Quick overview

We wrote this guide to demystify AI automations and show you exactly how to make them pay off. Along the way, we’ll unpack the biggest blocker that keeps most companies from seeing real ROI, share the lessons we’ve learned working with teams across industries, and walk you through a clear path to building automations that actually stick.

Think of it as a practical playbook that’s grounded in real-world wins and missteps, to help you move past AI considerations to becoming AI native.

What is an AI automation?

Traditional workflow automation executes multi-step business processes with little to no manual input. These automations can be as simple as automatically saving email attachments to a cloud drive.

AI automation takes this a step further by embedding AI into these processes to handle tasks that require autonomous decision-making to carry out tasks. Instead of just moving data, AI can analyze it, classify it, summarize it, and decide what to do next. Here’s some examples:

Ambient clinical documentation: Transcribes clinician–patient conversations and drafts structured visit notes directly into the EHR. Claims adjudication: Auto-triages simple insurance claims, verifies documentation, and issues payouts end-to-end. Dynamic route optimization: Continuously recalculates delivery routes to reduce miles, time, and fuel in real time. Product catalog enrichment: Extracts/normalizes product attributes from feeds/images to improve search, filters, and merchandising. Decision copilot for support: Classifies ticket intent, drafts resolutions with citations, and escalates only edge cases to humans.

Implementing AI automations is a crucial step for organizations looking to implement AI and push towards being AI nativity. Companies doing this are already ahead of the curve, enabling all their teams to make smarter decisions while moving faster.

Though the potential is real, putting AI automations in the hands of your team is not as simple as buying a platform or tool and calling it a day.

The hard AI truth

While the hype around AI power and ROI is real, the reality is that a staggering 95% of AI initiatives in companies fail to get their gen AI pilots to success [1] .

AI is more than capable, implementation practices are not. An org will be very excited about a new tool and dive headfirst into development, without any concrete plan on roll out or enabling teams to be successful with AI automations.

Orgs aiming to be AI native’s need to be methodical in choosing and rolling out an AI automation tool. Along with nailing down the rollout, the true key to guaranteeing success is strategically partnering with your AI automation platform to find ways they can enable your success. Overlooking this will guarantee wasting leadership time and company resources to produce no meaningful results.

We put this guide together using our years of experience partnering and enabling out customers with successful AI implementations, to help you find a clear path to AI ROI with AI automation tools.

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Finding success with AI automation

These are based on observations from our learnings over the years of helping enabling companies to achieve AI nativity.

This is the benefits of AI automations you can expect from implementing a AI automations tool like Vellum AI into your org:

AI automations by industries

Healthcare

Healthcare organizations juggle patient data, compliance paperwork, and time-sensitive communications. Much of this work is repetitive and prone to error, which can delay care and increase risk.

AI cuts the busywork ,so healthcare providers can focus on care. Intake moves faster. Notes are cleaner. Compliance is easier because every action is logged and consistent. The net effect is shorter time-to-care and more capacity without burning out the team.

Here are some examples of  the AI automations being used by healthcare teams today:

Team AI Automations Operations Automate patient intake by extracting form data into EHRs; streamline scheduling. Patient Services Classify and prioritize patient inquiries by urgency; auto-route to providers. Compliance Review and summarize regulatory updates; automate audit preparation.

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Insurance

Insurance workflows involve heavy documentation, manual reviews, and regulatory checks. Delays or errors can lead to dissatisfied customers and compliance risks.

Automation speeds up the whole journey from intake to payout. Simple claims flow straight through. Underwriting is more consistent because data is extracted the same way every time. Customers get answers sooner and regulators see a clean paper trail.

Here are some examples of the AI automations helping insurance teams today:

Team AI Automations Claims Auto-classify claims, flag fraud, and route complex cases to adjusters. Underwriting Analyze applications; pre-fill key data to accelerate policy approvals. Compliance Automate policy document review for regulatory compliance.

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eCommerce

eCommerce teams manage high volumes of customer interactions, product data, and sales reporting. Manual work in these areas can lead to stockouts, delayed responses, or missed revenue opportunities.

Shoppers get a sharper storefront and faster help. Product data stays clean, which lifts search and merch. Support triages itself so humans handle the real problems. Promotions and inventory stay in sync, which means fewer misses and more revenue.

Here are some examples of the AI automations helping eCommerce teams today:

Team AI Automations Marketing Auto-generate SEO product descriptions; personalize campaigns at scale. Customer Support Classify and route tickets by intent/sentiment; draft responses for FAQs. Merchandising Summarize daily sales and inventory data; auto-generate trend reports.

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Supply chain & logistics

Supply chains rely on precise timing and clear communication. Manual errors or missed updates can cause costly delays.

You stop reacting and start anticipating. Forecasts update before stockouts hit. Routes adapt in real time so miles, fuel, and delays drop. Exceptions surface fast with clear playbooks, which keeps partners and customers in the loop.

Here are some examples of the AI automations helping supply chain and logistics teams today:

Team AI Automations Operations Predict demand; auto-generate purchase orders; optimize shipment routing. Procurement Classify invoices; auto-approve low-risk vendor payments. Finance Summarize supplier costs; generate risk and spend reports for leadership.

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Legal

Legal teams deal with mountains of contracts, compliance checks, and research tasks. Much of this work is repetitive and rules-based, making it an ideal candidate for automation.

Contracts stop bottlenecking deals. Clauses are pulled and checked the same way every time. First drafts land in minutes, not days. Risk goes down because policy checks and audit logs are built into the process.

Here are some examples of the AI automations helping legal teams today:

Team AI Automations Legal Ops Extract and classify contract clauses; automate redlining. Compliance Streamline due diligence workflows in M&A or audits. Research Summarize case law and precedents into briefs.

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EdTech

EdTech companies face challenges in scaling support for learners while managing administrative overhead. Manual grading, progress tracking, and onboarding slow down growth.

Ops runs smoother and teachers get their time back. Onboarding clicks into place. Progress signals roll up automatically so at-risk students get help earlier. Feedback scales without losing the human touch.

Here are some examples of the AI automations helping edtech teams today:

Team AI Automations Academic Ops Automate onboarding workflows for students and instructors. Student Support Summarize student performance; flag at-risk learners for intervention. Curriculum Generate personalized study plans and practice quizzes.

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AdTech

AdTech teams manage data-heavy campaigns across multiple platforms. Manual tracking and reporting can cause slow responses to performance issues.

AI automations push budgets move to where performance is strongest. Reports write themselves so teams act in hours, not weeks. Creative tests scale without going off-brand. Pacing stays healthy and policy checks happen before problems do.

Here are some examples of the AI automations helping adtech teams today:

Team AI Automations Campaign Ops Auto-adjust cross-channel budgets based on performance data. Analytics Summarize campaign data; generate client-ready reports. Creative Generate ad copy variations tailored to audience segments.

AI automations by team

Engineering

AI automations can take on much of the routine engineering toil, like digging through logs, triaging flaky tests, or tracking performance regressions, so engineers stay unblocked and shipping focused. They can build automations that flag performance regressions from CI runs, summarize long PRs, and detect API/contract drift before it hits prod.

We see engineers using Vellum to build automations that triage incidents, summarize logs and PRs, and catch perf or schema regressions early to ship faster.

Product

AI automations help product teams pull feedback from tickets, call notes, and usage events, cluster it into themes, and tie each theme to the metrics PMs own so prioritization isn’t guesswork. They can draft PRDs or acceptance criteria from patterns, and generate UI/flow previews to sanity-check scope before handoff [2] .

We see product teams using Vellum to build automations that unify feedback, surface clear themes with evidence, and draft PRD/AC starters to move faster and make the most out of every sprint.

Sales

AI automations help sales teams spend more time on closing deals that move the needle by automating account enrichment, score leads, and draft outreach tailored to persona and stage to massively cut CRM busywork. Reps can also expedite the time to prep for high value demos and presentations, while delivering context specific and personalized follow-ups automatically.

We see sales teams using Vellum to build automations that enrich records, prioritize targets, and enhance all the prep work to close deals faster.

Revenue Operations (RevOps)

AI automations help revops teams reconcile CRM, billing, and finance data, flag anomalies, and refresh forecasts to avoid end-of-quarter surprises. Reviews are better spent on strategy, rather than spreadsheet debates.

We see revops using Vellum to build automations that sync metrics, detect drift, and update forecasts to keep the revenue pipelines optimized.

Operations

Ops teams can use AI automations to orchestrate cross-team handoffs, assign owners, and monitor SLAs to alleviate tedious manual ops work and help operations move faster. These automations surface blockers early with proposed next steps, while updating status across tools automatically.

We see operations teams using Vellum to build automations that trigger advancement steps, manage escalations, and keep SLA dashboards current to maintain predictable throughput.

Marketing

AI automations enable marketing teams to move 10x faster with brand aware agents that produce research and content. Marketers can spend more time on creative strategy and optimizing conversions funnels, rather than tedious content creation.

We see marketing teams using Vellum to build automations that create social media content, blog content, newsletters, and deep competitor research to compile learnings to ship campaigns faster.

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Customer Support

Customer support teams us AI automations to classify support request intent, suggest policy-correct replies, and escalate only edge cases, making queues manageable. Live agents, assisted context aware agents that enable comprehensive customer support can spend more time on nuanced and complex issues.

We see customer support teams using Vellum to build automations that route support tickets by intent, draft cited replies, implement RAG powered support chats, and flag exceptions to reduce handle time.

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Compliance & Legal

Compliance and legal teams use AI automations to extract clauses, compare them to standards, and summarize regulatory updates so reviews are consistent. This keeps lawyers focused on decisions, not document scanning.

We see legal teams using Vellum to build automations that parse contracts, check policy deltas, and rope in subject matter experts where needed.

Data & Analytics

Data teams are enabled to move faster to surfacing high value data by AI automations that can label datasets, generate recurring summaries with citations, and deliver self-serve answers so analysts spend less time on prep. Through this stakeholders can get trusted numbers without ad-hoc requests blocking data teams for priority work.

We see data teams using Vellum to gain precious hours back with automations for validating inputs, shipping recurring briefs, and enabling self-serve answers.

Team Common Challenges How AI Automations Help Engineering Log spelunking, flaky test triage, hidden perf regressions, API/contract drift. AI automations take on routine engineering toil—clustering errors, flagging regressions from CI, summarizing long PRs, and detecting schema drift—so engineers stay unblocked and shipping-focused. Product Scattered feedback, manual synthesis, slow prioritization and scope drift. Consolidate tickets, calls, and usage; cluster themes tied to KPIs; draft PRDs/ACs; and preview flows before handoff so prioritization isn’t guesswork. Subject-Matter Experts (SMEs) Repetitive reviews, context switching, becoming a bottleneck. Encode checklists, policy critics, and templated analyses so routine cases resolve automatically; exceptions arrive with context and a first pass. Operations Manual handoffs, unclear ownership, missed SLAs and status chasing. Orchestrate next steps, assign owners, monitor SLAs, and surface blockers with suggested actions while updating status across tools automatically. Customer Support Spiky queues, inconsistent triage, repetitive replies, scattered knowledge. Classify intent/sentiment, draft policy-correct responses with citations, route edge cases, and auto-compile case wrap-ups for reuse. Sales CRM admin, account research, inconsistent talk tracks and follow-ups. Enrich accounts, score leads by fit/engagement, generate persona-aware outreach and objection handling, and auto-log notes/next steps. Revenue Operations (RevOps) Conflicting metrics, slow reconciliations, unreliable forecasts. Reconcile CRM/billing/finance data on a cadence, flag anomalies, and refresh forecasts with explainable drivers and weekly readouts. Marketing Content backlog, personalization gaps, slow learnings and taxonomy drift. Turn briefs into on-brand, channel-ready variants; localize by segment; and feed performance learnings back into future briefs. Merchandising / Category Mgmt Messy catalogs, missing attributes, pricing/stock errors, weak discoverability. Normalize attributes, enrich copy from feeds/images, and flag pricing/stock anomalies before they hit the storefront. Finance Manual reconciliations, approval bottlenecks, slow close, variance rework. Classify transactions, auto-match records across subledgers, route approvals with audit trails, and draft variance explanations from source data. HR / People Ops High screening volume, scheduling friction, repetitive policy Q&A. Pre-screen applicants against role criteria, coordinate interview calendars, guide onboarding steps, and answer policy FAQs consistently. Compliance & Legal Stacked contract reviews, shifting regs, inconsistent redlines. Extract/compare clauses to playbooks, highlight deviations, summarize regulatory updates, and assemble review packets. Data & Analytics Data prep drain, recurring report requests, ad-hoc interruptions. Label datasets, generate recurring summaries with citations, deliver self-serve answers, and watch data quality with owner alerts. IT / Security Provisioning load, access reviews, anomaly triage, compliance artifacts. Enforce access policies, provision/deprovision with logs, triage anomalies with evidence, and compile change/compliance reports automatically. Customer Success Buried risk signals, reactive outreach, missed expansion timing. Merge usage, billing, and support signals into health scores; create alerts; draft context-aware outreach; and assemble EBR/renewal packs.

AI automation implementation guide

To avoid the common pitfalls of failed AI rollouts, you need a structured approach. These are best practices we’ve distilled from our experience and leaders in the space to give you a clear guide to engineering success with AI automations.

1) Align your leadership

You cannot transform a business if its leaders don't understand the game. The first step is to get the entire leadership team on the same page with a clear, strategic vision for AI.

This is typically done through workshops that educate key decision-makers on core AI concepts, opportunities, and terminology. The goal is to shift their perspective from a traditional organizational chart to an "AI-first" model, where technology enhances human capability at every level. By establishing this shared understanding upfront, subsequent recommendations for specific projects become natural conclusions of a strategy they've already agreed upon, not a surprising new expense.

AI-First Scorecard (Org Readiness): Assess Adoption, Architecture, Capability across the org: Where will this live (systems, owners)? What data/services can it reuse (not rebuild)? What rituals exist for iteration (eval reviews, post-mortems)? Document gaps and assign owners before the pilot, so you don’t ship into a vacuum [4] . Pick your agentic maturity target (L1 → L3). Decide whether this initiative aims for Level 1 (AI workflow) , Level 2 (router workflow) , or Level 3 (autonomous agent) this quarter. Aligning on “how much decision-making we hand over” prevents scope creep and mismatched expectations [3] . Process mapping: Use a tool like Figma to visually map the company's core workflows based on the information gathered. For many companies, this is the first time they see a clear, objective map of how their business actually operates day-to-day, as most standard operating procedures (SOPs) are often outdated and ignored.

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Use case identification: This is where expertise separates the amateurs from the specialists. With a clear process map, you can identify bottlenecks, repetitive tasks, and areas clogged with manual work like data entry or report generation. These are the gold nuggets—the "quick wins" that can deliver immediate ROI and build momentum for broader AI adoption. Governance & transparency rules: Write down what will be logged, who can see it, how long it’s retained, and what end users will be told (citations, rationale, confidence). Establish a lightweight review council that meets monthly to approve promotions and review incidents [4] .

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2) Identify high-impact opportunities

With leadership aligned, the next phase is a deep dive into the business to find the best opportunities for automation. The goal is to understand the business better than the people who run it every day, including its flaws and inefficiencies.

In-depth interviews: Conduct detailed interviews with everyone, from department heads to the front-line employees who are in the trenches creating value. This uncovers the real-world challenges and workarounds that never make it into official documentation.

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3) Choosing the right tools for AI automation

The right platform can make all the difference. While there are many options, a few stand out for their power and ease of use. An ideal tool should offer a visual builder, developer-friendly features like SDKs, robust testing and versioning, and strong governance controls.

Problem & Outcome Framing: Before naming a tool, write a one-pager that states the business problem in plain language, the customer/employee it impacts, and the measurable outcome you’re after. This anchors every design choice and lets you kill work that doesn’t move the needle [4] . Tooling Fit & Integration Plan: Choose the platform that reinforces your shared plumbing (APIs, identity, data models) and supports evals, versioning, and audit logs. Outline exactly how it will integrate with existing systems, who owns each connector, and how you’ll monitor cost/latency in production [4] .

Here’s how to determine your ideal AI automation platform:

Criterion Why It Matters What Good Looks Like Questions to Ask Easy Building Faster time-to-value for both non-technical and technical teams. Clear visual builder, reusable blocks/templates, plus SDK/CLI for engineers. How quickly can we build a basic flow? Do non-engineers need code? Collaboration Keeps product, ops, and engineering aligned. Workspaces, roles, comments, reviews, and shared datasets/projects. How do multiple people work on the same flow without conflicts? Governance Reduces risk and supports compliance at scale. Role-based access, audit logs, data retention controls, policy guards. What’s logged by default, and who can see/change it? Observability Lets you debug, measure impact, and improve reliably. Run traces, replays, cost/latency dashboards, error categories. Can we replay a run and see each step, tool call, and token usage? Evaluations & Testing Prevents regressions as prompts/models change. Golden datasets, A/B testing, pass/fail thresholds, scheduled checks. How do we test changes before they go live? Versioning & Rollback Makes iteration safe and recoverable. Immutable versions, diffs, environments (dev→prod), one-click rollback. If something breaks, how fast can we revert? Integrations Connects automations to real business systems. Native connectors (DB/CRM/ticketing), easy custom actions, retries/backoff. Which systems are supported out-of-the-box? How do we add our own? Data Security Protects sensitive data and trust. Encryption, secrets management, private networking, compliance attestations. How is data stored, isolated, and accessed? Cost & Performance Controls Prevents surprise bills and slow user experiences. Budgets, rate limits, semantic caching, batch modes, autoscaling. How do we cap costs and spot outliers? Multi-Model Support Avoids lock-in and fits the best model to each task. Easy provider switching, per-step model choice, fallbacks/ensembles. Can we choose models per step and fail over automatically? Human-in-the-Loop Balances automation with review for edge cases. Approval steps, exception queues, annotation tools, SLAs. How do humans review/approve and how is that captured? Support & Ecosystem Shortens ramp-up and expands what’s possible. Templates, examples, partner network, docs, responsive support. What help is available on day one and as we scale?

4) Develop and deploy with the right tools

Once you've identified a high-impact use case, the focus shifts to building and implementing the solution. This is where technology finally enters the picture. The rise of low-code AI platforms has made this phase more accessible than ever, empowering both technical and non-technical teams to build robust automations.

Set bold automation goals: Many workflows are 70–80% rote. Say the quiet part out loud: aim to automate that portion, keep humans for the judgment calls, and measure the mix explicitly (automation rate, assisted rate, human-only rate) [5] .

As noted in Vellum's guide to low-code tools, these platforms act as the "connective tissue that makes SaaS, data, and AI models feel like one system." They bridge the gap between business users who understand the process and engineers who can ensure security and scalability.

Enable every team with AI automations on Vellum

Most platforms made for AI automations are either too complex and code intensive or too limited by simplicity, leaving teams stuck between choosing a solution that caters to their technical or non-technical users.

Vellum is purpose built to enable teams with a collaborative building space for all levels of AI automation complexity. Vellum’s Agent Builder makes it easy for anyone on the team to design, test, and deploy automations in one place with no code required.

Ready to build AI automations in Vellum?

Give every team the tools to automate their busywork with AI automations on Vellum!

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FAQs

1) What makes AI automation different from traditional workflow automation?

Traditional automation moves data; AI automation understands it. Instead of just triggering actions, AI can classify, summarize, and make next-step decisions — helping teams handle complex, judgment-based work faster and more consistently.

2) How do I know which processes are good candidates for AI automation?

Look for repetitive, high-volume workflows that rely on judgment calls but follow consistent logic. If your team spends hours reviewing, sorting, or summarizing data, chances are AI automation can take over 70–80% of that workload.

3) How long does it usually take to see ROI from AI automations?

Most teams see measurable time savings or cost reductions within one to two quarters. The key is to start with a small, high-impact workflow, prove the value, and expand from there instead of trying to automate everything at once.

4) Do AI automations replace human roles?

No — they remove the repetitive work that keeps people from focusing on higher-value tasks. The goal is augmentation , not replacement. Teams that pair AI automation with clear human oversight often see better accuracy and happier employees.

5) What data do I need before building an AI automation?

Clean, structured, and accessible data. Even small inconsistencies can limit accuracy. Start by reviewing where your team’s data lives, how it’s labeled, and whether it’s up to date — AI can’t automate chaos.

6) How should leadership prepare for adopting AI automations?

Start by aligning on outcomes, not technology. Leadership should understand what “being AI-native” means for the organization, define what success looks like, and make AI enablement part of every team’s roadmap — not just an IT initiative.

7) How do I choose the right AI automation platform?

Look for a platform that’s flexible enough for both technical and non-technical users, supports version control and testing, integrates with your existing systems, and keeps data secure. A good platform makes building and iterating automations simple for everyone.

8) How can I make sure my AI automations stay accurate over time?

Regularly review their outputs. Schedule recurring evaluations to check performance against benchmarks, gather user feedback, and retrain or refine prompts when results drift. Treat automations like products, they need ongoing care.

9) What’s the best way to introduce AI automations to my team?

Start small, show impact quickly, and celebrate wins. Let teams co-create early automations so they feel ownership, then document and share outcomes. That early momentum drives adoption across the organization.

10) Why use Vellum for AI automations?

Vellum gives every team, technical or not, the tools to design, test, and deploy AI automations in one collaborative space. It removes the friction of building in code-heavy environments and helps you scale from first prototype to production-ready workflows fast.

Citations

[1] MIT NANDA. (2025). The GenAI Divide: State of AI in Business 2025 .

[2] Atlassian. (2026). State of Product Report 2026 .

[3] Vellum AI. (2025). Agentic Workflows in 2025: The Ultimate Guide .

[4] Harvard Business School Online. (2024). Building an AI Business Strategy: A Beginner’s Guide .

[5] OpenAI. (2025). AI in the Enterprise: Lessons from Seven Frontier Companies .