How to use AI in your business the smart way, not the hype way

Manelik Sfez
Jun 24
5 min read

Updated: 12 hours ago

AI is everywhere, or so it seems. Every SaaS platform has launched a copilot. Every pitch deck includes a slide about machine learning. And yet, most companies that deploy AI aren’t seeing meaningful returns. According to McKinsey, while nearly 80% of businesses have implemented some form of generative AI, a similar number report almost no measurable impact on earnings.

This is what McKinsey calls the "GenAI paradox": massive adoption, minimal effect.

Their conclusion? AI isn’t the problem. The workflows are. To see real results, they say, companies must stop layering AI tools onto legacy systems and start redesigning operations from the ground up around a new kind of AI: agents.

That argument makes sense. But it misses something just as critical: most agents aren’t ready either.

At Ultrabrand, we believe the real opportunity today lies in rethinking how your business works. Not just automating repetitive tasks. Not just testing chatbots. But redesigning key workflows to integrate smart automation, and preparing for agent-led roles when (and only when) they actually make sense.

The GenAI Paradox: why AI isn’t paying off

The data is clear. AI use has skyrocketed, with companies embracing everything from ChatGPT to Microsoft Copilot to AI-powered ticketing assistants. But when McKinsey asked leaders whether AI is improving their bottom line, the answers were mostly disappointing. The issue isn’t that AI doesn’t work. It’s that it’s being used in the wrong way.

In most cases, companies are deploying general-purpose tools, known as horizontal AI, that may help individuals write emails faster or summarize documents. But these gains are diffuse. They don't transform the way a company operates. So while employees may spend slightly less time formatting slides, the company still operates at the same velocity.

Automating a broken system doesn’t make it smart

Adding AI to a flawed process is like adding a motor to a broken bicycle. It might move, but it won’t go far. Take customer service. Most AI deployments here involve chatbots that surface knowledge base articles or draft replies. But if the backend systems are slow, fragmented, or require human approval at every step, the automation is just lipstick. That’s what McKinsey means: to get ROI from AI, you have to rethink the system.

What AI agents are supposed to do (and what they actually do)

McKinsey’s preferred solution? AI agents.

Unlike copilots that support humans in specific tasks, agents are designed to operate more independently. They plan, execute, and adapt. In theory, they can own entire workflows.

Take a credit-risk memo process in a retail bank. Before agents, a relationship manager might manually collect data, extract reports, chase missing information, analyze creditworthiness, and submit a final recommendation, which is a process taking two to four days per case.

With an agent in place, all those steps are completed autonomously. The human simply reviews the result. The outcome? According to McKinsey, 20–60% productivity gains and 30% faster decision cycles.

Copilots assist. Agents act. Humans still verify.

In practice, though, very few agents live up to that promise.

The problem isn’t just technical. It’s contextual. Agents today still require narrowly defined instructions, reliable data, and clearly scoped environments. Most businesses don’t have that level of structure. So agents often get stuck, fail silently, or require human correction.

That doesn’t mean they’re useless. It means they’re early. Expecting them to transform your business overnight is like hiring interns and expecting executives.

Graph: A retail bank used AI agents to reinvent the process of creating credit-risk memos (source: McKinsey)

How to use AI in business: when agents aren’t fully there yet

The smart move today isn’t to chase agents. It’s to redesign your operations to be agent-ready, even if you start with simpler automation.

That means identifying workflows that are manual, repetitive, and spread across multiple systems. Then rethinking how those workflows should work, and not just speeding up what’s already inefficient.

Here’s a practical flow:

Map your workflows

Where are people losing time? Where are decisions delayed?

Define the outcome

What would an ideal process look like without constraints?

Redesign the system

Remove unnecessary steps, reduce handoffs, simplify approval layers.

Automate first

Use reliable integrations (Wix, Make, Zapier, CRM workflows) to automate steps.

Test agents

Where patterns are stable, try agents. But keep humans in the loop.

Redesign first. Automate second. Then think about agents.

Graph: Example gen Al use cases across the enterprise and specific functions (source: McKinsey)

For example, if your sales qualification process spans web forms, manual email sorting, spreadsheet tagging, and Slack follow-ups, don’t plug in an AI assistant. Redesign the flow. Connect the form to your CRM. Auto-tag leads by criteria. Trigger follow-ups. Then, and only then, test an AI agent to handle common replies.

The real ROI comes from intelligent operations

Business value doesn’t come from tools. It comes from how you use them.

A team that coordinates seamlessly through well-defined workflows will always outperform a team that’s manually stitching together AI hacks.

A few real examples:

A consultancy that built a Zapier-based lead scoring engine combined with AI-drafted outreach increased their close rate by 40%.
A SaaS company that restructured customer support into tiers, using AI for level-1 filtering and automation for routing, cut response times in half.
A luxury retail business that automated inventory and returns workflows avoided hiring three new employees.

None of these relied on fully autonomous agents. They relied on intelligent process design, and, this is how to use AI in business for what it is today.

Ultrabrand’s take: where we stand between the hype and the reality

At Ultrabrand, we don’t approach AI as a magic bullet. We see it as part of a broader operating system for modern businesses.

What McKinsey gets right is that companies need to redesign how they work. What they understate is how early we still are. Most businesses aren’t ready to deploy agents, not because they’re behind, but because the agents themselves are still evolving.

That’s why we focus first on intelligent automation. That means designing workflows that eliminate unnecessary friction, connect tools, and mirror how a healthy, scalable business should function. In many cases, these workflows already deliver massive ROI without any AI at all.

Then, when it makes sense, when the use case is stable, when the inputs predictable, and when the cost-benefit clear, we explore how agents can play a role.

It’s not about resisting AI. It’s about using it wisely. In systems that are designed to evolve. Where agents are one layer, not the foundation.

Our recommendation is therefore: stop plugging in AI, start rebuilding intelligently.

Don’t be distracted by shiny copilots. And don’t wait for agents to mature while your competitors rethink their operations. The companies that win with AI won’t be the ones that used the most tools. They’ll be the ones that built the best systems.

So if you want to use AI in your business... start by fixing your business. And then build forward.

About the author

Manelik Sfez, founder of the brand and digital transformation consultancy Ultrabrand, brings 25 years of international business, marketing, and brand strategy experience to the table. He has worked with some of the world’s most iconic brands throughout his career. From luxury goods to global retail, financial services and technological and industry giants, he has guided companies through brand-led transformations that have enabled significant business growth.