AI is reshaping industries as it can analyze vast datasets, automate tasks, and make predictions that were once impossible. AI offers unique solutions that traditional methods can't provide. According to Forbes, 60% of businesses, from healthcare to finance, believe AI can help them serve customers much better and improve company productivity. With such AI business opportunities, knowing how to start an AI business can help new startups thrive.

For those looking to enter this field, creating a solid business plan can be essential for thriving in the competitive AI landscape. Let’s elaborate on how to start an AI company and carve out your market niche to stay ahead of the competition.

How to Create an AI Startup: 5 Steps

Here is the foundation of a smart artificial intelligence business plan.

Step 1. Evaluate AI Readiness for Your Business

Before delving into startup building for an AI business, assess your business’s readiness. This can be done by auditing resources, assessing data quality, and internal expertise. You'll be able to identify the gaps, if any, and effectively allocate resources. Determine if you have access to high-quality datasets relevant to your solution.

Additionally, check if your team is familiar with AI development tools and methodologies, and if any infrastructure is needed to support further development. At this stage, you'll understand if you can scale with resources you have, or you will need to find more specialists. With a clear understanding of your readiness, set realistic timelines and mitigate challenges upfront.

Step 2. Research Before Starting an AI Company

To start an AI business, define your AI strategy. Gartner suggests that you should start with the business output and work backward. Identify the specific challenges or opportunities AI can address within your industry. This step sets the foundation for the tech stack, data sources, and roles you'll need on your team.

Here are some popular AI startup models to choose from, yet you may come up with an industry breakthrough on your own, who knows 😉

• Develop AI tools for specific needs, like better medical image analysis for finding diseases early or solutions for banks to spot fraud in transactions quickly.

• Offer AI as a Service (AIaaS) with ready-made APIs. Such user-friendly and customizable services, like Microsoft Azure AI or Google Cloud AI, help with image and video analysis, natural language processing, and decision-making.

• Start an AI consulting business, by providing expertise to other businesses.

• Provide data labelling services for training AI models. Precise data makes better AI. More and more businesses use AI worldwide, so demand for Data Annotators is growing.

• Advance AI through research and innovation.

• Design and manufacture specialized AI hardware.

• Create courses or platforms for AI skill development.

• Address AI ethics, fairness, and regulatory compliance.

Step 3. Secure Funding

How to build an AI startup without significant upfront costs? The good news is that AI startup companies can begin by using open-source AI frameworks, libraries, and tools such as TensorFlow, PyTorch, and Scikit-learn.

AI startups typically seek money from grants, research funds, and AI competitions. To grow faster, they attract investors like venture capital firms, angel investors, corporate venture arms, and crowdfunding platforms.

Regardless of your funding sources, to become a tech unicorn one day, you need to:

• Make a good pitch deck showing your startup's idea, team, and future plans.

• Customize your pitch to match what investors want, highlighting the ROI they can expect.

• Build a prototype or an MVP to show how your AI works.

• Partner with others in the industry and with customers to share the experience and grow together.

Step 4. Build Scalable Infrastructure

Scaling an AI startup effectively hinges on establishing a robust infrastructure from the outset. Growing AI companies benefit greatly from a cloud-based setup, as platforms like AWS, Google Cloud, or Azure offer flexible and scalable resources. With cloud platforms, you can manage services for data storage, compute power, and AI-specific tools. Such set-up will also save you from large capital investments in physical infrastructure.

Beyond cloud resources, you should consider integrating data management and model tracking tools. For example, MLflow or Databricks can be an option, helping you streamline data workflows and track experiments. If you plan for scalability in advance, it will minimize your technical debt and your teams will be ready to handle increasing data loads and user demands when your business expands.

Step 5. Recruit Top Talent

A successful artificial intelligence startup requires more than just a brilliant idea. Startup AI companies need skilled teams to bring those ideas to life. However, with demand high, AI engineer salary expectations have risen, so you’ll need to offer competitive compensation to attract top-tier talent.

The global talent market offers twice fewer AI experts than businesses require. You need to put effort if you want to hire AI developers who match. Here are the proven ways how to start an AI company with the right specialists on board.

Networking

Attend AI conferences, workshops, and meetups to communicate with potential team members. Partner with universities and research institutions to access emerging talent in the field of AI. You can also find AI developers’ profiles on online platforms like LinkedIn.

AI for Recruitment

Ironically, AI can help you find AI talent. Modern AI-driven tools allow you to analyze plenty of big data in tech recruitment, so you can say “Bye!” to bias and identify potential candidates who will be successful within your team roles in the long run.

Outstaffing

Beyond struggling with talent shortages, starting an artificial intelligence startup can be time-consuming and resource-intensive. You can partner with outstaffing companies to flexibly scale your tech team when needed.

This approach can help you access the expertise you require without the distraction from your core operations. HR professionals from outstaffing agencies can gather your project requirements, and get you suitable candidates, so you just have to decide who will get your job offer.

“Building an AI startup team requires a mix of technical expertise, adaptability, and problem-solving abilities. Diverse backgrounds bring fresh perspectives, and fostering ownership and purpose keeps teams motivated.”

Runbo Li Co-founder & CEO at Magic Hour

How to Identify Key Roles for AI Startup Companies

We are to explore the essential roles to consider when starting an AI company.

To start an AI business, we’d engage representatives of 3 Archetypes: businessmen, ideologists, and engineers.

When your startup is growing, the number of people (and departments) belonging to each of the archetypes should grow too.

1. Ideologists

Let’s see who shapes the “face” of an AI startup.

Founder(s)

The visionary individuals who conceptualize the startup and drive its mission.

IT Marketers

These specialists are responsible for promoting your startup and creating a positive brand image within potential clients.

AI Ethicists

As AI becomes more pervasive, ethical considerations become increasingly important. AI ethicists help your team make responsible decisions and avoid biases in AI systems.

2. Businessmen

They are responsible for the commercial results of the venture.

Product Developers

Someone who can turn ideas into tangible products.

Salespeople

Essential for generating revenue and securing customers.

AI Project Managers

Project managers ensure that AI projects stay on track, with on-time delivery, within budget, and in accordance to business goals.

3. Engineers

How to start an AI company without engineers? Im-po-ssi-ble. Without a tech team, all other specialists involved in the AI startup will have nothing to invest in, promote, and benefit from. The IT team structure can vary depending on your startup model. Below, you can see the list of the most in-demand specialists.

Data Scientists

They build machine learning models, analyze data trends, and refine algorithms.

Data Analysts

Data analysts are essential for extracting valuable insights from data.

Machine Learning Engineers

These engineers create software able to leverage data science by developing machine learning models and deploying them in production systems.

Prompt Engineers

Prompt engineering makes AI systems more human-like. It is all about creating prompts to improve AI algorithms.

Data Engineers

Data engineers create the infrastructure needed to collect, store, and manage data. They make data accessible and usable for AI applications.

Domain Experts

Domain experts possess industry-specific knowledge that helps AI teams understand the context and nuances of the mission the AI system should cope with.

How to Gather a Professional, Collaborative, and Diverse AI Team

We’d start by examining hard skills.

• A degree in computer science, machine learning, or related fields from a reputable institution show that candidates have foundational knowledge.

• The best specialists mostly have worked on relevant AI projects within relevant industries. They can share portfolios or code samples and references from previous managers or clients.

• Certifications in AI tools, such as TensorFlow or PyTorch, confirm a developer’s specific expertise.

• Research papers or open-source work in AI are a sign of huge interest in this niche.

• In tech interviews, good specialists fluently explain AI concepts and practical AI solutions.

With a diverse AI startup team, it is easier to offer unrivaled (feature-rich and bug-free) solutions to the market. So, we’d also consider various backgrounds, and skill sets when starting an AI company.

To level up cross-functional communication between data scientists, engineers, and domain experts:

• Look for candidates with a similar approach to business ethics;

• Make them aware of each other’s duties;

• Organize memorable team-building activities.

How to Maintain a Forward-Looking Approach

Set Clear Goals and Metrics

This is how to create an AI startup able to deliver commercial results, sticking to key growth metrics while bringing your AI initiatives to life. Using OKR examples for software engineers can be a practical way to align the team with goals, such as improving algorithm accuracy or optimizing deployment times.

• Determine a Customer Acquisition Cost (CAC) and Lifetime Value (CLTV). A high CLTV relative to CAC suggests a profitable customer acquisition strategy.

• Analyze the startup's Revenue Growth over time.

• Measure a Churn Rate — how many customers stop using the product or service within a certain time frame. A high Churn Rate should be a stimulus to improve customer satisfaction.

• Track DAU and MAU (daily and monthly active users).

• Understand the startup's long-term goals, whether it aims for an IPO, acquisition, or other exit strategies.

To keep your AI team involved, you or your project manager can discuss those metrics with them. Also, we’d recommend discussing the startup's goals and milestones reached, such as partnerships, new investors, awards, or notable customer acquisitions.

Starting an AI startup, be ready to balance your ambition and your budget. Here are some metrics that can help you prepare your company budget for the necessary changes in your AI company team structure.

• Evaluate Burn Rate. It’s how quickly the startup is using its cash reserves.

• Assess your Runway, answering how long the startup can operate with its cash reserves without additional funding.

• Calculate the difference between revenue and the cost of goods sold (COGS). A healthy gross margin indicates the startup can cover operating expenses and invest in growth.

Consider starting an artificial intelligence startup with an international team

The global talent market can offer you more unique skill sets and diverse hourly rates. You can find experienced AI developers for hire, for instance, in Eastern Europe. Despite the target country, you have to establish a legal entity there to start hiring locals. This can result in an additional tax burden, which can be unsuitable for startups with their tight budgets. Then, any tech business may look for some EOR services provider. This is a company which has the right to hire globally. Typically, such companies hire developers for your project and handle all the HR ops, so that you don’t have to worry about establishing relationships with local authorities.

Embrace Agility and Invest in Continuous Learning

Market conditions and users’ preferences are unstable. That’s why, sticking to an agile team structure in software development can benefit AI projects. Regularly assess your team's progress and make necessary adjustments to optimize your AI solutions based on real-world usage and changing needs. It would be easier to arrange if you collect and analyze customer feedback, reviews, and Net Promoter Scores (NPS).

To address that, engage specialists who are willing to stay updated with the latest research in AI.

As you can see, starting an AI startup is a multi-faceted process that requires careful planning, recruiting top talent, and innovation. To position your AI startup for success in a competitive landscape, building the ideal team must be ongoing. IT staffing services from Outstaff Your Team can be just up to the point if you want to:

• Stay responsive to market change and upgrade your team with pros well-versed in the latest trends;

• Get a professional strategy to retain top talent, which is a key to long-term success in the field of AI.

FAQ

by Kateryna Balakyreva
on November 22, 2024. Share on LinkedIn Post on Twitter

Kateryna joined the IT industry 3 years ago. Reviewing B2B software and related frameworks, she concluded that the best-in-class programs need well-built teams and started to write about tech teams scaling. Her natural habit to improve texts and search for alternative visions comes from working at the publishing house in early youth.