Practical AI

Revealing the Unseen

• By Howard M. Cohen
• 08/07/2025

This column is the product of an ongoing hunt for companies that use AI for more than simply writing emails and creating prettier pictures of their executives. Although many seem content treating artificial intelligence like an increasingly more expensive toy, I've been seeking people and organizations that understand the true potential of this technology: systematically improving business outcomes through intelligent automation.

This hunt led me to Copper Sparrow, a consulting firm that's taking a radically different approach to AI implementation. Instead of dazzling clients with flashy chatbots and content generators, they're focusing on what co-founders and principals Mike Danforth and Larry Cohen call "the unseen"—the backend processes that actually drive business results.

The Challenge with Current AI Thinking

Visit any of the rapidly launching AI technology marketplaces today, and you'll find hundreds of AI agents promising to revolutionize your business. Marketing agents, sales agents, customer service agents—they all look remarkably similar, and all deliver predictably modest results. Most organizations implementing these tools report the same experience: initial excitement followed by the sobering realization that their business hasn't fundamentally changed, though their budgets have diminished.

"The value of AI is not in the seen, it's in the unseen," explains Mike Danforth, who spent 15 years in the Salesforce ecosystem before founding Copper Sparrow. "Right now, all of AI is focused on what is seen: increasing sales, improving marketing, and deflecting customer calls. It's not focusing on moving that piece of paper from desk A to desk B without human interaction."

This distinction matters more than you might think. End-user AI tools can provide incremental improvements, but they don't address the fundamental inefficiencies that plague most organizations. They're like a fresh coat of paint on a house with structural problems—it looks better, but the foundation remains shaky.

Starting with the Foundation: Data Strategy

Copper Sparrow's approach begins with a principle that may sound simple but could prove revolutionary in practice: "AI starts and ends with data." Before implementing any artificial intelligence solution, the company conducts a comprehensive audit of how data flows through the organization.

"Most companies aren't ready for AI," notes Larry Cohen. "Automating a broken conveyor belt doesn't make it more efficient—it just pushes Pepsi cans into the ether faster."

This data-first methodology recently proved its worth with a global technology company facing a familiar challenge. A director of data had spent four months manually compiling information from internal and external sources to answer a single question: why were sales declining in a specific region, namely, Central Africa? She already suspected the answer was an ongoing civil war, but needed data to prove it.

The real problem wasn't the four-month research project—it was the underlying data quality issues that made such projects necessary. Field sellers were copying and pasting information instead of providing accurate details. Data existed in multiple formats across various systems. No automated process existed to synthesize information and provide insights.

Copper Sparrow's solution involved three phases:

·      Automating data collection to reduce human error and increase accuracy

·      Implementing AI-driven analysis to provide real-time insights

·      Expanding the system to provide predictive intelligence across marketing, supply chain, and regional operations

The Commercial Mid-Market Sweet Spot

This approach particularly resonates with commercial mid-market companies—organizations large enough to have complex data challenges but not large enough to afford the big consulting firms that charge $400-600 per hour.

"There's a gap in the market," Danforth explains. "Regional systems integrators can connect point A to point B, but they lack the advisory expertise to know what 'good' looks like. The big consulting firms have that expertise, but are prohibitively expensive. We're filling that gap."

Their primary target: business-to-business marketers working in small teams of 2-10 people. These professionals face a unique challenge: they can't easily measure the impact of their efforts, which typically take 12-18 months to show results. AI enables them to integrate data from multiple touchpoints, create attribution models, and provide CFOs with statistically significant proof of marketing's contribution to revenue.

Beyond Automation: Intelligent Process Optimization

Another key differentiator in Copper Sparrow's approach lies in understanding the distinction between simple automation and AI-enhanced process optimization. Traditional automation follows predetermined rules; if this happens, then do that. AI-enhanced automation can make nuanced decisions based on multiple variables, learning and adapting over time.

Consider the difference between an automated email sequence and an AI-driven customer engagement system. The automated sequence sends the same messages to everyone at predetermined intervals. The AI system analyzes customer behavior, engagement patterns, communication preferences, and business context to determine optimal timing, messaging, and channel selection for each individual interaction.

This level of sophistication becomes possible only when organizations have properly structured and harmonized their data, which explains why Copper Sparrow insists upon starting every engagement with comprehensive data strategy work.

AI’s at the Fulcrum: Time Versus Money

For executives evaluating AI investments, the value proposition often comes down to a simple equation: how much money am I willing to spend to get significantly more time back?

Danforth shared how one customer put it: If I can spend more money and get a lot more time back, I'm willing to spend more money. According to Danforth, this time-versus-money calculation drives how they prioritize AI implementations.

The most successful projects focus on processes that currently require significant human time and judgment but could be streamlined through intelligent automation. Examples include:

• Data analysis and reporting: Instead of analysts spending weeks compiling reports, AI systems can synthesize information from multiple sources and highlight anomalies or trends in real-time.
• Lead qualification and routing: Rather than sales development representatives manually researching and scoring prospects, AI can analyze multiple data points to identify high-value opportunities and route them appropriately.
• Compliance and risk monitoring: Instead of periodic manual audits, AI systems can continuously monitor transactions, communications, and processes for potential issues.

Implementation Strategy: Working Through the Channel

Copper Sparrow's go-to-market strategy offers lessons for other AI service providers. Rather than pursuing end customers directly, they focus on enabling Salesforce account executives (AE) with thought leadership content and sales tools.

"I just need to convince Salesforce AEs that I know what I'm talking about on AI, and then they will call me every time," Danforth explains. "This approach leverages existing relationships and trust, significantly shortening sales cycles."

The strategy works because Salesforce—like most technology vendors—is pushing AI solutions that their partner ecosystem isn't equipped to deliver. By providing both the expertise and the content to support these conversations, Copper Sparrow positions itself as the go-to resource for complex AI implementations.

Looking Forward: The Agentic Age

While current AI implementations focus on augmenting existing processes, Microsoft CEO Satya Nadella has predicted a shift toward what he calls "the agentic age," where traditional applications give way to AI agents working together to accomplish business objectives.

This vision might seem overwhelming for organizations still struggling with basic data integration, but it reinforces the importance of getting the fundamentals right. Companies that invest now in proper data strategy and intelligent process automation will be well-positioned for more advanced AI and agentic implementations in the future.

Those that continue treating AI as a collection of end-user tools will likely find themselves further behind the competitive curve as the technology evolves.

The Bottom Line

The most practical approach to AI isn't about finding the flashiest application or the most user-friendly interface. It's about systematically identifying where intelligent automation, machine learning, and artificial intelligence’s data-handling abilities can eliminate inefficiencies, reduce human error, and accelerate decision-making processes.

Copper Sparrow emphasizes that this requires starting with a data strategy, focusing on backend processes rather than frontend tools, and working with partners who understand both the technical implementation and the business strategy required for success.

For organizations ready to move beyond AI as a novelty and start using it as a genuine business transformation tool, the path forward is clear: stop asking what AI can do and start asking how AI can make your existing processes work better, faster, and more reliably to deliver superior business outcomes.

To find your best path to a better future leveraging AI, look for the unseen.