How businesses can save time and money with AI freelancers

The modern business landscape is undergoing a major transformation in operational models. Artificial intelligence is shifting from an experimental tool to a strategic solution. A new workforce – so-called “AI freelancers” or autonomous AI agents – can perform highly specialized tasks with a speed and scale unattainable for human labor. They deliver increased efficiency and profitability.

Market leaders have quickly realized that true economic value lies in process automation through agents. 70% of surveyed companies believe that implementing AI is “critical” or “very important” for achieving their strategic goals. 48% – identified AI adoption as a priority for the coming year.

The concept of the AI freelancer and key benefits

An AI freelancer is an autonomous or semi-autonomous software agent capable of performing specific, repetitive or high-volume tasks that previously required expensive human labor (such as a copywriter, junior designer, support operator or data analyst).

The main advantages of this new “workforce” include:

• Significant reduction in operational costs, especially in high-frequency routine tasks.

• Round-the-clock work, which is critical for global operations and lead processing.

• The ability to instantly handle virtually unlimited amounts of data or requests, delivering increased productivity.

AI-driven analysis identifies savings opportunities three to four times faster than traditional methods. For example, retail companies were able to uncover around 5% of hidden or “shadow” costs, particularly in logistics, warehousing and marketing operations. In addition, about 40% of expenses were reassessed. Total savings in the first year of AI adoption often exceed 10%.

Cost of human work vs machine work

A human-handled interaction costs an average of $6.00, while the average cost of an AI chatbot interaction is $0.50. For pay-per-use models, it can drop as low as $0.006 per interaction. This creates a twelve-fold cost difference.

For a large company processing 50 000 interactions per month, human support would cost roughly $300 000. An AI solution would cost on average only $25 000. This delivers savings of up to 91.7% on routine inquiries.

The case of Swedish fintech company Klarna proves that AI can generate direct profit. Klarna’s AI chatbot performs the work previously done by 700 people. The average resolution time decreased from 11 minutes to under 2 minutes. Customers receive more accurate and complete responses, reducing repeat inquiries by 25%. The system also enables global support 24/7, covering over 35 languages and 23 markets. Altogether, this contributed an estimated $40 million in annual profit for the company.

Although AI handles most routine requests, humans remain essential for complex, emotional or high-value interactions. For example, one analysis shows that by using an AI chatbot (costing about $15 000 per year) to process the majority of inquiries, a company still needs two human agents. But this is 63% more cost-effective compared to hiring five people.

Main areas of AI freelancer implementation

1. Automation of administrative routines and HR

AI effectively handles repetitive creative and administrative tasks. Oxa, a software developer for autonomous vehicles, uses GenAI to create reporting templates and to write and edit social media content.

AI freelancers are also transforming HR. They generate high-quality job descriptions, avoid vague or biased language, and help evaluate candidate resumes based on specific criteria.

2. GEO strategy

The evolution of search systems driven by generative AI (such as ChatGPT and Gemini) has changed the game. Users increasingly receive answers directly from AI assistants without visiting websites. This has created a new approach — Generative Engine Optimization (GEO), which is an evolution of SEO.

GEO focuses on creating content that AI systems will cite as an authoritative source. For businesses, this means that successful content must be so clear and credible that AI uses it as fact rather than merely as a traffic source.

Key principles of GEO include:

• Clear structure: use of headings, subheadings, FAQ blocks and step-by-step instructions

• Schema.org markup: implementing structured data (FAQ, HowTo) to support correct recognition by AI systems

Companies that optimize content for AI today will gain a competitive advantage and a new traffic channel.

Attention: Large-scale use of AI for content generation increases the risk of unintentional intellectual property infringement. To prevent this, businesses need to include a “human in the loop” to verify generated content.

3. Optimization in design and branding

AI freelancers deliver significant savings in design by instantly generating initial ideas and prototypes, reducing time and financial costs at the brainstorming stage.

AI-based tools like Picsart and Logo Diffusion enable the creation of logos, illustrations and branded creatives from scratch in seconds. More than 45 curated design styles are available to completely transform a brand book.

Instant and inexpensive creative generation frees designers from routine work. They can focus on strategic brand management, fine-tuning customizations (colors, themes) and ensuring uniqueness, which is critically important for large brands.

4. AI as an instant understanding tool

Artificial intelligence eliminates the need for deep technical knowledge to interpret data. For example, Tableau requires only a simple question to deliver an answer in an easy-to-understand format with a chart.

This is made possible by its Ask Data and Explain Data features. Ask Data responds to a simple question and uses Natural Language Processing to automatically generate a visualization. Explain Data acts as a personal analyst, instantly identifying reasons for changes in charts. If a user sees a spike or drop, AI automatically analyzes all data fields, builds a statistical model and proposes an explanation.

The link between reduced analysis time and financial growth is crucial. This is demonstrated by real-world cases from Bookimed and Toyota.

Data collection and analysis at Ukrainian company Bookimed used to take up to a week. After switching to automated dashboards with Tableau, the process shrank to two hours. Thanks to faster reaction to user behavior, the company achieved a steady ten percent monthly revenue increase.

At Toyota, implementing an AI platform allowed factory workers to deploy machine learning models and reduced over ten thousand hours of human labor per year. This significantly increased efficiency and productivity.

5. Reducing dependence on the IT department

Branch managers independently obtain the reports they need using AI. This reduces the burden on central analytics teams. IT specialists can then focus on strategic, high-profit projects.

What do we have?

Organizations are rethinking internal processes. Reports show that the greatest revenue gains from AI use occur in marketing and sales, as well as in strategy and finance. In other words, the strategic value of AI lies in accelerating high-profit functions, not just in reducing operational costs.

The implementation of AI freelancers may initially lead to workforce reductions in 30 percent of companies. However, it simultaneously catalyzes the emergence of new roles. Companies are creating demand for specialists who can manage machines and oversee their outputs. New professions are emerging, such as AI product managers, writers for AI evaluation, and Human-in-the-Loop validators. This requires investment in reskilling, transforming specialists into AI system managers.

7 steps for successful AI freelancer implementation

1. Start with ROI: Prioritize areas with the highest operational costs and fastest financial returns, such as customer support.

2. Ensure data quality: Invest in systems that provide real-time automatic data updates.

3. Intellectual property compliance: Hire people to review AI-generated content to minimize legal risks.

4. Invest in GEO: Refocus your content strategy on creating AI-friendly materials with clear structure and markup to gain a new channel of citations and traffic.

5. Train for new roles: Invest in reskilling staff, transforming them into prompt engineers, validators, and AI system managers.

6. Use a hybrid model: Combine AI speed for routine tasks with human intelligence for emotional, strategic, and complex problem-solving.

7. Transform the system: Don’t just attach AI to old, inefficient processes. Success requires redesigning the work.