{"id":152,"date":"2026-06-12T14:18:18","date_gmt":"2026-06-12T14:18:18","guid":{"rendered":"https:\/\/www.softwarestech.com\/blog\/?p=152"},"modified":"2026-07-05T11:12:50","modified_gmt":"2026-07-05T11:12:50","slug":"how-ai-is-transforming-businesses-2026","status":"publish","type":"post","link":"https:\/\/www.softwarestech.com\/blog\/how-ai-is-transforming-businesses-2026\/","title":{"rendered":"How Artificial Intelligence Is Transforming Modern Businesses in 2026"},"content":{"rendered":"\n<p><strong>Written by the Softwarestech AI &amp; Data Engineering Team<\/strong> \u2014 reviewed by machine learning engineers and data scientists. <em>Last updated: June 2026.<\/em><\/p>\n\n\n\n<blockquote class=\"wp-block-quote is-layout-flow wp-block-quote-is-layout-flow\"><p>Over the last two years, we&#8217;ve moved roughly forty AI projects from pilot to production for clients across retail, finance, healthcare administration, and manufacturing. The pattern is consistent: the businesses that win with AI for business in 2026 aren&#8217;t the ones with the flashiest demo. They&#8217;re the ones that picked one painful, well-defined process and stuck with it through the unglamorous work of integration, monitoring, and retraining.<\/p><\/blockquote>\n\n\n\n<div style=\"border:1px solid #e2e8f0;background:#ffffff;padding:20px 24px;border-radius:12px;margin:24px 0\">\n<p style=\"margin:0 0 10px;font-weight:700;color:#1e293b\">On This Page<\/p>\n<ul style=\"margin:0;padding-left:20px;columns:2;column-gap:24px\">\n<li><a href=\"#where-businesses-are-with-ai\" style=\"color:#2563EB;text-decoration:none\">Where Businesses Actually Are With AI in 2026<\/a><\/li>\n<li><a href=\"#generative-ai-workflows\" style=\"color:#2563EB;text-decoration:none\">Generative AI in Day-to-Day Workflows<\/a><\/li>\n<li><a href=\"#ai-agents-workflow-automation\" style=\"color:#2563EB;text-decoration:none\">AI Agents and Workflow Automation<\/a><\/li>\n<li><a href=\"#predictive-analytics-forecasting\" style=\"color:#2563EB;text-decoration:none\">Predictive Analytics and Forecasting<\/a><\/li>\n<li><a href=\"#ai-customer-service\" style=\"color:#2563EB;text-decoration:none\">AI in Customer Service<\/a><\/li>\n<li><a href=\"#industry-specific-use-cases\" style=\"color:#2563EB;text-decoration:none\">Industry-Specific Use Cases<\/a><\/li>\n<li><a href=\"#two-examples-from-recent-projects\" style=\"color:#2563EB;text-decoration:none\">Two Examples From Recent Projects<\/a><\/li>\n<li><a href=\"#business-function-table\" style=\"color:#2563EB;text-decoration:none\">Business Function, AI Use Case, and Impact<\/a><\/li>\n<li><a href=\"#ai-governance-data-privacy\" style=\"color:#2563EB;text-decoration:none\">AI Governance, Data Privacy, and Regulation<\/a><\/li>\n<li><a href=\"#build-vs-buy\" style=\"color:#2563EB;text-decoration:none\">Build vs. Buy: Choosing the Right AI Approach<\/a><\/li>\n<li><a href=\"#getting-started\" style=\"color:#2563EB;text-decoration:none\">Getting Started Without Boiling the Ocean<\/a><\/li>\n<li><a href=\"#faq\" style=\"color:#2563EB;text-decoration:none\">Frequently Asked Questions<\/a><\/li>\n<\/ul>\n<\/div>\n\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" alt=\"Diagram of an AI brain connected to Sales, Customer Support, Operations, and Finance nodes, showing how AI is transforming business operations in 2026\"  loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"538\" src=\"https:\/\/www.softwarestech.com\/blog\/wp-content\/uploads\/2026\/07\/stx-how-ai-is-transforming-businesses-2026-img1-1783196752598-1024x538.png\" \/><\/figure>\n\n\n\n<div style=\"border:1px solid #e2e8f0;background:#f8fafc;padding:24px;border-radius:12px;margin:24px 0\">\n<h2 style=\"margin-top:0\">Key Takeaways<\/h2>\n<ul>\n<li><strong>Pilots are giving way to production:<\/strong> most mid-sized businesses now have at least one AI system running on live data, not just a sandbox demo.<\/li>\n<li><strong>Generative AI&#8217;s biggest wins are boring:<\/strong> drafting, summarizing, and searching internal knowledge save real hours, but rarely replace entire roles.<\/li>\n<li><strong>AI agents are different from chatbots:<\/strong> agents take multi-step actions across systems (checking a database, updating a ticket, sending an email) rather than just answering questions.<\/li>\n<li><strong>Predictive analytics pays for itself fastest:<\/strong> demand forecasting and inventory optimization often show ROI within one or two quarters.<\/li>\n<li><strong>Human handoff still matters in customer service:<\/strong> the best setups use AI to triage and resolve simple cases while routing complex or emotional issues to people.<\/li>\n<li><strong>Governance can&#8217;t be an afterthought:<\/strong> data privacy, audit trails, and model documentation are now expected by customers, partners, and regulators alike.<\/li>\n<li><strong>Build vs. buy depends on how core the workflow is:<\/strong> off-the-shelf tools are fine for common tasks; custom AI makes sense when the workflow is part of your competitive edge.<\/li>\n<\/ul>\n<\/div>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"where-businesses-are-with-ai\">Where Businesses Actually Are With AI in 2026<\/h2>\n\n\n\n<p>Three years ago, &#8220;doing AI&#8221; mostly meant a chatbot widget on a website and a slide deck about &#8220;exploring use cases.&#8221; That phase is mostly over. By mid-2026, most companies we talk to have moved past experimentation for at least one process and into what we&#8217;d call &#8220;production AI&#8221;: systems that run continuously, touch real customer or operational data, and have someone accountable when they go wrong.<\/p>\n\n\n\n<p>That doesn&#8217;t mean every business has a mature AI strategy. Far from it. What we typically see is a barbell: one or two AI systems that are genuinely embedded in daily operations (often a customer support assistant or a forecasting model), and a long tail of half-finished pilots that never got past the proof-of-concept stage because nobody budgeted for the integration work, the monitoring, or the retraining.<\/p>\n\n\n\n<p>The gap between those two groups usually isn&#8217;t the model. Most teams are using broadly similar foundation models under the hood: variants of GPT-4.1\/4.5-class models, Claude, Gemini, or open-weight models like Llama 3 and Mistral fine-tuned for a specific task. The difference is engineering discipline. Data pipelines that keep the model fed with current information, evaluation processes that catch when outputs drift, and a clear owner inside the business who treats the AI system like any other piece of production software, not a science experiment.<\/p>\n\n\n\n<p>If your organization is somewhere in the middle (one successful pilot, a few stalled ones, and pressure from leadership to &#8220;do more with AI&#8221;), you&#8217;re in good company. The rest of this article walks through where AI for business 2026 is delivering real value, where the hype outpaces the substance, and how to think about your next move.<\/p>\n\n\n\n<div style=\"flex-wrap:wrap;gap:16px;margin:20px 0\">\n  <div style=\"flex:1;min-width:140px;text-align:center;background:#f8fafc;border:1px solid #e2e8f0;border-radius:10px;padding:16px\">\n    <span style=\"align-items:center;justify-content:center;width:40px;height:40px;background:#eff6ff;border-radius:8px\">\n<title>AI Brain<\/title>\n\n\n\n    <\/span>\n    <p style=\"margin:8px 0 0;font-weight:600;font-size:14px\">Generative AI<\/p>\n  <\/div>\n  <div style=\"flex:1;min-width:140px;text-align:center;background:#f8fafc;border:1px solid #e2e8f0;border-radius:10px;padding:16px\">\n    <span style=\"align-items:center;justify-content:center;width:40px;height:40px;background:#eff6ff;border-radius:8px\">\n<title>Machine Learning<\/title>\n\n\n\n\n\n\n\n    <\/span>\n    <p style=\"margin:8px 0 0;font-weight:600;font-size:14px\">Predictive Analytics<\/p>\n  <\/div>\n  <div style=\"flex:1;min-width:140px;text-align:center;background:#f8fafc;border:1px solid #e2e8f0;border-radius:10px;padding:16px\">\n    <span style=\"align-items:center;justify-content:center;width:40px;height:40px;background:#eff6ff;border-radius:8px\">\n<title>Automation<\/title>\n\n\n\n\n\n    <\/span>\n    <p style=\"margin:8px 0 0;font-weight:600;font-size:14px\">AI Agents &amp; Automation<\/p>\n  <\/div>\n  <div style=\"flex:1;min-width:140px;text-align:center;background:#f8fafc;border:1px solid #e2e8f0;border-radius:10px;padding:16px\">\n    <span style=\"align-items:center;justify-content:center;width:40px;height:40px;background:#eff6ff;border-radius:8px\">\n<title>Analytics<\/title>\n\n\n\n\n\n    <\/span>\n    <p style=\"margin:8px 0 0;font-weight:600;font-size:14px\">Governance &amp; Reporting<\/p>\n  <\/div>\n<\/div>\n\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" alt=\"Bar chart comparing AI adoption levels for AI for business 2026 across customer support, operations, marketing, finance, and product development\"  loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"538\" src=\"https:\/\/www.softwarestech.com\/blog\/wp-content\/uploads\/2026\/07\/stx-how-ai-is-transforming-businesses-2026-img2-1783196754643-1024x538.png\" \/><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"generative-ai-workflows\">Generative AI in Day-to-Day Workflows: Realistic Gains, Not Hype<\/h2>\n\n\n\n<p>Generative AI&#8217;s most visible use case is still text: drafting emails, summarizing meetings, writing first-draft reports, and answering internal questions. None of that is glamorous, but it adds up. In our own internal benchmarking across client teams using tools like Microsoft 365 Copilot, Google Gemini for Workspace, or custom internal assistants built on the Claude or OpenAI APIs, we consistently see knowledge workers saving somewhere between 3 and 7 hours per week on writing and information-retrieval tasks. That&#8217;s a meaningful chunk of a 40-hour week, but it&#8217;s not the &#8220;AI replaces your team&#8221; story you&#8217;ll see in some vendor pitches.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" style=\"align-items:center;gap:10px\"><span style=\"width:32px;height:32px\"><title>Machine Learning<\/title>\n\n\n\n\n\n\n<\/span> Internal Copilots and Knowledge Search<\/h3>\n\n\n\n<p>The single highest-value generative AI deployment we build for clients is an internal copilot connected to the company&#8217;s own documents: wikis, policy manuals, past proposals, support tickets, contracts. Instead of an employee spending 20 minutes hunting through SharePoint or Confluence for &#8220;how do we handle a refund request over $500,&#8221; they ask the assistant directly and get an answer with a citation back to the source document. One professional services client we worked with cut new-hire ramp-up time on policy questions by roughly a third after rolling out an internal copilot built on a retrieval-augmented generation (RAG) architecture.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Drafting and Summarizing, Not Deciding<\/h3>\n\n\n\n<p>The realistic framing we give clients is simple: generative AI is excellent at producing a draft and terrible at being the final decision-maker. Sales teams use it to draft outreach emails and proposal sections, which a human then edits. Finance teams use it to summarize long vendor contracts into bullet points, which a human then verifies against the original. The productivity gain comes from skipping the blank page, not from removing the review step. Teams that skip the review step are the ones that end up with embarrassing hallucinated numbers in a client-facing document, and yes, we&#8217;ve seen it happen.<\/p>\n\n\n\n<div style=\"border-left:4px solid #10B981;background:#f0fdf4;padding:16px 20px;border-radius:0 8px 8px 0;margin:24px 0\">\n<p style=\"margin:0 0 6px;font-weight:700;color:#047857;text-transform:uppercase;font-size:13px;letter-spacing:0.05em\">Pro Tip<\/p>\n<p style=\"margin:0;color:#1e293b\">Before you roll out an internal copilot company-wide, run it past your three most skeptical employees first \u2014 the ones who&#8217;ll actually try to break it with edge-case questions. If it survives them, it&#8217;ll survive the rest of the company. If it doesn&#8217;t, you just found your gaps for free, before a customer or executive did.<\/p>\n<\/div>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"ai-agents-workflow-automation\">AI Agents and Workflow Automation<\/h2>\n\n\n\n<p>&#8220;AI agent&#8221; became one of the most overused terms of 2025, so it&#8217;s worth being precise. A chatbot answers questions. An agent takes actions. It can look something up in a database, call an API, fill out a form, update a record, or trigger a notification, often chaining several of those steps together to complete a task without a human clicking through each one.<\/p>\n\n\n\n<p>A practical example: a customer emails asking to change their shipping address on an order placed two days ago. A chatbot might tell them how to do it. An agent can actually check the order status in the warehouse system, confirm it hasn&#8217;t shipped, update the address in the fulfillment platform, and send a confirmation email, all in one flow, with a human only looped in if something doesn&#8217;t match expectations (the order already shipped, the new address fails validation, etc.).<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" style=\"align-items:center;gap:10px\"><span style=\"width:32px;height:32px\"><title>Automation<\/title>\n\n\n\n\n<\/span> What Makes Agents Reliable Enough for Production<\/h3>\n\n\n\n<p>The honest answer is: guardrails. The agent frameworks getting traction in 2026, built on tools like LangGraph, Microsoft&#8217;s AutoGen, OpenAI&#8217;s Agents SDK, or Anthropic&#8217;s Claude with tool use, all emphasize the same things: constrained tool access (the agent can only call specific, pre-approved functions), human-in-the-loop checkpoints for anything irreversible or above a dollar threshold, and detailed logging so you can audit exactly what the agent did and why. We tell clients not to deploy an agent that can take an action you wouldn&#8217;t want it to take autonomously a thousand times in a row, because eventually, it will.<\/p>\n\n\n\n<p>Where this pays off operationally is in workflow automation that used to require a person stitching together three or four systems: order management connected to inventory connected to customer communications, or HR onboarding workflows that touch payroll, IT provisioning, and benefits enrollment. If your team is already thinking about this kind of cross-system automation, it&#8217;s worth reading our guide to <a href=\"\/blogs\/modern-sdlc-guide-2026\/\">modern SDLC practices for 2026<\/a>, since reliable AI agents depend on the same testing and release discipline that good engineering teams already apply to traditional software changes. It&#8217;s also worth reading our guide to <a href=\"\/blogs\/devops-best-practices-2026\/\">DevOps best practices for 2026<\/a>, because the CI\/CD, testing, and observability habits that make a deployment pipeline trustworthy are the same habits that make an AI agent trustworthy.<\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" alt=\"Workflow diagram showing an AI agent moving from trigger event to processing to automated action to human review, a common AI automation pattern for 2026\"  loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"538\" src=\"https:\/\/www.softwarestech.com\/blog\/wp-content\/uploads\/2026\/07\/stx-how-ai-is-transforming-businesses-2026-img3-1783196756688-1024x538.png\" \/><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"predictive-analytics-forecasting\">Predictive Analytics and Forecasting<\/h2>\n\n\n\n<p>While generative AI gets the headlines, predictive analytics is where a lot of businesses see the clearest financial return. These are models, often gradient-boosted trees like XGBoost or LightGBM, or time-series models like Prophet and increasingly transformer-based forecasters, trained on your historical data to predict demand, churn, equipment failure, or cash flow.<\/p>\n\n\n\n<p>What changed by 2026 isn&#8217;t the underlying math so much as accessibility. Cloud platforms (AWS SageMaker, Azure ML, Google Vertex AI) now offer managed forecasting services that a small data team can stand up in weeks rather than months, and the cost of running these models at scale has dropped enough that even mid-sized businesses can justify it.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" style=\"align-items:center;gap:10px\"><span style=\"width:32px;height:32px\"><title>Analytics<\/title>\n\n\n\n\n<\/span> Inventory and Demand Forecasting<\/h3>\n\n\n\n<p>For retail and distribution businesses, demand forecasting is often the first AI project that pays for itself. By feeding a model historical sales data, seasonality patterns, promotional calendars, and external signals like local events or weather, you get demand predictions that are meaningfully more accurate than spreadsheet-based forecasting \u2014 especially for products with volatile or seasonal demand. We cover one such example in detail below.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Operational and Financial Forecasting<\/h3>\n\n\n\n<p>Beyond inventory, the same techniques apply to staffing levels (predicting call center volume by hour), cash flow (predicting which invoices are likely to be paid late), and equipment maintenance (predicting when a machine is likely to fail based on sensor data). The common thread is that these models don&#8217;t need to be perfect \u2014 they need to be better than the current method, which for most businesses is still &#8220;a person&#8217;s gut feeling plus last year&#8217;s numbers.&#8221;<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"ai-customer-service\">AI in Customer Service: Where the Human Handoff Still Matters<\/h2>\n\n\n\n<p>Customer service is probably the most mature AI use case by volume, but also the one where getting the balance wrong does the most reputational damage. The 2026 landscape has three layers worth distinguishing:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n\n<li><strong>Customer-facing chatbots<\/strong> handle simple, repetitive questions: order status, password resets, store hours, return policies. Modern versions handle these well, often resolving 40-60% of incoming volume without escalation.<\/li>\n\n\n<li><strong>Agent-assist copilots<\/strong> sit alongside human support reps, suggesting responses, pulling up relevant knowledge base articles, and summarizing long ticket histories so a rep doesn&#8217;t have to read through 15 back-and-forth emails before responding.<\/li>\n\n\n<li><strong>Full resolution agents<\/strong> (the workflow automation type described earlier) can actually process a return, issue a refund within policy limits, or update an account, not just talk about doing it.<\/li>\n\n<\/ul>\n\n\n\n<p>The mistake we see most often is companies trying to make layer one do the job of layer three: deploying a chatbot and hoping it reduces support headcount, without giving it the integration or authority to actually resolve anything. The result is a frustrating loop where the bot can&#8217;t help and the customer has to start over with a human anyway, which is worse than not having a bot at all.<\/p>\n\n\n\n<div style=\"border-left:4px solid #F59E0B;background:#fffbeb;padding:16px 20px;border-radius:0 8px 8px 0;margin:24px 0\">\n<p style=\"margin:0 0 6px;font-weight:700;color:#B45309;text-transform:uppercase;font-size:13px;letter-spacing:0.05em\">Common Pitfall<\/p>\n<p style=\"margin:0;color:#1e293b\">Don&#8217;t ship a customer-facing chatbot before deciding what it&#8217;s allowed to actually do. We&#8217;ve seen teams launch a polished-looking bot that can hold a conversation but can&#8217;t issue a refund, change an address, or escalate cleanly \u2014 so every conversation ends with &#8220;please contact support,&#8221; and customers leave more frustrated than if the bot didn&#8217;t exist. Scope the bot&#8217;s authority first, build the conversation layer second.<\/p>\n<\/div>\n\n\n\n<p>The cases that work well always preserve an easy, fast handoff to a human \u2014 and they&#8217;re upfront that the customer is talking to an AI. Beyond being good practice, transparency about AI use in customer interactions is increasingly expected by regulators and customers alike (more on this below).<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"industry-specific-use-cases\">Industry-Specific Use Cases<\/h2>\n\n\n\n<p>The &#8220;AI for business&#8221; conversation looks different depending on your industry. Here&#8217;s how we see it playing out across the sectors we work with most.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Retail: Personalization and Inventory<\/h3>\n\n\n\n<p>Beyond demand forecasting, retailers are using AI for product recommendations, dynamic pricing within set guardrails, and personalized marketing copy generated at scale (hundreds of product description variants for different audience segments, for example). The common requirement across all of these is clean, well-structured product and customer data \u2014 which is often a bigger lift than the AI model itself. If your team is rebuilding storefront or product experiences around this kind of personalization, our <a href=\"\/blogs\/web-development-trends-2026\/\">web development trends for 2026<\/a> piece covers how AI-powered personalization and code generation are showing up in modern front-end work.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Healthcare: Administrative Automation<\/h3>\n\n\n\n<p>To be clear about scope: we&#8217;re talking about administrative and operational AI here, not diagnostic AI, which sits in a much more heavily regulated category with its own approval processes. The practical wins for healthcare organizations in 2026 are things like AI-assisted medical coding and billing review, appointment scheduling optimization, automated prior-authorization paperwork drafting, and clinical note summarization for clinician review. These reduce administrative burden without touching clinical decision-making, which keeps them in a much more manageable compliance lane.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Finance: Fraud Detection and Risk Scoring<\/h3>\n\n\n\n<p>Financial services firms have used machine learning for fraud detection for over a decade, but the models have gotten significantly better at catching novel fraud patterns in real time rather than after the fact. Modern fraud detection systems combine transaction-level anomaly detection with behavioral signals (typing patterns, device fingerprints, login locations) to flag suspicious activity within milliseconds, with a much lower false-positive rate than rules-based systems from five years ago \u2014 which matters a lot, because every false positive is a legitimate customer being declined or delayed.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Manufacturing: Predictive Maintenance<\/h3>\n\n\n\n<p>For manufacturers, the highest-impact AI application is usually predictive maintenance: using sensor data (vibration, temperature, acoustic signals) to predict equipment failures before they cause unplanned downtime. Unplanned downtime on a production line can cost tens of thousands of dollars per hour depending on the industry, so even a model that gives maintenance teams a few days&#8217; warning on a subset of failures can justify the investment many times over.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"two-examples-from-recent-projects\">Two Examples From Recent Projects<\/h2>\n\n\n\n<h3 class=\"wp-block-heading\">Example 1: Demand Forecasting for a Mid-Sized Retailer<\/h3>\n\n\n\n<p>A mid-sized home goods retailer we worked with was managing inventory across 40 stores largely through regional manager intuition and a shared spreadsheet template. Overstock on slow-moving seasonal items was tying up working capital, while popular items frequently sold out during peak weeks. We built a forecasting pipeline using two years of point-of-sale history, supplier lead times, and local event calendars, feeding a gradient-boosted forecasting model that produced store-level, SKU-level demand predictions updated weekly.<\/p>\n\n\n\n<p>Within two quarters of rollout, the client reported about a 22% reduction in overstock inventory value and a noticeable drop in stockouts on their top 200 SKUs during peak season. Just as importantly, regional managers kept their judgment in the loop. The system flagged recommended order quantities, and managers could override them, which built trust in the tool rather than having it feel imposed from above.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Example 2: Internal AI Assistant for a B2B Services Support Team<\/h3>\n\n\n\n<p>A B2B software services client had a support team of about 15 people handling roughly 800 tickets a week, many of which were variations on a small set of recurring issues (license activation, billing questions, integration troubleshooting). We built an internal AI assistant, connected via RAG to their knowledge base, past resolved tickets, and product documentation, that drafted suggested responses for incoming tickets directly inside their helpdesk tool (Zendesk).<\/p>\n\n\n\n<p>Support reps reviewed and edited the drafts rather than writing from scratch. After three months, average first-response time dropped from around 4 hours to under 45 minutes, and the team&#8217;s ticket backlog, which had been growing for over a year, started shrinking for the first time. No support staff were let go; instead, the team absorbed a 30% increase in ticket volume over the following year without adding headcount, which the client considered the real win.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"business-function-table\">Business Function, AI Use Case, and Typical Impact<\/h2>\n\n\n\n<figure class=\"wp-block-table\">\n<table style=\"width:100%;border-collapse:collapse\">\n<thead>\n<tr>\n<th style=\"border:1px solid #e2e8f0;padding:8px 12px;text-align:left\">Business Function<\/th>\n<th style=\"border:1px solid #e2e8f0;padding:8px 12px;text-align:left\">AI Use Case<\/th>\n<th style=\"border:1px solid #e2e8f0;padding:8px 12px;text-align:left\">Typical Impact \/ Metric<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td style=\"border:1px solid #e2e8f0;padding:8px 12px;text-align:left\">Customer Support<\/td>\n<td style=\"border:1px solid #e2e8f0;padding:8px 12px;text-align:left\">Chatbot triage + agent-assist copilot<\/td>\n<td style=\"border:1px solid #e2e8f0;padding:8px 12px;text-align:left\">40-60% of routine tickets resolved without escalation; response times cut by 50-80%<\/td>\n<\/tr>\n<tr>\n<td style=\"border:1px solid #e2e8f0;padding:8px 12px;text-align:left\">Inventory &amp; Supply Chain<\/td>\n<td style=\"border:1px solid #e2e8f0;padding:8px 12px;text-align:left\">Demand forecasting and replenishment<\/td>\n<td style=\"border:1px solid #e2e8f0;padding:8px 12px;text-align:left\">15-25% reduction in overstock; fewer stockouts on top SKUs<\/td>\n<\/tr>\n<tr>\n<td style=\"border:1px solid #e2e8f0;padding:8px 12px;text-align:left\">Sales &amp; Marketing<\/td>\n<td style=\"border:1px solid #e2e8f0;padding:8px 12px;text-align:left\">AI-drafted outreach, personalized content at scale<\/td>\n<td style=\"border:1px solid #e2e8f0;padding:8px 12px;text-align:left\">3-7 hours\/week saved per rep on writing tasks<\/td>\n<\/tr>\n<tr>\n<td style=\"border:1px solid #e2e8f0;padding:8px 12px;text-align:left\">Finance &amp; Risk<\/td>\n<td style=\"border:1px solid #e2e8f0;padding:8px 12px;text-align:left\">Fraud detection, anomaly scoring<\/td>\n<td style=\"border:1px solid #e2e8f0;padding:8px 12px;text-align:left\">Real-time flagging with lower false-positive rates than rules-based systems<\/td>\n<\/tr>\n<tr>\n<td style=\"border:1px solid #e2e8f0;padding:8px 12px;text-align:left\">Manufacturing &amp; Operations<\/td>\n<td style=\"border:1px solid #e2e8f0;padding:8px 12px;text-align:left\">Predictive maintenance from sensor data<\/td>\n<td style=\"border:1px solid #e2e8f0;padding:8px 12px;text-align:left\">Days of advance warning on key failures; reduced unplanned downtime<\/td>\n<\/tr>\n<tr>\n<td style=\"border:1px solid #e2e8f0;padding:8px 12px;text-align:left\">HR &amp; Internal Ops<\/td>\n<td style=\"border:1px solid #e2e8f0;padding:8px 12px;text-align:left\">Internal copilot for policy\/knowledge search<\/td>\n<td style=\"border:1px solid #e2e8f0;padding:8px 12px;text-align:left\">~30% reduction in time spent searching internal documentation<\/td>\n<\/tr>\n<tr>\n<td style=\"border:1px solid #e2e8f0;padding:8px 12px;text-align:left\">Healthcare Administration<\/td>\n<td style=\"border:1px solid #e2e8f0;padding:8px 12px;text-align:left\">Coding, billing review, note summarization<\/td>\n<td style=\"border:1px solid #e2e8f0;padding:8px 12px;text-align:left\">Reduced administrative hours per clinician per week<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/figure>\n\n\n\n<p>One pattern worth calling out from this table: the functions with the clearest AI for business 2026 ROI (customer support, inventory, finance) are also the ones with the most structured historical data already sitting in some system of record. That&#8217;s not a coincidence. If you&#8217;re trying to decide where to start, look first at where your data is already clean enough to use.<\/p>\n\n\n\n<div style=\"border:1px solid #e2e8f0;background:#ffffff;padding:24px;border-radius:12px;margin:24px 0\">\n<p style=\"margin:0 0 12px;font-weight:700;color:#1e293b;font-size:17px\">Quick Checklist: Is Your Data Ready for an AI Project?<\/p>\n<ul style=\"margin:0;padding-left:0\">\n<li style=\"padding:6px 0 6px 28px;position:relative;border-bottom:1px solid #f1f5f9\">\n<span style=\"position:absolute;left:0;top:6px;color:#10B981;font-weight:700\">&#10003;<\/span>You can pull at least 12-24 months of relevant historical data without a multi-week IT request<\/li>\n<li style=\"padding:6px 0 6px 28px;position:relative;border-bottom:1px solid #f1f5f9\">\n<span style=\"position:absolute;left:0;top:6px;color:#10B981;font-weight:700\">&#10003;<\/span>The data lives in a structured system (database, CRM, ERP) rather than scattered spreadsheets<\/li>\n<li style=\"padding:6px 0 6px 28px;position:relative;border-bottom:1px solid #f1f5f9\">\n<span style=\"position:absolute;left:0;top:6px;color:#10B981;font-weight:700\">&#10003;<\/span>Someone on your team can explain what the data fields actually mean in practice<\/li>\n<li style=\"padding:6px 0 6px 28px;position:relative;border-bottom:1px solid #f1f5f9\">\n<span style=\"position:absolute;left:0;top:6px;color:#10B981;font-weight:700\">&#10003;<\/span>Known data quality issues (duplicates, missing fields, inconsistent formats) are documented, not a surprise<\/li>\n<li style=\"padding:6px 0 6px 28px;position:relative;border-bottom:1px solid #f1f5f9\">\n<span style=\"position:absolute;left:0;top:6px;color:#10B981;font-weight:700\">&#10003;<\/span>You have a way to measure the current baseline (current accuracy, current resolution time, current cost) before AI touches it<\/li>\n<li style=\"padding:6px 0 6px 28px;position:relative;border-bottom:1px solid #f1f5f9\">\n<span style=\"position:absolute;left:0;top:6px;color:#10B981;font-weight:700\">&#10003;<\/span>Someone owns data access permissions and can document who\/what can see this data<\/li>\n<li style=\"padding:6px 0 6px 28px;position:relative\">\n<span style=\"position:absolute;left:0;top:6px;color:#10B981;font-weight:700\">&#10003;<\/span>You&#8217;ve identified at least one person who&#8217;ll be accountable for the system once it&#8217;s live<\/li>\n<\/ul>\n<\/div>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"ai-governance-data-privacy\">AI Governance, Data Privacy, and the Regulatory Landscape in 2026<\/h2>\n\n\n\n<p>Governance used to be the section of the AI conversation that got skipped in the rush to ship something. That&#8217;s no longer realistic. Through 2025 and into 2026, the regulatory picture for AI has continued to take shape across regions. The EU&#8217;s AI Act has begun phasing in obligations for higher-risk AI systems, a growing number of U.S. states have passed or proposed AI-specific disclosure and consumer protection laws, and existing data protection frameworks like GDPR and various state privacy laws apply fully to AI systems that process personal data, regardless of whether AI-specific rules exist yet.<\/p>\n\n\n\n<p>We&#8217;re deliberately not going to give you a state-by-state legal breakdown here. That&#8217;s a conversation for your legal counsel, and the rules are still moving. What we can tell you from an engineering and operational standpoint is what tends to hold up well regardless of how the specific regulations shake out. The <a href=\"https:\/\/www.nist.gov\/itl\/ai-risk-management-framework\" target=\"_blank\" rel=\"noopener noreferrer\">NIST AI Risk Management Framework<\/a> is a useful reference point here \u2014 it&#8217;s not a law, but it&#8217;s become a common baseline that auditors and enterprise customers increasingly ask about, even outside the US.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n\n<li><strong>Know what data your AI systems touch.<\/strong> If a model is trained on or has access to customer data, employee data, or other sensitive information, that needs to be documented and access-controlled the same as any other system holding that data.<\/li>\n\n\n<li><strong>Keep humans accountable for high-stakes decisions.<\/strong> Lending, hiring, healthcare, and similar categories increasingly require a documented human review step, not just an AI recommendation that gets rubber-stamped.<\/li>\n\n\n<li><strong>Disclose AI use to customers<\/strong> where it affects their experience: chatbots, AI-generated content, automated decisions. This is becoming standard practice even where not strictly mandated.<\/li>\n\n\n<li><strong>Maintain an audit trail.<\/strong> Logging what data went into a decision, which model version made it, and what the output was, turns a &#8220;the AI did something weird&#8221; incident into a fixable bug instead of a mystery.<\/li>\n\n<\/ul>\n\n\n\n<p>If your company handles sensitive data (health records, financial information, anything covered by industry-specific compliance frameworks), it&#8217;s worth pairing your AI roadmap with a broader look at your security posture. Our <a href=\"\/blogs\/cybersecurity-essentials-2026\/\">cybersecurity essentials guide for 2026<\/a> covers a lot of the same data-handling fundamentals that AI governance builds on. For a sense of how the broader research community is framing these questions \u2014 not just the regulatory side but the underlying capability and safety research \u2014 <a href=\"https:\/\/hai.stanford.edu\/\" target=\"_blank\" rel=\"noopener noreferrer\">Stanford HAI<\/a> publishes some of the most widely cited annual reporting on AI trends and policy, and it&#8217;s a good source to point skeptical stakeholders toward.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"build-vs-buy\">Build vs. Buy: Choosing the Right AI Approach<\/h2>\n\n\n\n<p>This is probably the question we get asked most often, and the honest answer is &#8220;it depends on how core the workflow is to your competitive position.&#8221;<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">When Off-the-Shelf Makes Sense<\/h3>\n\n\n\n<p>For common, well-understood tasks (email drafting, meeting summaries, general customer service chatbots, standard analytics dashboards), off-the-shelf tools like Microsoft Copilot, Google Gemini, Salesforce Einstein, or Zendesk AI are usually the right call. They&#8217;re cheaper, faster to deploy, vendor-supported, and improve automatically as the underlying models improve. There&#8217;s little competitive advantage in building your own version of a meeting summarizer.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">When Custom AI Makes Sense<\/h3>\n\n\n\n<p>Custom solutions earn their cost when the workflow involves your proprietary data, your specific processes, or a use case that&#8217;s central to how you compete: a recommendation engine tuned to your unique product catalog, a fraud model trained on your transaction patterns, an agent that orchestrates your specific multi-system fulfillment process. The build often isn&#8217;t really &#8220;building a model from scratch&#8221; anymore. It&#8217;s building the integration layer, data pipelines, evaluation framework, and guardrails around a foundation model API, which is a substantial engineering project in its own right even if the core model is &#8220;off the shelf.&#8221;<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">The Middle Path: Configure and Extend<\/h3>\n\n\n\n<p>A lot of the best outcomes we&#8217;ve seen land in between: starting with an off-the-shelf platform&#8217;s extensibility features (custom GPTs, Salesforce&#8217;s Agentforce, Microsoft Copilot Studio) and building targeted custom integrations only where the standard product falls short. This gets you to a working solution faster while preserving the option to invest more heavily in custom development once you&#8217;ve proven the use case actually delivers value. If you&#8217;re weighing this decision for your organization, our <a href=\"\/blogs\/why-businesses-need-it-consulting-2026\/\">IT consulting guide for 2026<\/a> goes deeper on how to structure that evaluation, and our <a href=\"\/blogs\/enterprise-software-solutions-2026\/\">enterprise software solutions overview<\/a> covers how AI fits into broader digital transformation planning \u2014 including where workflow automation and RPA tools overlap with the agent patterns described earlier in this article.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\" \/>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"getting-started\">Getting Started Without Boiling the Ocean<\/h2>\n\n\n\n<p>If there&#8217;s one piece of advice that applies across every client we&#8217;ve worked with, it&#8217;s this: pick one process, make it work end to end, and use that as proof before expanding. The businesses that struggle with AI for business 2026 are almost always the ones that tried to &#8220;do AI&#8221; everywhere at once: a chatbot here, a forecasting tool there, an agent pilot somewhere else, without anyone owning the data quality, monitoring, or follow-through for any of them.<\/p>\n\n\n\n<p>A practical starting checklist looks like this. Identify a process that&#8217;s repetitive, data-rich, and currently a known pain point (long support queues, manual forecasting in spreadsheets, slow document review). Confirm you have, or can reasonably get, clean historical data for it. Define what &#8220;better&#8221; looks like in measurable terms before you build anything. Start with the smallest version that could plausibly work, get it in front of real users quickly, and budget for the ongoing work of monitoring and improving it, because AI systems aren&#8217;t &#8220;done&#8221; the way a typical software feature is. They need to be watched, evaluated, and occasionally retrained as your business and data change.<\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" alt=\"Checklist grid of six requirements for a successful AI for business 2026 pilot: clear use case, clean data, human in the loop, pilot scope, success metrics, governance review\"  loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"538\" src=\"https:\/\/www.softwarestech.com\/blog\/wp-content\/uploads\/2026\/07\/stx-how-ai-is-transforming-businesses-2026-img4-1783196758876-1024x538.png\" \/><\/figure>\n\n\n\n<p>None of this requires a massive AI team. Most of the projects described in this article were delivered by small, focused teams of two to four engineers and data scientists working closely with the business stakeholders who&#8217;d actually use the system day to day. What matters more than team size is having someone who understands both the technical side and the business process well enough to know when the AI is working \u2014 and, just as importantly, when it isn&#8217;t. That&#8217;s really the throughline for AI for business 2026: less about which model you pick, more about whether someone is actually watching it.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"faq\">Frequently Asked Questions<\/h2>\n\n\n\n<h3 class=\"wp-block-heading\">How much does it cost to implement AI for a small or mid-sized business?<\/h3>\n\n\n\n<p>Costs vary widely depending on scope, but a focused first project \u2014 like an internal copilot or a demand forecasting pilot \u2014 typically ranges from the low tens of thousands of dollars for a configure-and-extend approach using existing platforms, up to significantly more for a fully custom build with new data pipelines and integrations. Ongoing costs (API usage, hosting, monitoring, and maintenance) should also be budgeted for, since they continue well after the initial build.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Will AI replace jobs in my company?<\/h3>\n\n\n\n<p>In most of the deployments we&#8217;ve been involved with, AI changed how work gets done more than it eliminated roles outright \u2014 support teams handled more volume without adding headcount, for example, rather than shrinking. That said, roles centered entirely on tasks AI now automates well (basic data entry, simple first-draft writing) are genuinely shrinking in scope, and it&#8217;s worth being honest with your team about that as you plan.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">How long does it take to see results from an AI project?<\/h3>\n\n\n\n<p>For well-scoped projects using existing data, a working pilot can often be in front of users within 6-12 weeks, with measurable business impact (cost savings, time savings, or revenue lift) becoming visible within one to two quarters. Projects that require building new data pipelines from scratch, or that involve significant organizational change management, take longer.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Do we need our own data science team to use AI effectively?<\/h3>\n\n\n\n<p>Not necessarily for your first projects. Many businesses start by working with an external partner or using configurable platform features, and build internal capability gradually as AI becomes more central to operations. What you do need internally, even early on, is someone who owns the AI initiative, understands the business process it touches, and can evaluate whether it&#8217;s actually working.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Is generative AI accurate enough to trust for business use?<\/h3>\n\n\n\n<p>For drafting, summarizing, and information retrieval with proper citations, modern generative AI is reliable enough for daily use as long as a human reviews outputs before they go to customers or into financial\/legal documents. It&#8217;s not yet reliable enough to be the sole decision-maker for high-stakes outputs without review \u2014 and most well-run AI programs build that review step in by design rather than treating it as optional.<\/p>\n\n\n\n\n<h2 class=\"wp-block-heading\">Further Reading<\/h2>\n\n\n\n<ul class=\"wp-block-list\">\n<li><a href=\"https:\/\/www.softwarestech.com\/blog\/digital-transformation-with-ai\/\">Digital Transformation with AI: Strategy &amp; ROI 2026<\/a><\/li>\n<li><a href=\"https:\/\/www.softwarestech.com\/blog\/enterprise-software-solutions-2026\/\">Enterprise Software Solutions 2026<\/a><\/li>\n<li><a href=\"https:\/\/www.softwarestech.com\/blog\/cybersecurity-essentials-2026\/\">Cybersecurity Essentials for AI-Driven Businesses<\/a><\/li>\n<\/ul>\n\n\n<p>For industry benchmarks and additional context, we recommend the <a href=\"https:\/\/www.mckinsey.com\/capabilities\/quantumblack\/our-insights\/\" target=\"_blank\" rel=\"noopener noreferrer\">McKinsey AI Insights<\/a>.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">How do we make sure our AI use stays compliant with privacy regulations?<\/h3>\n\n\n\n<p>Start by mapping what data your AI systems access and how it&#8217;s stored and logged, since most privacy obligations hinge on data handling rather than the AI itself. Build in human review for high-stakes decisions, disclose AI use to customers where it affects them, and keep audit trails of model versions and decisions. Given how quickly the regulatory landscape is evolving, it&#8217;s worth involving legal counsel early and treating compliance as an ongoing process rather than a one-time checklist.<\/p>\n\n\n\n<figure class=\"wp-block-image size-medium\" style=\"width:160px;margin:0 auto 16px\"><img decoding=\"async\" src=\"https:\/\/www.softwarestech.com\/blog\/wp-content\/uploads\/2026\/07\/stx-how-ai-is-transforming-businesses-2026-img5-1783196761104.png\" alt=\"AI Strategy badge icon representing Softwarestech's AI for business 2026 consulting services\" width=\"160\" \/><\/figure>\n\n\n\n<div style=\"background:linear-gradient(135deg,#2563EB 0%,#06B6D4 100%);color:#fff;padding:32px;border-radius:12px;margin:32px 0;text-align:center\">\n<h2 style=\"margin-top:0;color:#fff\">Ready to Move From AI Pilot to Production?<\/h2>\n<p>Our AI &amp; Data Engineering team helps businesses identify high-impact use cases, build the data pipelines and integrations behind them, and put the governance in place to run AI systems safely at scale. Explore our <a href=\"https:\/\/www.softwarestech.com\/services\" style=\"color:#fff;text-decoration:underline\">AI and data engineering services<\/a> or get in touch to talk through your roadmap.<\/p>\n<a href=\"https:\/\/www.softwarestech.com\/contact\" style=\"background:#fff;color:#2563EB;padding:14px 28px;border-radius:999px;font-weight:700;text-decoration:none;margin-top:8px\">Talk to Our AI Team<\/a>\n<\/div>\n\n","protected":false},"excerpt":{"rendered":"<p>A practical look at how companies are using AI for business in 2026, from generative copilots and predictive analytics to AI agents, governance, and build-vs-buy decisions.<\/p>\n","protected":false},"author":1,"featured_media":434,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"default","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","ast-disable-related-posts":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"default","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"ast-content-background-meta":{"desktop":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"publisher_sync_id":"local-wp-post-51","rank_math_title":"AI Transforming Businesses 2026: Real ROI &amp; Use Cases","rank_math_description":"How AI transforming businesses 2026 creates real ROI \u2014 agents, forecasting, copilots. Two client case studies, a build-vs-buy guide, and practical advice.","rank_math_focus_keyword":"AI transforming businesses 2026","footnotes":""},"categories":[11],"tags":[132,133,134,53,54,64,135,58,60,61],"class_list":["post-152","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-artificial-intelligence","tag-ai-agents","tag-ai-automation","tag-ai-for-business","tag-ai-roi","tag-ai-strategy","tag-artificial-intelligence","tag-business-ai","tag-digital-transformation","tag-enterprise-ai","tag-machine-learning"],"_links":{"self":[{"href":"https:\/\/www.softwarestech.com\/blog\/wp-json\/wp\/v2\/posts\/152","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.softwarestech.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.softwarestech.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.softwarestech.com\/blog\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.softwarestech.com\/blog\/wp-json\/wp\/v2\/comments?post=152"}],"version-history":[{"count":4,"href":"https:\/\/www.softwarestech.com\/blog\/wp-json\/wp\/v2\/posts\/152\/revisions"}],"predecessor-version":[{"id":410,"href":"https:\/\/www.softwarestech.com\/blog\/wp-json\/wp\/v2\/posts\/152\/revisions\/410"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.softwarestech.com\/blog\/wp-json\/wp\/v2\/media\/434"}],"wp:attachment":[{"href":"https:\/\/www.softwarestech.com\/blog\/wp-json\/wp\/v2\/media?parent=152"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.softwarestech.com\/blog\/wp-json\/wp\/v2\/categories?post=152"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.softwarestech.com\/blog\/wp-json\/wp\/v2\/tags?post=152"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}