What’s New in AI: Trends Every Business Should Know

A McKinsey survey finds that roughly 9 out of 10 organizations are using AI in some form, and many are experimenting with new AI-driven workflows. This surge is driven largely by generative AI – large language and multimodal models that can create text, images, code, and more on demand – as well as by AI agents and “copilots” embedded in everyday software. What does this mean for businesses? In short, AI is reshaping how we work: automating routine tasks, sharpening data-driven insights, and enabling new products and services.

For example, OpenAI data shows that enterprise usage of AI tools has exploded: weekly usage of ChatGPT in the workplace grew 8× in one year, and advanced features like custom “GPTs” (workflow bots) are 19× more popular than before. Workers report concrete gains from AI. According to OpenAI’s survey of nearly 9,000 employees, 75% say AI improved the speed or quality of their work, saving around 40–60 minutes per day. Heavy users often report saving more than 10 hours a week. Crucially, 75% of users say AI enabled them to do new tasks they couldn’t do before – from generating marketing campaigns to writing reports – simply by “explaining” their needs to an AI.

These shifts are not limited to tech companies: AI investment is booming across all sectors. IDC projects that global AI spending by enterprises will reach $307 billion in 2025. Venture capital, cloud providers, and software vendors are all funneling resources into AI tools for business. However, this rapid evolution also brings challenges (data, skills, ethics, regulation). This article cuts through the hype to highlight the most impactful AI trends that business leaders should grasp, and how companies – from startups to Fortune 500s – are leveraging them across industries like finance, retail, and healthcare. We’ll emphasize practical insights and “next steps” you can apply today, not just theory.

Generative AI and Copilots

The biggest AI revolution in the past two years has been generative AI. These are powerful neural models (e.g. GPT, Llama, Stable Diffusion, etc.) that can produce original content. Unlike earlier AI, which mainly classified data or answered simple queries, generative models write text, summarize documents, design graphics, and even write code. As one industry report notes, this shift is “not merely an incremental advancement but a change in basic assumptions” about AI. In practice, business users now employ generative AI for tasks like drafting emails, creating marketing content, designing products, and analyzing large reports. Many companies have integrated these models into workflows: for example, Bank of America uses AI to recommend personalized investment strategies to customers, and many marketing teams auto-generate ad copy or social posts.

The pace of innovation is relentless. Enterprises update or improve AI features almost daily; OpenAI notes it released a new update roughly every three days in 2025. At the same time, usage has scaled up dramatically. As AI moves from curiosity to utility, firms are building AI copilots and agents tailored to business processes. (Here “agents” means AI systems that can act semi-autonomously within defined tasks – think of a virtual assistant that can plan a project or manage an inbox on your behalf.) According to McKinsey’s Tech Trends report, “agentic AI” – systems that can plan and execute multistep workflows autonomously – is among the fastest-growing trends in enterprise tech. In simpler terms, companies are now creating “virtual coworkers” powered by AI: for example, an agent that autonomously updates dashboards from raw data, or a bot that triages customer requests and pulls information from multiple systems.

These agentic systems are still emerging, but early adopters are already reaping benefits. In customer service, “copilot” chatbots can handle routine inquiries 24/7, freeing human agents for complex cases. In IT and data teams, AI scripts can spot anomalies or write code snippets, accelerating development. As one analyst puts it, agentic AI is “transforming operations by automating intricate workflows” such as claims processing or patient monitoring in healthcare. In business contexts, Gartner predicts a “virtual workforce of agents” that can reason across data and execute tasks will become the norm. The key for leaders is to think beyond basic chatbots – consider where an AI assistant could learn your business context and carry out recurring tasks end-to-end. Early examples include procurement bots that source and order supplies, AI schedulers that coordinate meetings by negotiating preferences, and budgeting agents that prepare preliminary financial plans.

Importantly, companies should not view generative AI as a plug‑and‑play magic bullet. It works best when coupled with human expertise and good data. Many organizations find the “steep” part of the curve is designing the right prompts, ensuring privacy/security, and integrating AI outputs into decisions. But across the board, the shift is clear: firms of all sizes are embeddding generative AI into core operations. In fact, one industry study found that when looking at core business functions, 71% of companies now use generative AI tools.

AI in Finance

Finance has long been a bellwether for new technologies, and AI is no exception. Banking, insurance, and investment firms are applying AI everywhere from customer service to trading. Today, roughly 85% of financial institutions report using AI for at least some purposes. Common use cases include fraud detection (using ML models to spot abnormal transactions), risk modeling (simulating market or credit risk with AI-driven analytics), algorithmic trading, and personalized financial advice. For example, JPMorgan Chase uses AI to validate payments and reported a 20% drop in account validation rejections. Banks also deploy chatbots and voice assistants for customer inquiries and routine tasks.

Generative AI is now permeating finance as well. Many banks are experimenting with AI summarizers for research (e.g. digesting quarterly reports) and even AI-assisted code generation for trading algorithms. The EY finance report notes that generative models are being trained to handle financial documentation, compliance reports, and even contract reviews. In wealth management, AI robo-advisors offer customized portfolios, and marketing teams use AI for predictive lead scoring. Overall, finance institutions are reallocating huge budgets toward these technologies to compete with fintech startups and tech giants pushing into embedded finance.

However, this rapid AI adoption in finance comes with scrutiny and risk. Regulators are watching closely. In late 2024 the U.S. Financial Stability Oversight Council (FSOC) explicitly flagged AI’s growing role as both an “extraordinary opportunity and a mounting risk” for the financial system. Specifically, regulators worry about algorithmic bias in lending, AI-driven trading shocks, and model explainability. As a result, a new “sliding-scale” oversight is emerging: high-impact use cases (like credit scoring or automated trading) face tight regulation, while back‑office automation gets a lighter touch. This means financial CIOs and risk officers must build robust AI governance from the start – embedding compliance checks, ethics reviews, and cybersecurity safeguards alongside innovation.

On the upside, when managed well AI is transforming finance. Early adopters report substantial efficiency and revenue gains: internal studies show AI can dramatically cut loan processing time, reduce fraud losses, and personalize client outreach to boost engagement. A McKinsey survey found that while only 39% of firms see direct profit improvement so far, 64% say AI is enabling innovation or differentiation – suggesting that the major payoff may come from new products and services (like better risk forecasting or embedded banking features) rather than immediate cost cuts. For financial leaders, the key takeaway is to treat AI as a strategic priority: invest in pilots that tie directly to business goals (such as market growth or customer loyalty) and build the data and talent foundations to scale those pilots into production.

AI in Retail

In retail and consumer goods, AI is quietly revolutionizing both front-end and back-end operations. Shoppers have come to expect personalization (think tailored recommendations and dynamic pricing), and AI is powering that at scale. Leading retailers no longer view personalization as purely a marketing campaign; they start with AI-driven planning. As one analysis notes, top retailers “place personalization at the end of a rigorous process” – meaning they first use AI to predict demand and optimize inventory, then customize the shopping experience on top. In practice, AI models now forecast sales at the SKU level by ingesting signals like weather, local events, and social-media trends, so that product is stocked in the right store or online just in time. These demand-sensing systems have delivered measurable results: companies report double-digit reductions in stock-outs and fulfillment costs. For example, AI-powered route and logistics planning can cut supply-chain costs by ~10%.

On the customer side, generative and conversational AI are opening new channels. AI chatbots and voice assistants can now guide customers through purchasing decisions. The Spinnaker supply-chain report highlights that AI assistants are becoming a sales channel: consumers might ask Alexa or Google for product recommendations, and behind the scenes AI systems scan retailer catalogs to respond accurately. This requires businesses to have clean, structured product data so that AI “knows” each item’s specs. Forward-looking retailers are also experimenting with augmented reality (AR) powered by AI – for instance, virtual try-on apps or in-store AR displays that let shoppers visualize products. While still maturing, these immersive experiences signal where retail is headed.

Meanwhile, operational automation is booming. In warehouses, AI-driven robots and computer vision speed up picking/packing. In stores, cashier-less checkout and smart shelves (which detect inventory in real time) are moving toward reality. AI tools are also assisting store planners: for example, “co-pilot” software can automate tasks like setting promotions or scheduling staff by analyzing sales patterns and store needs, letting human managers focus on strategy. In marketing and customer service, generative AI automates content creation (84% of small businesses are willing to use AI to auto-generate marketing content) and powers multilingual chat support, helping retailers scale global operations cheaply.

Retailers and CPG leaders should look at these trends in light of a bigger theme: agility. The industry is under constant pressure to adapt (changing consumer tastes, supply-chain shocks, labor shortages). AI can be a force multiplier: by improving forecasting and automating routine decisions, it makes supply chains more resilient and staffing leaner. The most successful firms will use AI to master the basics (the right products, delivered reliably) and then layer on innovation. As one expert notes, differentiation will come not from flashy features alone, but from excellence at scale – and AI is helping retailers get there.

AI in Healthcare

Healthcare has become an unexpected AI leader. After years of lagging behind other sectors in IT adoption, the $4.9 trillion global healthcare industry is now deploying AI tools at a staggering pace. A recent Menlo Ventures study finds that 22% of healthcare organizations have implemented domain-specific AI tools – up 7× from 2024. Hospitals and providers are leading this charge (about 27% have AI in use), while insurers and drug makers are rapidly catching up. This surge is driven by urgent needs: administrative overhead and clinician burnout are eroding margins, and rising R&D costs plague drug development. AI offers a way to reduce costs and improve outcomes.

On the clinical side, patient diagnosis and monitoring are major focus areas. Smart wearables, implantable sensors, and telehealth platforms are feeding real-time data into AI models. For example, AI algorithms can analyze continuous glucose monitors or ECG readings to predict flare-ups before patients feel symptoms. AI-driven diagnostic tools help radiologists detect anomalies (tumors, fractures) faster. BCG predicts that by 2025, doctors will routinely use wearables and genomics data to deliver personalized treatment at home. Consumers are already familiar with AI health bots that answer medical questions; now these bots are growing smarter, capable of chronic care management and emergency alerts.

Administrative workflows are also being transformed. Leading health systems like Kaiser Permanente and Advocate Health are pioneering generative AI for clinical documentation. Kaiser rolled out a real-time AI scribe (via Abridge) across 40 hospitals and 600+ clinics – its fastest tech deployment ever. Advocate evaluated hundreds of AI tools and is using them to cut paperwork (prior authorizations, referrals, insurance coding) by over 50%. These innovations free up doctors and nurses to spend more time on patient care. Additionally, AI is speeding up drug discovery and insurance underwriting; for instance, life sciences firms are training proprietary AI models on decades of lab data to shorten R&D timelines. Overall, healthcare AI spending is skyrocketing (nearly $1.4 billion in 2025, triple 2024’s level).

What makes healthcare’s AI push noteworthy is its scope and ambition. Unlike earlier tech waves that were regulation-driven, this one is driven by value. Hospitals and clinics are rapidly prototyping dozens of use cases – from virtual nursing assistants to AI triage tools – and scaling the winners. As a result, more healthcare AI startups reached unicorn status in the past year than in any other sector. For healthcare leaders, the lesson is clear: AI is no longer optional. Both providers and payers view it as essential to meet patient expectations, control costs, and improve outcomes. That said, they also face unique challenges (privacy, regulation, clinical safety), which means robust testing and safeguards are paramount when bringing AI into patient care.

Responsible AI and Governance

As AI use deepens, responsible deployment is a core trend, not an afterthought. Concerns over bias, privacy, and security have led businesses to invest in AI governance, transparency, and ethics. According to industry observers, establishing robust frameworks for oversight is now imperative. This means setting up policies around data quality, defining accountability for AI-driven decisions, and ensuring model explainability for sensitive uses. For instance, an AI lending algorithm may require human review before finalizing a loan decision to prevent unchecked bias.

Regulators worldwide are aligning with this trend. In finance, as noted, authorities are crafting tiered rules so that only high-impact AI (like credit scoring or autonomous trading) gets intense oversight. Likewise, the forthcoming EU AI Act will classify AI applications by risk level, imposing strict requirements (certification, audit) on “critical” systems. Companies in any sector should prepare for similar scrutiny. Best-practice companies now embed AI governance from Day One – they create cross-functional AI ethics boards, involve compliance teams early in development, and audit models regularly. In fact, RGP’s report on financial services recommends three imperatives: “Governance First”, building reusable compliance frameworks, and investing in model explainability. In simpler terms, businesses that innovate with AI will also need to govern it, combining rapid deployment with careful risk management.

Looking Ahead and Key Takeaways

AI’s rapid advance means the next few years will be pivotal. For business leaders, the question is no longer if to adopt AI but how. The most successful strategies we see involve:

• Strategic Alignment: Tie AI projects to clear business goals. Top adopters set growth or innovation targets (not just cost cuts). For example, a retailer might measure AI success by sales lift from personalization, not just by clicks saved. A bank might track new revenue from AI-driven products.

• Process Redesign: Don’t just insert AI into old workflows. McKinsey finds that companies that overhaul processes around AI (e.g. reengineering job roles, decision flows) capture far more value. It pays to re-think how tasks are done now that AI can assist or automate parts.

• Data and Talent: Accelerating AI requires solid data infrastructure (clean, accessible data) and people trained to use it. Invest in AI platforms (cloud services, MLOps pipelines) and in upskilling staff. Even non-technical teams should learn to work with AI tools.

• Ethics and Security: Build trust by design. Implement bias checks, limit data sharing where needed, and secure AI systems against adversarial attacks. Transparent communication about AI use (to customers and regulators) will become table stakes.

• Iterate and Scale: Start with pilot projects that can quickly show results. Many firms find success with narrow use cases (like chatbots for customer support or AI-assisted scheduling). Once proven, scale those solutions company-wide. According to surveys, while most organizations are still learning, the leaders (“frontier firms”) invest 2–7× more in AI usage per employee than average – they prioritize broad adoption once a use case is proven.

In summary, AI is reshaping business at every level. Its hype has given way to practical tools: from LLMs that write code or reports, to specialized AI in finance, retail, and healthcare. The trend is global and cross-industry, enabling smarter operations, deeper customer insights, and new product ideas. But to harness it fully, companies must pair technology with strategy and governance. As many experts emphasize, AI is most powerful when it amplifies human capabilities and innovation. Business leaders who understand these trends – and act on them – will be best positioned to turn AI’s promise into concrete performance gains.

FAQ

What are the top AI trends for businesses in 2025?

The leading trends are generative AI (large models that create text, images, code, etc.), AI agents/cobots (systems that autonomously execute tasks), and AI democratization (cloud-based AI tools and no-code platforms). Businesses are integrating these into workflows – for example, automated content generation and AI-powered analytics. Other important trends include industry-specific AI (in finance, retail, healthcare) and a focus on responsible AI (governance, fairness, security). Recent surveys show widespread AI use: ~78% of companies deploy AI and 71% use generative tools in core functions.

How is AI impacting the finance industry?

Finance is using AI extensively for fraud detection, risk analysis, customer service, and trading. In 2025, over 85% of banks and insurers apply AI to functions like credit scoring or cybersecurity. Generative AI is also used in finance (e.g. summarizing reports or automating compliance). These tools have boosted efficiency (e.g. 20% fewer false-rejections in fraud screening). However, regulators are vigilant: bodies like the U.S. FSOC warn of AI risks in lending and trading, prompting new oversight rules. Financial leaders must therefore innovate responsibly, embedding strong governance and explainability from the start.

How is AI being used in retail and supply chains?

Retailers leverage AI for hyper-personalization and operational efficiency. On the planning side, AI-powered demand forecasting and inventory optimization ensure products are stocked smartly. On the customer side, AI-driven chatbots and voice assistants serve as new sales channels (customers asking Siri/Alexa for product recommendations, for instance). Stores are experimenting with AR/VR for immersive shopping. In distribution, AI controls logistics and warehouse robots for faster fulfillment. The net effect: cost reductions and better customer experiences. For example, AI-based route planning can cut logistics costs ~10%. Retail leaders focus on “doing the basics right” at scale using AI, then layering in innovative services.

How can small and medium businesses (SMBs) start using AI?

AI tools are now accessible even to startups and SMBs. Many AI features are built into the software they already use (CRM, spreadsheets, support desks). Surveys find that 53% of SMBs already use AI, and only ~18% have no plans. Common use cases for SMBs include automating marketing (e.g. auto-generating social posts, which 84% are open to trying), handling customer inquiries with chatbots, and using AI for sales forecasting or cashflow analysis. The key is to begin with clear goals (e.g. improving customer response time) and try free or low-cost AI services (many cloud providers offer pay-as-you-go AI APIs). SMBs should also ensure they train their teams on these tools and pay attention to data quality. Over time, even small businesses can gain a competitive edge with AI, as 82% of surveyed small businesses now believe AI is essential to stay competitive.

What should businesses watch out for when adopting AI?

While AI offers big opportunities, there are risks and challenges. Businesses must guard against bias, privacy breaches, and security threats. This means vetting AI models (especially those trained on external data) and keeping sensitive data safe. It’s also important to manage change: not all jobs or processes should be automated without a plan. McKinsey notes that organizations that do not redesign workflows often see limited value. Lastly, ROI can lag if teams lack AI skills or if pilots aren’t well-managed. To succeed, companies should start small, measure results, invest in employee training, and build a culture that encourages experimentation. Ensuring ethical oversight and compliance from the beginning will help turn AI investments into sustainable business value.