Implementing AI in companies: why it’s still harder than it seems

In recent years, Artificial Intelligence (AI) has advanced at an impressive pace. Still, the gap between AI’s potential and the concrete results most companies achieve remains wide.

Organizations across industries—retail, financial services, construction, logistics, and B2B—recognize that AI can reduce costs, accelerate processes, and improve customer experience. Yet very few manage to turn those expectations into reality.

This article explains what can be automated today, what remains difficult, and how to move forward realistically, based on hundreds of implementations and dozens of conversations with operations, technology, and sales teams.

Why implementing AI is more complex than it seems

Many processes that should be automated are still done manually:

After-sales and support
Reconciliations and accounting processes
Operational reporting
Document review (contracts, invoices, purchase orders)
Internal request management

This isn’t due to lack of interest, but because practical AI implementation requires order, context, processes, and technical integration that many organizations still haven’t solved.

What you can automate today with AI

The technology available in 2025 enables high-accuracy automation across several task categories:

1. Content generation and transformation

Generative AI for:

Writing and translating text
Emails, internal reports, commercial messages
Image generation and operational content

Examples:

OpenAI for advanced content
Microsoft Copilot for internal documents
Canva AI for fast visual assets

2. Automated customer support (up to 80%)

Today it’s possible to resolve most inquiries across:

Email
Chat and webchat
WhatsApp
Phone (intelligent IVR with voice models)

Recommended platforms:

Vambe for WhatsApp
Intercom Fin AI for web support
Modern voice bots based on speech synthesis and recognition models

3. Building internal apps with little (or no) code

Models can generate:

Internal apps
Smart forms
Automated workflows

Examples:

Lovable for internal apps
Cursor for assisted development

4. Automatic processing of large volumes of data

AI can:

Review hundreds or thousands of documents in seconds
Detect patterns or anomalies
Analyze research or audit information

Best for:

Finance
Risk
Internal audit
Legal & compliance

What remains difficult in 2025

Even though AI is powerful, there are areas where automation is still complex:

1. Integrations across multiple systems

Technical work is still needed to connect APIs, scrapers, and heterogeneous files. These efforts require engineering, maintenance, and data governance.

2. Problems that depend on human judgment

It’s still hard to automate tasks involving:

Subjective decisions
Undocumented information
Tacit rules only the team understands

3. Processes without clear structure

If a process lacks:

A precise definition of success
Well-documented exceptions
Clear owners

…AI will only amplify the disorder.

4. Scaling automations in changing environments

Systems, roles, and data change frequently. Without governance, an automation that worked well today can break tomorrow.

How some companies are making progress

Organizations that are getting results tend to share a few patterns:

Build internal capabilities (process analysis, data governance, functional owners).
Formalize processes and define success criteria.
Organize data so AI operates on reliable information.
Implement modular, measurable automations, avoiding “giant projects”.
Create responsible teams for the full lifecycle of each automation.

It’s not instant, but it’s absolutely achievable when built on a solid foundation.

Ways to move forward today

1. For simple processes: no-code

Tools like:

Help you start quickly when workflows are simple and well-defined.

2. For industry-specific needs

Pre-built platforms aligned with business logic often work better:

Legal: Harvey
Development: Cursor
Internal apps: Lovable
Collections: Bulk
WhatsApp & support: Vambe
B2B sales: Recerc

These solutions include ready-to-use capabilities, integrations, specialized support, and implementation best practices.

3. For custom solutions

When a process is complex, requires proprietary logic, or integrates multiple systems, a bespoke build is necessary.

AI makes development faster, but you still need professionals who understand:

System architecture
Integrations
Security and maintenance
Data governance
Continuous operations

Conclusion

The opportunity to apply AI in companies is enormous. But turning potential into real impact requires order, processes, clean data, and a clear plan—not just a good demo.

The key is to move in stages, choose tools well, and first build the foundations before scaling.

Want to understand how to apply AI for real in your company?

At Recerc, we help B2B sales and operations teams implement AI in critical processes like quote generation, price validation, commercial automation, and more.

Learn more at: 👉 https://recerc.com

Or schedule a conversation with our team.