10 Ways AI Helps You Build What Your Business Needs

Do more. Work smarter. Focus on what matters.

TL;DR

AI is more than a tool. It’s a strategic partner that can amplify human potential across an organization. This guide explores ten real ways AI helps businesses overcome challenges, boost productivity, enhance customer experiences, and drive innovation by elevating human capabilities, not replacing them. Learn how AI is reshaping roles in software development, marketing, IT, sales, legal, HR, and more—and discover the essential human-AI skills needed to thrive in this new era of work.

AI: The intelligent assistant transforming how we work

AI is reshaping how businesses operate, and organizations are investing accordingly. The numbers tell a striking story. According to McKinsey’s State of AI 2025 report, drawing on responses from nearly 2,000 organizations across 105 countries, 88% of organizations now use AI in at least one business function. But only 7% have fully scaled it across their organization. Most are still experimenting or piloting—and those that are scaling tend to be doing so in just one or two functions.

That gap matters. Because the organizations getting the most value from AI aren't the ones with the best tools or the biggest budgets. They're the ones deploying it broadly across sales, marketing, IT, HR, legal, finance, product, customer service, and operations, rather than concentrating it in one corner of the business.

AI isn't a replacement for human intelligence or talent. It's an amplifier, empowering individuals and teams to accomplish more and make better decisions. Think of it as having an incredibly fast, well-informed assistant available to every team—not just the ones that asked for it first.

This guide explores ten real ways AI can help you close that gap—moving from isolated experimentation to meaningful impact across your organization.

10 ways AI empowers your business to work smarter

AI isn’t about eliminating jobs. It’s about eliminating tedious, time-consuming tasks and amplifying human capabilities. By strategically integrating AI across key business functions, organizations can unlock new levels of efficiency, innovation, and strategic advantage. Here are ten real-world applications where AI helps businesses not just work, but truly excel.

1. Drive revenue: Intelligent sales and growth strategies

Sales teams constantly strive to personalize engagement, shorten sales cycles, and identify the most promising leads in a competitive market. The reality for many teams is that repetitive questions eat up valuable rep time, pipeline reviews run on stale data, and customer emails need multiple rewrites before they’re clear. AI provides the intelligence to solve this at scale, turning raw data into actionable insights and giving reps their day back.

Cut the time to a data-backed decision in half. Pipeline calls used to wait on a data pull, the rep refreshing a report mid-meeting while the room waited. AI-powered analytics agents update views in real time, so the answer is in the room when the question is asked. Filters, cuts, and what-ifs are adjustable in natural language, mid-conversation, without a BI ticket.

Get the L1 questions off the rep’s plate. A front-door agent trained on your sales playbook fields the common asks—like product questions, pricing edge cases, where-to-find-the-deck—escalating only the truly complex inquiries. It tracks what’s being asked, so enablement knows what to fix. Top reps stop losing hours to the same five questions every morning.

Hyper-personalize outreach for stronger connections. AI analyzes customer needs, past interactions, and preferences to help reps craft highly tailored emails, presentations, and product recommendations. An email clarifier can turn confusing threads into one clean ask, reducing the back-and-forth that slows deals down.

Build the pipeline tools that actually fit. Paste a mock of the dashboard, tracker, or commission view your team needs. AI returns a working version with your data wired in. No ticket, no quarter-long roadmap, no fighting BI for a refresh. Custom CRM views purpose-built for exactly how your team works, on your own data, at a fraction of off-the-shelf software costs.

2. Build faster: Accelerating software development and innovation

Software development teams constantly battle deadlines, technical debt, and the relentless demand for rapid innovation. Pull requests sit in queues, senior engineers lose time to boilerplate reviews, and spec documents ping-pong through endless revisions. AI transforms this challenge, shifting the developer’s focus from repetitive tasks to higher-level architecture and creative problem-solving.

Conduct a first-pass PR review in under two minutes. AI review agents handle the boilerplate findings—security, reliability, performance, bug detection—scored against your own quality rubric before a human opens it. Senior engineers spend less time on routine review and more time on the complex calls that actually need judgment. As AppDirect's SVP of Engineering Mathew Spolin noted in How to Adopt AI Development at Scale, AppDirect's own engineering team went from 0% to over 90% AI-assisted code in one year, with accepted lines of code increasing 44x while quality held or improved. The hard part wasn't the technology. It was the organizational change, the metrics, and the process evolution that made it stick.

Use AI as a thinking partner, not just a code generator. PMs and engineers can stress-test spec documents before they circulate, surfacing counter-arguments and edge cases so the first draft is sharper and the review loop is shorter. Specs stop ping-ponging through five rounds of revision.

Vibe-code from a prompt bar or drop into a full development environment (IDE). The rise of "vibe coding" allows both technical and non-technical users to build applications quickly by describing desired outcomes in natural language. Whether your team writes every line or none at all, a real integrated development environment keeps up.

Different apps need different models: fast and lightweight for ops dashboards, deep reasoning for analyst tools. Go with a platform, like Devs.ai, that lets you choose the appropriate LLM for each app or feature, and swap any time to optimize cost and performance, without having to rewrite prompts or lose memory.

Onboard faster, ship more. For new engineers, AI acts as a 24/7 senior mentor, helping them navigate unfamiliar codebases and understand context quickly. Some teams report a 2x increase in epics shipped per quarter, with no new headcount.

Agentic AI is moving fast here. Gartner predicts that 40% of enterprise applications will be integrated with task-specific AI agents by the end of this year, up from less than 5% in 2025 — marking one of the fastest technology transitions in enterprise software history.

3. Maximize impact: Smarter marketing and content velocity

Marketers are under constant pressure to generate engaging content, understand complex market dynamics, and deliver measurable results across multiple channels. The reality for many teams is that they manage a sprawling martech stack with multiple single-purpose tools, most of which they barely use. And new capabilities only arrive when vendors get around to shipping them. AI changes that equation entirely, giving marketing teams the ability to build the tools, content agents, and dashboards they actually need, without waiting on a vendor roadmap or filing a single ticket to engineering.

Deploy brand-safe content agents, on every channel. Stand up agents trained on your brand voice, your style guide, and what you can and can't say. They draft email, social, blog, and ad copy that sounds like you—not like every other AI tool. Approval workflows bake in legal and brand review by default, so compliance happens in the flow, not as a last-minute scramble.

Generate campaign briefs from a single line. Type a one-line prompt and get a full brief: audience, channel mix, timeline, budget split, asset variants, and a draft of the first three deliverables, grounded in past campaign performance. Edit the parts you’d edit anyway. Then ship.

Build dashboards that finally tie back to revenue. Stop renting a "unified marketing platform" that isn't. Build your own cross-channel dashboard wired to the systems you already use—ad platforms, email tools, GA, CRM—and give every stakeholder the view that's relevant to them, without waiting on your data team to build it. The pattern is consistent across the research: marketing and product teams that deploy AI purposefully, not just experimentally, are among the first to see measurable revenue impact.

4. Give IT a seat at the AI table—and the tools to lead it

AI adoption isn't waiting for IT approval. It's already happening—on personal accounts, free tiers, and unsanctioned tools that nobody in IT knows about. This is what's known as shadow AI: employees using AI tools outside of any organizational oversight, often with company data. Every team wants an agent yesterday, and when IT can't move fast enough, people find their own way. The challenge isn't whether to embrace AI. It's how to govern it without becoming the bottleneck.

Make it safe to build. The fastest way to eliminate shadow AI is to replace it with something better—a governed environment where every team can build and use AI with confidence. That means proper access controls, data residency guardrails, and full audit visibility. It matters more than most organizations realize. IBM’s 2025 Cost of a Data Breach Report found that 97% of organizations that reported an AI-related security incident lacked proper AI access controls.

Building AI governance from day one isn’t optional, it’s the foundation.

Manage every model, agent, app, and SaaS contract from one place. Route every internal AI agent through a single governance layer. The reality in most organizations is that the majority of AI tools are running without IT approval or oversight, creating security holes and data silos that quietly undermine the business.

Cut SaaS, not headcount. Replace the per-seat tools nobody opens with lightweight internal apps purpose-built for exactly how your team works. Teams can build tier-1 helpdesk agents, onboarding and offboarding workflows, self-serve provisioning portals, and AI usage dashboards—in other words, the tools your CFO and CISO will actually open.

5. From reactive to proactive: Legal efficiency and oversight

Legal and compliance functions are often the last to modernize, grappling with vast documentation, complex regulations, and a reputation for slowing everything down. Contract reviews pile up. Compliance questions arrive at 4:55 PM. The legal team becomes the bottleneck on every deal. AI is transforming these roles into proactive strategic assets, ensuring faster compliance, mitigating risks, and freeing legal professionals for the high-value counsel that drives business value.

Cut contract review time from hours to minutes. AI rapidly scans, analyzes, and drafts legal documents—contracts, NDAs, employment agreements—with policy guardrails baked in. It identifies key concerns, checks for compliance against internal policies or external regulations, and summarizes critical information. Policy-aware drafting means the first version is already cleaner than the fifth was before.

Search across every contract, policy, and prior matter instantly. One intelligent, permission-aware search across contracts, policies, and prior matters eliminates the archaeology of legal research. AI can sift through case law, legislation, and prior decisions to improve the speed and accuracy of research, and can analyze historical data to predict the likely outcome of disputes.

Spot legal risks before they become problems. AI proactively monitors for compliance issues, scans jurisdiction-specific laws, and highlights legal risks in mergers, acquisitions, and partnerships before they escalate. The legal team stops being the bottleneck and starts being the early-warning system.

6. Optimize talent: Human-centered HR and people strategy

HR departments are evolving from administrative hubs to strategic partners, focused on employee experience, talent development, and efficient acquisition. Onboarding decks go unread. Pulse surveys get no follow-up. AI streamlines these processes, personalizes employee journeys, and provides data-driven insights to foster a more engaged and productive workforce.

Personalize the employee experience, around the clock. AI agents trained on your handbook, benefits, and culture answer HR policy questions 24/7, generate tailored onboarding materials, and recommend personalized learning paths based on individual career goals and skill gaps. When an employee asks about parental leave at 11:30 PM, they get an accurate, policy-linked answer by 11:32—not a ticket in a queue.

Make smarter talent decisions, faster. AI assists in screening resumes, matching candidates to roles based on skills and cultural fit, and identifying skill gaps within the existing workforce. Recruitment cycles shorten, targeted development plans become actionable, and the right talent lands in the right place faster.

Surface engagement insights before attrition becomes the signal. By analyzing feedback from surveys, internal communications, and other sources, AI gives HR managers faster, more actionable insights into engagement levels and sentiment. AI can read culture and pulse data and surface what to act on proactively, before attrition becomes the signal.

7. Enhance experiences: Rethink customer service and support

Customer service teams face immense pressure to deliver personalized, rapid, and accurate support while managing ever-increasing volumes. AI transforms this by creating more efficient, empathetic, and proactive customer interactions, turning customer service into a strategic advantage rather than a cost center.

Create intelligent virtual assistants that understand nuance. The latest AI-driven assistants handle complex conversations, understand context, and provide accurate, personalized responses, freeing human agents from repetitive queries to focus on high-touch interactions that build lasting loyalty. Gartner predicts that 25% of enterprise customer service departments will use AI to augment live agent interactions by 2027, leading to more complex case handling, not fewer agents.

Build in proactive problem prevention. AI analyzes customer data, interaction history, and external signals to identify patterns and predict potential issues before they arise. Organizations can proactively reach out with solutions, transforming reactive support into preventative, value-added care.

Empower human agents with real-time context. AI tools equip agents with real-time access to personalized customer history, comprehensive knowledge bases, and suggested responses based on conversation context. The result is faster resolutions, more confident agents, and customers who spend less time on hold and more time getting what they came for. The goal isn't fewer agents—it's better ones.

8. Gain foresight: Strategic financial analysis and risk management

Financial analysts constantly navigate vast, complex datasets to provide accurate forecasts, assess risks, and identify opportunities. AI brings unprecedented speed and accuracy to these tasks. McKinsey’s 2025 State of AI research found that software engineering and IT teams are reporting 10 to 20 percent cost reductions from AI, while marketing and product development are seeing revenue uplifts above 10 percent. This is a strong signal of where financial impact is concentrating first.

Forecast with precision. AI analyzes historical data, market trends, and economic factors to generate highly accurate financial forecasts, empowering better decisions on budgeting, investment, and long-term planning.

Uncover spending patterns. By analyzing transaction data, AI reveals intricate patterns in spending habits and financial behaviors, directly informing product offerings, pricing strategies, and targeted campaigns that optimize revenue and customer lifetime value.

Assess risk and control costs in real time. AI rapidly assesses credit, market, and liquidity risks by analyzing multiple indicators in real time. Organizations gain visibility into AI spend by team, project, user, and model, with budgets and guardrails in place before the surprise invoice arrives.

9. Innovate faster: Accelerate product development and market fit

Product development teams are under constant pressure to deliver innovative solutions that meet evolving customer needs. AI acts as a catalyst, providing deeper customer insights, accelerating design iteration, and optimizing the development lifecycle for faster, more successful launches.

Identify what users actually need. AI analyzes extensive user feedback, market trends, and performance data to identify unmet needs and suggest optimized designs and features, reducing the costly risk of building things nobody wants.

Iterate faster than the roadmap allows. Paste a mock of a dashboard or tracker and an AI app builder returns a working version with data wired in. What used to require a quarter-long roadmap ships the same afternoon. AI empowers product developers to quickly generate and test design alternatives, enabling agile iteration at a speed that genuinely changes what’s possible.

Optimize for manufacturing, not just experience. AI can optimize product designs not just for user experience, but also for manufacturing and supply chain efficiency—considering material availability, cost implications, and logistical feasibility to reduce waste and time-to-market.

10. Automate smarter: Streamlining intelligent workflow automation

Beyond simple rules-based automation, AI introduces adaptive intelligence to workflows — allowing businesses to streamline complex, multi-step processes, minimize errors, and free employees for higher-value work that requires creativity and critical thinking.

Validate and clean data automatically. AI reads, understands, and validates different types of data inputs from diverse sources, ensuring accuracy and significantly reducing manual effort across business processes, from onboarding forms to inventory updates.

Process documents without the manual lift. AI processes invoices, contracts, and customer feedback by reading and understanding data from diverse formats. It extracts relevant information, verifies details against existing records, and automatically updates financial systems or CRM, transforming unstructured data into actionable insights.

Build what you need, cut what you don't. Teams can build the CRM view, the ops dashboard, the partner portal, purpose-built for exactly how they work, on their own data, at a fraction of what legacy SaaS costs. This is where organizations achieve some of the most immediate and measurable productivity gains.

Cultivating human-AI skills for a smarter workforce

The rise of AI isn’t just about new tools. It’s about a new way of working that demands evolving human skills. Ignoring this human element often leads to frustration and underperformance, turning a powerful tool into a mere novelty. Here are the essential skills that separate organizations that thrive with AI from those that merely tolerate it.

Effective prompt engineering: The art of asking the right questions. AI’s power lies in generating contextually relevant content, but its effectiveness is only as good as the input it receives. Mastering prompt engineering means crafting clear, precise, and well-structured instructions — iterative prompting, providing context, defining tone, specifying desired formats. As this skill becomes a core professional competency, it empowers users to move beyond generic outputs and obtain highly customized, actionable intelligence.

Critical thinking and verification: AI’s outputs need a human eye. While AI excels at generating content at speed, it is prone to hallucinations—sometimes producing confident-sounding but incorrect information, or reflecting biases present in its training data. The ability to critically evaluate AI outputs and verify against reliable sources is paramount. This is the ultimate guardrail against misinformation and flawed decision-making.

Data literacy and ethics: Understanding AI’s building blocks and boundaries. Developing data literacy means understanding where AI’s data comes from, its limitations, and how it impacts outputs. 56% of IT leaders are concerned about confidential data exposure or non-compliant AI use in the workplace.

Adaptability and continuous learning. AI capabilities are evolving at an unprecedented pace. On average, more than two major LLMs are released every week, creating ongoing challenges for organizations trying to keep pace. Organizations that foster a culture of curiosity and continuous learning stay ahead of the curve.

Human-AI collaboration: The ultimate team sport. The most impactful AI applications involve seamless collaboration between humans and AI, leveraging the unique strengths of both. Humans bring creativity, empathy, strategic thinking, and contextual understanding. AI offers speed, data processing power, and tireless execution. The challenge is creating the conditions for that collaboration to work, with proper governance, clear policies, and a unified platform that doesn’t fragment into shadow IT.

Navigating the complexities of AI for smarter work

AI is reshaping the workplace — not by replacing human intelligence, but by amplifying it across virtually every function. But adoption alone isn't the goal. McKinsey's 2025 research found that only about 6% of organizations are genuinely scaling AI in ways that drive transformative enterprise-wide impact — and they share a common trait: they did the readiness work first. They got their data in order, redesigned workflows before automating them, built governance structures before scaling, and measured outcomes from day one.

The first complexity to solve: Are you ready?

Thoughtful deployment, with clear goals, solid data, and governance built in from the start, is what separates the organizations that transform from the ones that stall. If you're working through what readiness looks like for your organization, our companion article,  AI Readiness: The DOs and DON'Ts That Determine Success, walks through exactly that.

AI is an inflection point for technology advisors

For technology advisors and channel partners, this moment is a genuine turning point. Customers aren’t just asking which AI tools to buy. They’re asking how to build a coherent, secure AI strategy that actually delivers results. That’s where advisors can add the most value: guiding clients through LLM selection, data governance, shadow IT risks, and the build-vs-buy decisions that define whether an AI investment pays off or stalls in pilot purgatory.

The advisors winning in this environment aren’t just reselling tools. They’re helping clients architect outcomes. That means understanding the governance requirements that keep chief information security officers comfortable, the cost models that make CFOs pay attention, and the use cases that generate visible ROI fast enough to earn the next conversation: sales automation, contract review, IT helpdesk, employee onboarding.

Devs.ai is built for exactly this partner motion. As an AppDirect platform, it gives advisors a governed environment to deploy on behalf of clients, with access to 70+ models, a 400+ partner marketplace network, and billing and distribution already wired up. Your clients get enterprise-grade AI. You get a repeatable, monetizable practice.

Build what you need with Devs.ai

Every challenge in this article, from the fragmented tools and shadow AI to SaaS sprawl and governance gaps, points to the same underlying problem: most organizations are assembling AI from the outside in, bolting consumer tools onto enterprise problems and hoping it holds. But it almost certainly won’t.

When the tools don't fit, build your own

Devs.ai gives organizations one governed platform to build the AI apps, agents, and workflows their teams actually need: purpose-built on their own data, under their own brand, without assembling infrastructure from scratch. It started as the platform AppDirect built for its own operations before opening it to every organization facing the same challenge.

One platform instead of ten contracts

Access 70+ leading LLMs in private workspaces, pick and swap models per agent or app without rewriting prompts or losing memory, and manage everything on one consolidated bill. No multi-vendor juggling. No lock-in.

Governance that enables, not obstructs

The shadow AI problem doesn't get solved by saying no. It gets solved by giving every team a safe, sanctioned place to build, with role-based access controls, audit trails, dedicated cloud instances, and zero training on your data.

Read and in-depth article on the latest Devs.ai developments: The Three Reasons Enterprise AI Stalls, and How Devs.ai Clears All of Them.

Start building what you need using Devs.ai today.

Denise Sarazin is a technology writer who specializes in breaking down complex B2B topics into practical insights for technology providers, sellers, and decision-makers.

LAST UPDATE: May 27, 2026