigate how much time and money a particular AI platform might save you. Understand the monthly cost of each AI tool and compare it to your estimated return on investment. Don’t just adopt AI tools for the sake of it; make sure they fulfill an actual function and streamline operations in a way that existing human staff cannot.

Common implementation challenges

Businesses face several obstacles when adopting AI:

• Data quality and availability: 68 percent of organizations cite poor data quality as their biggest AI challenge. If you are collecting bad data, AI isn’t going to help much.
• Skills gap: Finding and retaining AI talent remains difficult, with demand far exceeding supply.
• Integration complexity: Connecting AI systems with existing infrastructure and workflows can be time-consuming and technically difficult. 
• Change management: Overcoming employee resistance and ensuring proper adoption of AI tools can be a challenge.
• ROI measurement: Difficulty in quantifying AI’s business impact makes it difficult to know whether the investment has paid off.

Vendor selection criteria

When choosing AI vendors, consider these factors:

• Industry expertise: Look for vendors with proven experience in your sector.
• Scalability: Ensure solutions can grow with your business needs.
• Security and compliance: Verify adherence to industry regulations and data protection standards.
• Support and training: Evaluate the level of ongoing assistance provided.
• Total cost of ownership: Consider both upfront and long-term costs.

Industry-specific AI applications

Different sectors are leveraging AI in unique ways to address their specific challenges:

Healthcare

AI is revolutionizing patient care and medical research. The Mayo Clinic uses AI to analyze ECG data and support detection of heart conditions. Meanwhile, pharmaceutical companies like Moderna employed AI to accelerate vaccine development, reducing typical timelines from years to months. Administrative tasks are also being streamlined, with natural language processing automating medical coding and billing processes and helping to reduce errors.

Financial services

Banks and financial institutions use AI for fraud detection, with JPMorgan Chase’s COiN platform reviewing commercial loan agreements in seconds rather than the 360,000 hours previously required by lawyers annually. Robo-advisors manage over $1.97 trillion in assets globally as of 2024, providing automated investment advice to millions of retail investors.

Retail and e-commerce

Amazon’s recommendation engine, powered by AI, drives 35 percent of the company’s revenue. Walmart uses AI for inventory management, reducing out-of-stock incidents by 30 percent. Virtual try-on technology using augmented reality and AI helps reduce return rates by up to 64 percent for fashion retailers.

Manufacturing

Predictive maintenance powered by AI reduces equipment downtime by up to 50 percent and extends machinery life by 20 to 40 percent. BMW’s production lines use AI-powered computer vision for quality control, rapidly detecting defects. Supply chain optimization through AI has helped manufacturers reduce inventory costs.

Trust, Security and Ethical Considerations

As AI becomes more prevalent in business operations, addressing trust and security concerns is crucial for successful adoption.

Data privacy and protection

With AI systems processing vast amounts of sensitive data, businesses must implement robust data governance frameworks. The National Institute of Standards and Technology (NIST) AI Risk Management Framework provides guidelines for responsible AI deployment, emphasizing the importance of data minimization, encryption and access controls.

Bias mitigation

AI systems can perpetuate or amplify existing biases if not properly designed and monitored. Organizations should implement bias testing protocols, diverse training datasets and regular audits to ensure fair and equitable AI outcomes. The EU AI Act, which came into force in 2024, requires high-risk AI systems to undergo conformity assessments and maintain detailed documentation of bias mitigation efforts.

Regulatory compliance

Businesses must navigate an evolving regulatory landscape. Key regulations include:

• EU AI Act: Establishes risk-based requirements for AI systems in Europe.
• California AI transparency requirements: Mandate disclosure of AI use in certain contexts.
• Sector-specific regulations: Healthcare (HIPAA), finance (SOX), and other industries have additional AI compliance requirements.

Building trust with stakeholders

Transparency is essential for gaining customer and employee trust in AI systems. Best practices include:

• Clearly communicating when AI is being used
• Providing explanations for AI-driven decisions
• Establishing human oversight and appeal processes
• Publishing AI ethics policies and governance structures

The future of AI

The future of AI is potentially limitless. Consider the following paths forward for the technology:

• AI agents will handle complex workflows: By 2028, Gartner predicts that 15 percent of day-to-day work decisions will be made autonomously through AI agents. These agents will manage entire business processes, from customer onboarding to supply chain optimization.
• AI will make the impossible possible: “AI is starting to make what was once considered impossible possible, like driverless cars,” explained Russell Glenister, CEO and founder of Curation Zone. “Driverless cars are only a reality because of access to training data and fast GPUs [graphics processing units], which are both key enablers. To train driverless cars, an enormous amount of accurate data is required and speed is key to undertake the training. Five years ago, the processors were too slow, but the introduction of GPUs made it all possible.”
• AI will revolutionize familiar activities: Dr. Nathan Wilson, co-founder and chief technology officer of Nara Logics, says AI is on the cusp of revolutionizing familiar activities like dining. Wilson predicted that AI could be used by a restaurant to decide which music to play based on the interests of the guests in attendance. AI could even alter the appearance of the wallpaper based on what the technology anticipates the crowd’s aesthetic preferences might be.
• AI will pave the way for 3D experiences: Rahnama predicts that AI will transform digital technology from the familiar two-dimensional, screen-imprisoned form. “We’ve always relied on a two-dimensional display to play a game or interact with a web page or read an e-book,” Rahnama explained. “What’s going to happen now with artificial intelligence and a combination of [the Internet of Things] is that the display won’t be the main interface — the environment will be. You’ll see people designing experiences around them, whether it’s in connected buildings or connected boardrooms. These will be 3D experiences you can actually feel.”
• Multimodal AI will become standard: Future AI systems will seamlessly process and generate text, images, audio, video and code simultaneously, enabling more natural and comprehensive business applications.

What does AI mean for workers?

As AI transforms industries, many fear technology and workplace automation will force humans out of work. However, recent data suggests a more nuanced reality. According to the World Economic Forum’s Future of Jobs Report 2025, while AI may displace 92 million jobs through 2030, it’s expected to create 170 million new roles, resulting in a net positive of 78 million jobs. 

While there is still some debate on how the rise of AI will change the workforce, experts agree there are some trends we can expect to see.

AI is transforming rather than replacing most jobs

Rahnama doesn’t foresee wide-ranging lost jobs. “The structure of the workforce is changing, but I don’t think artificial intelligence is essentially replacing jobs,” Rahnama explained. “It allows us to really create a knowledge-based economy and leverage that to create better automation for a better form of life.”

However, Rahnama does see potential repercussions for analyst-related jobs. “It might be a little bit theoretical, but I think if you have to worry about artificial intelligence and robots replacing our jobs, it’s probably algorithms replacing white-collar jobs, such as business analysts, hedge fund managers and lawyers.”

New AI-specific roles are emerging rapidly

The AI revolution has created entirely new job categories that didn’t exist just a few years ago:

• Prompt engineers: Specialists who optimize interactions with large language models.
• AI trainers: Professionals who teach AI systems to perform specific tasks and improve accuracy.
• AI ethics officers: Experts ensuring responsible AI deployment and compliance with regulations.
• Machine learning operations (MLOps) engineers: Specialists managing the lifecycle of AI models in production.
• AI product managers: Leaders who bridge technical AI capabilities with business needs.

Wilson says the shift toward AI-based systems will likely cause the economy to add jobs facilitating the transition. “Artificial intelligence will create more wealth than it destroys,” Wilson predicted, “but it will not be equitably distributed, especially at first. The changes will be subliminally felt and not overt. A tax accountant won’t one day receive a pink slip and meet the robot that is now going to sit at [their] desk. Rather, the next time the tax accountant applies for a job, it will be a bit harder to find one.”

Wilson also anticipates that AI in the workplace will fragment long-standing workflows, creating many human jobs to integrate those workflows.

Reskilling becomes critical

If AI does affect employment, this transition will take years — if not decades — across different workforce sectors. The U.S. Bureau of Labor Statistics projects that occupations requiring AI skills will grow faster than the overall job market. Companies are investing heavily in reskilling programs, with Amazon committing $700 million to train 100,000 employees in AI and machine learning skills by 2025.

Husain wonders where those workers will go in the long term. “In the past, there were opportunities to move from farming to manufacturing to services. Now, that’s not the case. Why? Industry has been completely robotized and we see that automation makes more sense economically.”

Husain pointed to self-driving trucks and AI concierges like Siri and Cortana as examples. He said that as these technologies improve, widespread use could eliminate as many as 8 million jobs in the United States alone.

“When all these jobs start going away, we need to ask, ‘What is it that makes us productive? What does productivity mean?'” Husain said. “Now, we’re confronting the changing reality and questioning society’s underlying assumptions. We must really think about this and decide what makes us productive and what is the value of people in society. We need to have this debate and have it quickly because the technology won’t wait for us.”

AI may require a shift to more specialized skills

As AI becomes a more integrated part of the workforce, it’s unlikely that all human jobs will disappear. Instead, many experts have begun to predict that the workforce will become more specialized. These roles will require skills that workplace automation can’t (yet) provide, such as creativity, problem-solving and qualitative skills. LinkedIn’s 2024 Workplace Learning Report found that the most in-demand skills combine technical AI knowledge with uniquely human capabilities like creative thinking, emotional intelligence and complex problem-solving.

AI is the future

Whether rosy or rocky, the future is coming quickly and AI will undoubtedly be a part of it. As this technology develops, the world will see new startups, numerous business applications and consumer uses, displacing some jobs and creating entirely new ones. With the generative AI market alone projected to reach $1.3 trillion by 2032 according to Bloomberg Intelligence, businesses that fail to adopt AI risk being left behind by more agile competitors. The question is no longer whether to implement AI, but how to do so responsibly and effectively while maintaining the human element that drives innovation and customer connection.

Neil Cumins contributed to this article. Source interviews were conducted for a previous version of this article.