10 Steps To Consider Adopting AI In Your Business

Artificial Intelligence has firmly entered our lives. It helps us with our small daily tasks like finding the best route or choosing a movie for the evening; and globally changing our reality as in the case of leveraging AI for clinical research. 

At the same time, many small and medium businesses are still looking at the possibility of applying AI within their organization. In this article, we’ll explore how to get started with your own in-house AI approach, and the 10 steps you need to take on your way to deploying your first AI project.

What AI Is and What It Is Not

If you are at the very beginning of the journey and Artificial Intelligence is more a popular term for you than an understandable technology – start from step zero – get a general idea of ​​​​how AI works and how it can be applied in your business. This will save you from unreasonable expectations and help clearly formulate your goals and objectives with AI. 

Study who among your competitors is already successfully using AI and what opportunities the technology gives them – this may be a good lesson on what to do or what not to do. 

Is AI Adoption Different in 2026 Than It Was Before?

Yes. It used to mean building and training a machine learning model — a process that could eat months and required a specialized data science team. Now it usually means calling an existing foundation model through an API and putting your engineering effort into integration, retrieval, and governance instead of model training. A pilot can go from idea to production in weeks. The bottleneck moved from data prep to governance sign-off.

What Is AI, and What Isn’t It, in 2026?

In a business context, AI now means foundation models like Claude: something you prompt, connect to your own data through retrieval, and increasingly deploy as an agent that plans and takes multi-step action. It is not, for most companies, a model you build yourself. Custom training still happens, but it’s the exception now, reserved for narrow, high-value problems nobody else has already solved.

Most organizations already use AI somewhere in the business — adoption is close to universal, per McKinsey’s State of AI research. Far fewer have gotten past the pilot stage. “We use AI” stopped being a differentiator a while ago. Showing measurable value from it still is.

How Does AI Adoption Benefit a Business?

• Employee productivity. People get an agent that can draft, research, and carry out multi-step work with a human still checking it.
• Lower cost. Manual systems and legacy SaaS subscriptions get replaced with AI-native workflows built around your own process (see SaaS Exit).
• Fewer human errors in calculations, document review, and compliance checks.
• Better marketing and customer experience through personalization and visibility inside AI answer engines (more on AEO/GEO below).
• Sharper strategic planning, grounded in your company’s own data instead of guesswork.

What Are the Common Challenges With AI Implementation in 2026?

Has AI Adoption Become Cheaper or More Expensive?

Cheaper to start, but the money moves around. Training a custom model needed expensive, specialized hardware. Most businesses in 2026 skip that entirely — they call a foundation model through an API and pay per use. The cost driver has shifted from infrastructure to engineering discipline: integration, governance, monitoring, picking the right model tier for the job. Overspending on AI adoption today usually just means running an expensive model on a task a cheaper one could’ve handled.

Do I Still Need a Large Dataset for AI Adoption?

No, not for most use cases. Retrieval plus a well-built prompt now covers most of what a large labeled dataset used to. What replaced the data problem is a trust problem: 51% of organizations report at least one negative consequence from using AI, most often inaccurate output (200OK Solutions, citing McKinsey’s State of AI survey). Governance — access control, evaluation, human review — is what decides whether a pilot ever gets near production data.

Why Do Agentic AI Projects Fail?

Usually from missing governance, not weak models. Agents that plan, call tools, and act on their own are mainstream now, but running them reliably at scale is still rare. Analysts estimate only a minority of enterprise AI deployments qualify as true agents with planning and adaptive behavior, and a large share of agentic pilots are expected to get shelved before reaching production (200OK Solutions). Most AI budgets right now are stuck in the gap between “we tried an agent” and “we run agents in production.”

Custom AI Model vs. Foundation Model Adoption

How to Start AI Implementation in Your Business

1. Define your goals with AI

It may sound obvious, but setting clear goals at the start is critical to the success of your project. Consider what problems or areas for improvement exist in your organization that could benefit from AI. How can AI capabilities complement your existing products and services? Describe specific use cases with a measurable outcome attached — what could an AI system, not necessarily one you build yourself, realistically fix in your organization. “We should use AI for X” isn’t a goal. “Cut support ticket resolution time by 30%” is.

2. Prioritize and start small

The old advice still holds: don’t try to boil the ocean. In 2026 that means shipping one narrow, well-governed agent into a real business process, not running a pilot that lives in a slide deck and never touches production data. A simple 2×2 — value against feasibility — still works for picking where to start:

3. Plan Realistic Results, Budget, and Time for AI Adoption

Results. As you move towards implementing the detailed plan, go back to your initial expectations. Make sure they are realistic and achievable. Keep in mind that your AI model will learn and improve. All stakeholders should be prepared to accept a range of results with 60%-99% accuracy while the model is training. Foundation-model AI adoption skips months of training, but it still needs iteration — prompt refinement, retrieval tuning, evaluation cycles. Expect accuracy to wobble while you tune it. Build the evaluation loop in from day one (see Evaluation Tool).

Budget. Costs now cover model API usage, integration and agent-orchestration engineering, governance tooling, and ongoing monitoring. Mixing model tiers — cheap models for simple tasks, frontier models for the hard reasoning — is a standard way to keep the bill down.

• Include internal headcount, contract resources, and IT infrastructure (including licenses for applications and cloud resources) to calculate your total budget
• Review the cost of data collection (from internal or external sources) as part of the overall budget
• Calculate the predicted value for your business from an AI project over a period of 12 to 36 months

Time. A foundation-model pilot can move from prototype to production in weeks, not the 3 to 36 months a custom ML project used to take.

4. Define Your Internal Capability Gaps

Look honestly at what your organization can actually do today, technically and organizationally, before committing budget. The roles that matter most now are prompt and agent design, AI governance, and systems integration, alongside whatever classical data science team you already have.

5. Prepare Your Data for Retrieval, Not Just Training

AI adoption still runs on data, but the requirement has changed. The question isn’t whether you have enough labeled data to train a model anymore — it’s whether your data is structured and accessible enough for retrieval, and safe enough for an agent to act on. Access control matters more here than raw volume. Additionally, you may need to purchase or enable external data sources. Keep in mind that different data sources and data types will have different data governance and privacy controls that you’ll need to meet.

Be sure to include storage in your plan. As your AI initiatives evolve, you will need to store more and more data. Incorporating a fast, optimized data warehouse into your roadmap in the early stages will help you avoid the hassle of scaling.

6. Close the Domain Expertise and Skills Gap

You still need engineers who know your domain, but the hiring mix has shifted toward agent-workflow design, evaluating model output, and integration — often through protocols like Anthropic’s Model Context Protocol (MCP), now the de facto standard for connecting AI systems to business tools and data. A good AI adoption partner can close this gap faster than a hiring cycle (see AI-Accelerated Engineering).

7. Prepare Your IT Infrastructure and Governance

Decide whether this happens in-house, through an outsourcing partner, or through an off-the-shelf agent that plugs into your existing systems. Governance belongs here, not bolted on afterward: credential isolation, prompt-injection defense, and compliance packs for HIPAA, GDPR, and similar standards need to be designed in before any agent is allowed to act.

8: Implement AI Adoption With Continuous Evaluation

No AI system is perfect on day one. In agentic AI, “training” mostly means continuous evaluation — watching what the agent decides, catching drift, feeding corrections back into prompts, tools, or retrieval sources. Your vendors should give you the ability to fully manage the lifecycle of the AI model so that it can take feedback, learn from it, and analyze errors. A partner worth paying gives you visibility into the whole lifecycle, not a black box.

9: Make Your Business Visible to the AI Systems Your Customers Use

Zero-click search is the default now, and a growing share of buyer research happens inside AI Overviews, ChatGPT, and other answer engines rather than a results page (CXL, Answer Engine Optimization guide). Content that isn’t structured for citation is invisible there, no matter how well it ranks in traditional search. Answer Engine Optimization (AEO) and Generative Engine Optimization (GEO) have become their own discipline in 2026, built on top of SEO rather than replacing it (see AEO/GEO services).

10. Embed AI Adoption in Your Business Culture

AI agents now handle work that used to need a human at every step. Decide on purpose what your team does with the time that frees up — new customer-facing work, deeper analysis, expansion — instead of leaving the question hanging.

Analyze the expected results and their impact on your business. What are your next steps once the AI solution is successfully adopted? How do you use the freed up resources or apply knowledge about new opportunities? In order to get the most out of AI, you must be prepared to make adjustments to the business culture and workflow of your employees.

An Analogy for the AI Adoption Journey

Christopher Nolan’s The Odyssey, with Matt Damon as Odysseus, is about a man who knows exactly where home is and still needs ten years, a crew, and a string of near-fatal detours to get there. It maps onto AI adoption better than it probably should.

Everyone can picture the destination — a process that saves time and money. Almost nobody gets there on the first try. McKinsey’s numbers explain the gap: nearly every organization has started AI adoption, and only a sliver have scaled it into something that actually moves the P&L.

The Sirens are the AI hype cycle. Odysseus survives them by planning for the temptation before he ever hears the song. A company that skips Steps 1 through 4 and deploys an unsupervised agent because a demo looked impressive is, in effect, untying itself from the mast mid-voyage.

Scylla and Charybdis is the governance trade-off. Lean too hard toward speed and you risk the inaccurate, unmonitored output that most organizations have already run into. Lean too hard toward review and the agent never leaves the harbor. Steps 7 and 8 exist so that choice gets made on purpose, not by accident.

Odysseus gets home because he treats the trip as a series of specific, survivable problems instead of one leap of faith. That’s the whole argument of this guide.

First Line Software Is an Anthropic Select Partner for AI Adoption

First Line Software is a Select Partner in the Anthropic Claude Partner Network — a title Anthropic gives to partners with a track record of running Claude-based AI adoption projects reliably in production. Our agent orchestration runs on the Claude Agent SDK and Model Context Protocol, mixing model tiers across Opus, Sonnet, and Haiku, with governance, credential isolation, prompt-injection defense, and compliance packs for HIPAA and GDPR built into every deployment before an agent is allowed to act.

Practically, that means the infrastructure question in Step 7 and the governance question in Step 8 are already solved at scale — across Real Estate, Healthcare, Hospitality, and Digital Experience deployments.

FAQ: AI Adoption in 2026

Do I need my own AI model to start AI adoption?

No. Most businesses adopt AI in 2026 by plugging into an existing foundation model like Claude — prompting, retrieval, an API key. Custom training is for the narrow, high-value cases where nothing off-the-shelf does the job.

How long does AI adoption take in 2026?

A well-scoped pilot on an existing foundation model can go from concept to production in a few weeks. If it’s stretching into months, that’s usually integration and governance complexity, not model training time.

What is the biggest risk in AI adoption right now?

Scaling an agent past the pilot stage without governance in place. A good chunk of agentic AI projects launched without clear ownership and evaluation get shelved within a year or two.

What is AEO/GEO, and why does it matter for AI adoption?

It’s the practice of structuring content so AI systems — Google AI Overviews, ChatGPT, Claude, Perplexity — cite it directly in their answers. As more buyer research happens inside those surfaces, getting cited matters as much as ranking used to.

What does an Anthropic Select Partner do differently in AI adoption? It means a track record of running Claude-based systems in production, with governance, credential isolation, and compliance built in — not just advisory work.

What’s next?

Implementing the first AI project in your organization can be a complex and challenging task. But the further you go the more familiar you’ll become with the technology, the more value you’ll get for your business – the more you’ll be convinced of the amazing possibilities that AI has for business development and creating competitive advantages. We still often think of AI as something from the future, but in fact –  AI is today. It is already everywhere. Don’t let the Fourth Industrial Revolution pass you by…

AI adoption in 2026 is less about building a model and more about integrating one safely and making it visible — to your own team, and to the AI systems your customers already use. The fundamentals haven’t changed: clear goals, a staged rollout, honest budgeting, a real culture shift. What’s different is how fast you can move once those fundamentals are actually in place.

See it in production in our live AI Lab, or start a conversation with our AI engineering team.

Don’t let the Fourth Industrial Revolution pass you by…

Let’s talk!

Our team of highly qualified engineers has extensive experience in working with artificial intelligence projects. If you want to go through these ten steps with confidence, we can become a reliable trusted partner along the way. Start a conversation and get advice from our AI & ML experts today to build a new future for your business tomorrow!

Glossary

• Foundation model. A large, pre-trained AI model, such as Claude, that businesses adapt through prompting and retrieval instead of training from scratch.
• AI agent. A system that plans, calls tools, and takes multi-step action toward a goal, with varying degrees of human oversight.
• Retrieval-Augmented Generation (RAG). A method that connects an AI model to your own data at query time instead of retraining the model on that data.
• Model Context Protocol (MCP). An open standard, introduced by Anthropic, for connecting AI systems to external tools and data sources.
• AEO / GEO. Answer Engine Optimization and Generative Engine Optimization: structuring content so AI systems cite it directly in generated answers.
• Hallucination. An incorrect AI-generated output presented as fact.

Updated: August 2026