Don't Miss the AI Train:

Transform Work Now or Get Left Behind

 

I watched a senior financial analyst at a Fortune 500 company spend six hours reconciling currency positions - work an AI could complete in minutes. His frustration was palpable. "I went to school for strategic financial analysis, not copying numbers between systems," he told me. This wasn't just about wasted time. It was about wasted human potential.

After two decades consulting with multinational corporations on treasury and financial risk management, I've seen this scenario play out countless times. Talented professionals reduced to glorified data entry clerks. It's why my co-founder Juliano and I started Brisken - to revolutionize the business workspace and make users happy again.

The conversation around enterprise AI typically focuses on efficiency gains. Cost savings. Productivity improvements. Error reduction. All valid benefits, but they miss something essential: AI's capacity to transform the human experience of work itself.

Done right, enterprise AI doesn't just make companies more efficient. It makes employees happier.

Why Most AI Implementations Fail People

Most enterprise AI implementations focus exclusively on operational metrics. How many FTEs can we eliminate? How much can we reduce processing time? What cost savings will we achieve?

These are important questions. But they're incomplete.

When organizations implement AI solely to reduce headcount or cut costs, they miss the transformative potential of AI to enhance the human experience of work. They create resistance rather than enthusiasm. Fear rather than engagement.

I've watched companies invest millions in AI initiatives only to see them fail because they never addressed a fundamental question: How will this technology change the daily experience of our employees?

The most successful AI implementations approach this differently. They start with the human experience and work backward.

Start With The Tasks People Hate

In treasury operations, this often includes reconciliations, payment processing, and compliance reporting. In customer service, it includes repetitive ticket handling and basic inquiry responses. In sales, it's data entry into CRM systems and routine follow-ups.

The first best practice for enterprise AI implementation: Identify and automate the tasks your employees actively dislike.

When implementing a Digital Coworker solution, companies typically begin by interviewing team members about which tasks they find most tedious and unfulfilling. The answers are often strikingly consistent across departments - data extraction, manual input, reconciliation, and basic report generation frequently top the list.

Organizations that focus initial automation efforts in these areas often see more than just efficiency gains. Many report a fundamental shift in employee sentiment. When the tasks people dislike are removed, employees suddenly have time for the work they value.

Build Intelligence At The Interface

Enterprise systems weren't designed for humans. They were designed for processes. These systems prioritize workflow efficiency and data management over user experience, often resulting in complex interfaces that serve organizational needs rather than individual usability. The rigid architecture of enterprise systems reflects their primary purpose: to standardize operations, enforce compliance, and maintain consistency across large organizations. While technically powerful, they frequently lack intuitive design elements that would make them more accessible to everyday users. This fundamental tension—between process optimization and human-centered design—creates challenges for organizations attempting to balance operational requirements with employee satisfaction and productivity.

SAP, Oracle, and other major systems organize information to match organizational structures and data flows. This makes perfect sense for the system. It makes almost no sense for the user.

The second best practice: Build intelligence at the interface between humans and systems.

This is where our OnePilot framework creates transformative change. By providing an AI-powered interface that sits between users and enterprise systems, we allow humans to interact in natural ways while the technology handles the complexity.

Employees can ask questions, issue commands, or make requests in natural language. The Digital Coworker translates these into the technical operations required by underlying systems.

A treasury analyst can simply ask, "What's our current EUR exposure?" without navigating through multiple screens and reports. A customer service representative can say, "Update this customer's shipping address and resend the order confirmation" without touching multiple systems.

People remain engaged and loyal when technology is designed to enhance their experience and meet their needs, rather than creating frustrating barriers or unnecessary complexity that demands excessive adaptation from users. Effective technology integrates seamlessly into daily life, solving real problems without introducing new ones, thereby establishing a relationship where humans maintain control rather than feeling controlled by their digital tools.

Cross-Functional Process Automation

Most enterprise processes cross multiple departments, systems, and data sources. Purchase-to-pay spans procurement, accounts payable, and treasury. Order-to-cash touches sales, fulfillment, accounts receivable, and collections.

These cross-functional processes create particular frustration because no single employee owns the entire process. People become stuck waiting for handoffs and approvals, often with limited visibility into where things stand.

The third best practice: Automate cross-functional processes end-to-end.

Our agentic AI framework within the OnePilot platform allows Digital Coworkers to manage these complex processes across system boundaries. They handle the coordination, follow-ups, and routine decisions while keeping humans informed and involved where judgment is required.

Just as one of many examples, this approach significantly reduces month-end closing time, allowing finance teams to complete their processes more efficiently. Finance personnel can enjoy improved work-life balance by avoiding weekend work during traditionally stressful month-end periods. 

That's the kind of transformation that changes how people feel about their jobs.

Create Digital Teammates, Not Tools

The language we use shapes how people perceive technology. When we talk about "AI tools" or "automation solutions," we frame technology as something separate from the human team.

The fourth best practice: Position AI as digital teammates rather than tools.

This isn't mere semantics. It fundamentally changes how employees relate to the technology.

When we implement our Digital Coworkers, all users work with their own dedicated personal digital co-worker. Each employee gives their co-worker a real name, for example Calvin or Amy. They clearly teach their digital co-worker specific capabilities and limitations. In conversation, users interact with their digital co-worker as just another team member with specific responsibilities. Team members are encouraged to provide feedback on their digital co-worker's performance, request new capabilities, and communicate with them directly.

The result was remarkable. Rather than fearing displacement by AI, employees embraced it as support. They began spontaneously finding new ways for Amy to help them and suggesting improvements.

We love when a user say "Calvin handles all the routine tasks I used to dread. Now I focus on analyzing market trends and making strategic recommendations - the work I actually went to school for."

This is the essence of our mission at Brisken: making users happy again by changing how humans and technology work together.

Implementation That Puts People First

Beyond the specific best practices for different business functions, there's a universal approach to AI implementation that maximizes both efficiency and satisfaction:

First, measure the right things. Track both operational metrics and employee experience metrics. Efficiency gains matter, but so do satisfaction scores, retention rates, and qualitative feedback.

Second, involve users from day one. The people doing the work understand its pain points better than anyone. Their input is invaluable in identifying automation opportunities and designing interfaces.

Third, implement incrementally but with a comprehensive vision. Start with specific high-impact processes, but maintain a roadmap for expanding across functions. Each success builds momentum and trust.

Fourth, invest in skill development alongside AI deployment. As routine tasks are automated, help employees develop the analytical, creative, and strategic skills that will become more valuable.

Fifth, communicate transparently about how AI will change roles. Fear thrives in information vacuums. Clear communication about how roles will evolve (not disappear) builds trust and engagement.

The Future Belongs To Companies That Get This Right

The question isn't whether your organization will implement AI. It's when and how.

Organizations that approach AI as merely a cost-cutting tool will achieve limited benefits while potentially damaging their culture and losing talent.

Those that use AI to transform the human experience of work will gain both operational advantages and a fundamental competitive edge in attracting and retaining talent.

I've seen this pattern consistently across industries - from banking to manufacturing, insurance to commodities trading. The companies that use AI to make employees happy outperform those focused solely on efficiency metrics.

This isn't surprising when you consider the costs of turnover, disengagement, and untapped human potential. By some estimates, disengaged employees cost organizations 34% of their salary in lost productivity.

The ROI of employee happiness is real. And AI done right is one of the most powerful ways to achieve it.

 

 

 

Getting Started: The Right Sequence

If you're ready to implement AI in ways that boost both efficiency and employee satisfaction, start here:

1. Conduct process and satisfaction audits simultaneously. Identify high-volume, rule-based processes while also gathering input on which tasks employees find most frustrating and unfulfilling.

2. Prioritize initiatives at the intersection of high automation potential and high employee frustration. These will deliver the most powerful combined benefits.

3. Select technologies that prioritize human experience alongside operational efficiency. Look for solutions that simplify interactions rather than adding complexity.

4. Implement with cross-functional teams that include both technical experts and end users. Their collaborative input will ensure solutions that work technically and humanly.

5. Measure comprehensively - track both operational metrics and human experience indicators.

I've guided dozens of organizations through this process. The ones that commit fully see transformative results - not just in efficiency but in workplace culture, employee retention, and ultimately, customer satisfaction.

At Brisken, our mission isn't just to automate work but to revolutionize the business workspace. We believe AI should adapt to humans, not the other way around. Our Digital Coworker and OnePilot framework embody this philosophy, creating experiences where technology serves people rather than forcing people to serve technology.

The future of work isn't about humans becoming more machine-like. It's about machines becoming more human-aware. When we get this right, we create workplaces where both efficiency and happiness thrive.

That's the promise of enterprise AI done right. And it's a promise worth keeping.