Stop Measuring AI Success by Lines of Code: The Real ROI is in the Boring Stuff 

The headlines are hard to miss, “AI-powered code generation boosting developer velocity by 30%.” Lines of code written per hour skyrocketing. Teams shipping features faster than ever. 

Yet the most significant returns aren’t showing up in those flashy metrics. The real ROI is emerging in places far less glamorous: the work that usually gets postponed, rushed, or quietly skipped. 

The Quality Underground 

While much attention is placed on code generation speed, something more consequential is happening behind the scenes. AI is proving most valuable when it tackles the tedious but essential work developers often deprioritize. 

Test creation. Documentation updates. Boilerplate scaffolding. The quiet foundations of reliable software. 

When testing becomes easier, teams actually do it. When documentation updates itself, it actually stays current. Organizations using AI-augmented testing report 50% lower costs and 60% faster test cycles¹. That’s more than efficiency. It’s a shift in quality assurance discipline. 

A clear pattern is emerging: the less exciting the task, the greater the AI payoff. 

The Multiplier Effect 

This is where traditional measurements fall short. Counting lines of code tells us little about stability. Shipping features faster is less impressive if those features fail in production. 

By contrast, metrics like test coverage and documentation completeness tell a different story. They reveal AI as a speed accelerator and a quality multiplier. 

Some organizations are already seeing dramatic improvements, with test coverage climbing from 60% to 85%, documentation kept current for the first time in years, and edge cases automatically captured. 

The takeaway is straightforward. AI makes developers quicker, and it makes the software they build more reliable. 

The Tasks That Actually Matter 

Consider the flow of software development. Writing business logic is often the easy part. The heavier lift comes in the margins: building robust test suites, maintaining documentation, handling edge cases thoroughly. 

These are the tasks that are critical for quality, slow to complete, and frequently sacrificed under pressure. They are also the exact tasks where AI thrives. 

Take test generation. Creating comprehensive tests often takes longer than the code itself, demanding developers think through failures and integration scenarios. AI can analyze code patterns, detect gaps, and generate tests that human teams might overlook. The result is not just faster coverage, but broader and more consistent coverage. 

The Measurement Revolution 

This shift creates an opening to rethink how AI success is measured.  Instead of tracking raw velocity, organizations are following quality indicators:  

These indicators surface AI’s true value: not simply producing more code but producing better software. 

The Compound Returns 

Quality improvements have a different kind of payoff: they compound. 

Faster code generation saves time today. Stronger test coverage prevents costly failures tomorrow. Automated documentation will reduce onboarding time next quarter. Better quality controls fuel faster iteration next year. 

Measured through this lens, AI’s impact becomes clearer. A 50% drop in production bugs delivers far greater financial benefit than a 50% increase in code generation speed. 

The Quality Advantage 

Teams focusing here are building something rare: systematic quality improvement woven into the development process itself. 

Others may continue to compete on speed, but organizations that compete on reliability are building resilience. They’re lowering technical debt instead of accumulating it. They’re creating the conditions for sustainable experimentation. 

Over time, that advantage compounds into a moat that’s hard to cross. 

Reframing Success 

When the next report touts impressive AI coding velocity, a different question is worth asking, “What is happening to quality?” 

Because real AI transformation isn’t about developers typing faster. It’s about software that’s more dependable, because the unglamorous work is finally being done. 

Organizations that see this are measuring the right outcomes. They’re finding that the “boring” tasks create the most durable advantages. Those are often the ones that matter most when customers decide whose product they trust. 

The pace of AI change can feel relentless with tools, processes, and practices evolving almost weekly. We help organizations navigate this landscape with clarity, balancing experimentation with governance, and turning AI’s potential into practical, measurable outcomes. If you’re looking to explore how AI can work inside your organization—not just in theory, but in practice—we’d love to be a partner in that journey. Request a strategy session. 

Sources: 

  1. Unisys, ROI of Generative AI in Software Testing, 2024 

Beyond Story Points: Rethinking Software Engineering Productivity in the Age of AI 

Why traditional metrics fall short, and how modern frameworks like DORA and SPACE can guide better outcomes 

For years, engineering leaders have relied on familiar metrics to gauge developer performance: story points, bug counts, and lines of code. These measures offered a shared baseline, especially in Agile environments where estimation and output needed a common language. 

But in today’s AI-assisted world, those numbers no longer tell the full story. Performance isn’t just about volume or velocity. It’s about outcomes. Did the developer deliver the expected functionality, with the right quality, on time? That’s how we compensate today, and that’s still what matters. But how we measure those things must evolve.  

With tools like GitHub Copilot, Claude Code, and Cursor generating entire functions, tests, and documentation quickly, output is becoming less about what a developer types and more about what they model, validate, and evolve. 

The challenge for CIOs, CTOs, and SVPs of Engineering isn’t just adopting new tools. It’s rethinking how to measure effectiveness in a world where productivity is amplified by AI and complexity often hides behind automation. 

Why Traditional Metrics Break Down 

The future of measurement hinges on three categories: productivity, quality, and functionality. These have always been essential to evaluating engineering work. But in the AI era, we must measure them differently. That shift doesn’t mean abandoning objectivity; it means updating our tools. 

The problem isn’t that legacy metrics are useless. It’s that they’re easily gamed, misinterpreted, or disconnected from business value. 

At best, these metrics create noise. At worst, they drive harmful incentives, like rewarding speed over safety, or activity over alignment. 

Today’s AI-assisted workflows lack mature solutions for tracking whether functionality requirements, like EPICs and user stories, have been fully met. But new approaches, like multi-domain linking (MDL), are emerging to close that gap. Measurement is getting smarter, and more connected, because it has to. 

The Rise of Directional Metrics 

Modern frameworks like DORA and SPACE were built to address these gaps. 

DORA (DevOps Research and Assessment) focuses on: 

These measure delivery health, not just effort. They’re useful for understanding how efficiently and safely value reaches users. 

SPACE (developed by Microsoft Research) considers: 

SPACE offers a more holistic view, especially in cross-functional and AI-assisted teams. It acknowledges that psychological safety, cross-team communication, and real flow states often impact long-term output more than individual commits. 

AI Complicates the Picture 

AI tools don’t eliminate the need for metrics; they demand smarter ones. When an LLM can write 80% of the code for a feature, how do we credit the developer? By the number of keystrokes? Or by their judgment in prompting, curating, and validating what the tool produced? 

But here’s the deeper challenge: What if that feature doesn’t do what it was supposed to? 

In AI-assisted workflows: 

Productivity isn’t just about output; it’s about fitness to purpose. Without strong traceability between code, tests, user stories, and epics, it’s easy for teams to ship fast but fall short of the business goal. 

Many organizations today struggle to answer a basic question: Did this delivery actually fulfill the intended functionality? 

This is where multi-domain linking (MDL) and AI-powered traceability show promise. By connecting user stories, requirements, test cases, design artifacts, and even user feedback within a unified graph, teams can use LLMs to assess whether the output truly matches the input. 

And this capability unlocks more than just better alignment, it opens the door to innovation. AI-assisted development enables organizations to build more complex, interconnected, and adaptive systems than ever before. As those capabilities expand, so too must our ability to measure their economic value. What applications can we now build that we couldn’t before? And what is that worth to the business? 

That’s not a theoretical exercise. It’s the next frontier in engineering measurement. 

Productivity as a System, Not a Score 

The best engineering organizations treat productivity like instrumentation. No single number can tell you what’s working, but the right mix of signals can guide better decisions. That system must account for both delivery efficiency and functional alignment. High velocity is meaningless if the outcome doesn’t meet the requirements it was designed to fulfill. 

That means: 

Most importantly, it means aligning measurement to what matters: Did the product deliver value? Did it meet its intended function? Was the effort worth the outcome? Those are the questions that still define success and the ones our measurement frameworks must help answer. 

How to Start Rethinking Measurement 

If your metrics haven’t evolved alongside your tooling, here’s how to get started: 

AI is reshaping how software gets built. That doesn’t mean productivity can’t be measured. It means it must be measured differently. The leaders who shift from tracking motion to monitoring momentum will build faster, healthier, and more resilient engineering teams. 

Robots & Pencils: Measuring What Matters in an AI-Driven World 

At Robots & Pencils, we believe productivity isn’t a score; it’s a system. A system that must measure not just speed, but alignment. Did the output meet the requirements? Did it fulfill the epic? Was the intended functionality delivered? 

We help clients extend traditional measurement approaches to fit an AI-first world. That means combining DORA and SPACE metrics with functional traceability, such as linking code to requirements, outcomes to epics, and user stories to business results. 

Our secure, AWS-native platforms are already instrumented for this kind of visibility. And our teams are actively designing multi-domain models that give leaders better answers to the questions they care about most. 

As AI opens the door to applications we never thought were possible, our job is to help you measure what matters, including what’s newly possible. We don’t just help teams move faster. We help them build with confidence and prove it. 

Pilot, Protect, Produce: A CIO’s Guide to Adopting AI Code Tools 

How to responsibly explore tools like GitHub Copilot, Claude Code, and Cursor—without compromising privacy, security, or developer trust 

AI-assisted development isn’t a future state. It’s already here. Tools like GitHub Copilot, Claude Code, and Cursor are transforming how software gets built, accelerating boilerplate, surfacing better patterns, and enabling developers to focus on architecture and logic over syntax and scaffolding. 

The productivity upside is real. But so are the risks. 

For CIOs, CTOs, and senior engineering leaders, the challenge isn’t whether to adopt these tools—it’s how. Because without the right strategy, what starts as a quick productivity gain can turn into a long-term governance problem. 

Here’s how to think about piloting, protecting, and operationalizing AI code tools so you move fast, without breaking what matters. 

Why This Matters Now 

In a recent survey of more than 1,000 developers, 81% of engineers reported using AI assistance in some form, and 49% reported using AI-powered coding assistants daily. Adoption is happening organically, often before leadership even signs off. The longer organizations wait to establish usage policies, the more likely they are to lose visibility and control. 

On the other hand, overly restrictive mandates risk boxing teams into tools that may not deliver the best results and limit experimentation that could surface new ways of working. 

This isn’t just a tooling decision. It’s a cultural inflection point. 

Understand the Risk Landscape 

Before you scale any AI-assisted development program, it’s essential to map the risks: 

These aren’t reasons to avoid adoption. But they are reasons to move intentionally with the right boundaries in place. 

Protect First: Establish Clear Guardrails 

Protect First: Establish Clear Guardrails 

A successful AI coding tool rollout begins with protection, not just productivity. As developers begin experimenting with tools like Copilot, Claude, and Cursor, organizations must ensure that underlying architectures and usage policies are built for scale, compliance, and security. 

Consider: 

For teams ready to push further, Bedrock AgentCore offers a secure, modular foundation for building scalable agents with memory, identity, sandboxed execution, and full observability, all inside AWS. Combined with S3 Vector Storage, which brings native embedding storage and cost-effective context management, these tools unlock a secure pathway to more advanced agentic systems. 

Most importantly, create an internal AI use policy tailored to software development. It should define tool approval workflows, prompt hygiene best practices, acceptable use policies, and escalation procedures when unexpected behavior occurs. 

These aren’t just technical recommendations, they’re prerequisites for building trust and control into your AI adoption journey. 

Pilot Intentionally 

Start with champion teams who can balance experimentation with critical evaluation. Identify low-risk use cases that reflect a variety of workflows: bug fixes, test generation, internal tooling, and documentation. 

Track results across three dimensions: 

Encourage developers to contribute usage insights and prompt examples. This creates the foundation for internal education and tooling norms. 

Don’t Just Test—Teach 

AI coding tools don’t replace development skills; they shift where those skills are applied. Prompt engineering, semantic intent, and architectural awareness become more valuable than line-by-line syntax. 

That means education can’t stop with the pilot. To operationalize safely: 

When used well, these tools amplify good developers. When used poorly, they obscure problems and inflate false productivity. Training is what makes the difference. 

Produce with Confidence 

Once you’ve piloted responsibly and educated your teams, you’re ready to operationalize with confidence. That means: 

Organizations that do this well won’t just accelerate development, they’ll build more resilient software teams. Teams that understand both what to build and how to orchestrate the right tools to do it. The best engineering leaders won’t mandate one AI tool or ban them altogether. They’ll create systems that empower teams to explore safely, evaluate critically, and build smarter together. 

Robots & Pencils: Secure by Design, Built to Scale 

At Robots & Pencils, we help enterprise engineering teams pilot AI-assisted development with the right mix of speed, structure, and security. Our preferred LLM, Anthropic, was chosen precisely because we prioritize data privacy, source integrity, and ethical model design; values we know matter to our clients as much as productivity gains. 

We’ve been building secure, AWS-native solutions for over a decade, earning recognition as an AWS Partner with a Qualified Software distinction. That means we meet AWS’s highest standards for reliability, security, and operational excellence while helping clients adopt tools like Copilot, Claude Code, and Cursor safely and strategically. 

We don’t just plug in AI; we help you govern it, contain it, and make it work in your world. From guardrails to guidance, we bring the technical and organizational design to ensure your AI tooling journey delivers impact without compromise. 

The Changing Role of the Computer Programmer 

How generative AI, cloud-native services, and intelligent orchestration are redefining the developer role and what it means for modern engineering teams 

In the early days of computing, programmers were indispensable because they were the only ones who could speak the language of machines. From punch cards to assembly language, software development was hands-on and highly specialized. Even as languages evolved, from COBOL and C to Java and C#, one thing stayed constant: developers wrote every line themselves. 

But that’s no longer true. And it hasn’t been for a while. 

Today, enterprise developers have access to an entirely new class of tools: generative AI, intelligent agents, and secure, cloud-native building blocks that reduce the need to write, or even see, large amounts of code. This shift isn’t superficial. It’s redefining the nature of software development itself. 

A recent Cornell University study reports that AI now generates at least 30% of Python code in major repositories in the U.S. And in enterprise environments at Google and Microsoft, 30–40% of new code is reported as AI-generated. That’s not a tweak in tooling. That’s a turning point in how software gets built. 

From Code to Composition 

For decades, the dominant paradigm in programming was one of writing: the developer’s job was to build logic from scratch, test it for accuracy, and ensure it could scale. As complexity grew, so did the stack of tools, including IDEs, frameworks, QA platforms, and versioning systems to support that work. 

But in the last few years, the developer toolbox has changed dramatically. Tools like GitHub Copilot, Claude Code, and Cursor now generate reliable code in real time. Entire modules can be scaffolded with a few prompts. Meanwhile, cloud platforms like AWS offer modular services that handle everything from authentication to observability out of the box. 

The result? Developers are shifting from authors to orchestrators. The value isn’t in how much code they can write; it’s in how well they can assemble, adapt, and govern systems that are increasingly AI-enabled, cloud-native, and composable. 

Productivity and Quality are Improving, but are We Building the Right Thing? 

AI-assisted development produces measurable gains. Code is being written faster. Boilerplate is disappearing. Bugs are easier to catch early. Even tests can be autogenerated. And yet, one challenge persists: verifying that the right thing is being built. 

It’s relatively straightforward to measure productivity (lines of code, lead time) and quality (bug rates, test coverage). But ensuring correct functionality, such as matching what’s shipped to product requirements, user stories, and EPICs, is harder than ever. Code generation tools accelerate output, but they don’t always ensure alignment with intent. 

That’s why the developer’s role is expanding. Understanding product vision, aligning technical architecture with business goals, and managing evolving requirements are becoming just as critical as technical skill. 

What Should Engineering Leaders Expect from Modern Developers? 

The pace of innovation in AI development tools is relentless. What a developer learns today may be outdated in a few months. This puts enormous pressure on engineering leaders to balance experimentation with sustainability. 

The safest path forward? Anchor learning and experimentation within robust cloud ecosystems. AWS, for instance, offers stable development trajectories, strong security guardrails, and continuous improvements that minimize disruption. The goal isn’t to chase every new tool; it’s to build foundational fluency and adapt deliberately. 

To succeed in this new environment, developers must think differently: 

Code Isn’t Dead, but It’s Being Delegated 

Let’s be clear: programming isn’t going away. But its role is evolving. The most impactful developers won’t be those who write the most lines of code, they’ll be the ones who know how to compose, configure, and coordinate intelligent systems with speed and confidence. 

They’ll use prompts, ontologies, and models as naturally as they once used loops and conditionals. They’ll know when to generate, when to review, and when to intervene. And they’ll be deeply embedded in outcome-oriented thinking. 

What Should Engineering Leaders Do Next? 

As the role of the programmer changes, so too must the systems that support them. This means: 

The ground is shifting. But for organizations willing to embrace this change, the opportunity is enormous: faster iteration, stronger alignment, and more resilient systems—built by developers who think in outcomes, not just code. 

Robots & Pencils: Redefining the Role, Rebuilding the Foundation 

At Robots & Pencils, we’ve spent over a decade helping organizations adapt to shifts in software architecture and engineering practice. As developers move from coding line-by-line to orchestrating intelligent, cloud-native systems, our role is to help them and their leaders make that leap with confidence. 

We design secure, cloud-native environments that empower developers to compose, not just code. With Anthropic as our preferred LLM and a track record of building modular, scalable solutions, we give teams the foundation they need to experiment responsibly, build faster, and deliver more value without compromising on security or quality. 

For teams rethinking what it means to “write software,” we bring the expertise, architecture, and systems design to make the next role of the developer a strength, not a risk. 

Patrick Higgins Named Chief Revenue Officer at Robots & Pencils

From IBM transformation to AI-powered product strategy, Higgins brings proven enterprise expertise and client-first vision to fuel the firm’s next phase of commercial expansion

Robots & Pencils, an AI-first, global digital innovation firm specializing in cloud-native web, mobile, and app modernization, today announced the appointment of Patrick Higgins as Chief Revenue Officer (CRO). A seasoned technology leader with over 15 years of experience driving digital innovation for Fortune 500 companies, Higgins steps into a pivotal role to deepen client partnerships, scale impact, and fuel the company’s next phase of growth.

Higgins built his career at the intersection of digital product development, enterprise transformation, and applied AI. He began at IBM delivering mission-critical programs for large government and healthcare clients. Higgins then spent nearly a decade at WillowTree, where he helped scale the firm into a full-service digital agency and led go-to-market efforts across Media, Healthcare, and most recently, AI. Over the past year alone, he has advised more than 80 organizations on how to turn AI ambitions into action through strategic governance, rapid prototyping, and practical deployment strategies.

Now, as CRO at Robots & Pencils, Higgins will lead all commercial operations, with a focus on aligning strategy, sales, and client partnerships to help organizations unlock the full potential of AI, cloud-native architecture, and next-generation experiences.

“Patrick’s ability to listen deeply, build trust, and connect business goals to technical outcomes is exceptional,” said Leonard Pagon, CEO of Robots & Pencils. “He doesn’t just understand AI—he understands how to activate it inside the enterprise. He’s helped clients across industries turn emerging tech into scalable solutions, and his presence here marks a key step in our evolution. We’re not chasing growth for growth’s sake—we’re scaling the way we serve our clients. Patrick is the right leader to ensure that growth stays grounded in trust, results, and partnership.”

Higgins joins a growing executive team committed to challenging the traditional global systems integrators with a model that prioritizes speed, strategy, and elite delivery. With global centers of excellence and strategic partnerships with AWS, Salesforce, and Databricks, Robots & Pencils is positioned to help clients move beyond experimentation and into meaningful, AI-infused transformation.

“What drew me to Robots & Pencils is the caliber of the team and the clarity of the mission,” said Higgins. “We’re not just talking about AI. We’re delivering it—wrapped in thoughtful design, modern cloud infrastructure, and agile engineering. This is a firm built to move fast and deliver real results, and I’m honored to help lead the next chapter.”

In addition to his leadership role, Higgins is an active contributor to the AI community, serving as a panelist for the University of Virginia’s AI initiatives and helping organizations demystify their path to innovation. He holds a BA and MBA from the University of Virginia and lives in Charlottesville with his family.

Context Is King: How AWS & Anthropic Are Redefining AI Utility with MCP 

If AI is going to work at scale, it needs more than a model; it needs access, structure, and purpose. 

At the AWS Summit in New York City, one phrase stuck with us: 

 “Models are only as good as the context they’re given.” 

It came during an insightful joint session from AWS and Anthropic on Model Context Protocol (MCP), a deceptively simple concept with massive implications. 

Across this recap series, we’ve explored the rise of agentic AI, the infrastructure required to support it, and the ecosystem AWS is building to accelerate adoption. MCP is the connective tissue that brings it all together. It’s how you move from smart models to useful systems. 

Why Context Is the New Bottleneck 

Generative AI has been evolving fast, but enterprise implementation is still slow. Why? 

Because no matter how advanced your model is, it can’t help you make better decisions if it’s not connected to what makes your business unique: Your data. Your tools. Your systems. Your users. 

That’s where MCP comes in. 

What Is MCP—and Why It Matters 

Model Context Protocol (MCP) is a specification that allows AI models to dynamically discover and interact with third-party tools, data sources, and instructions. Think of it as a structured interface—where systems publish a list of tools, what they do, the inputs they require, and how the model should use them. 

For executives, that means your AI agents can tap into real business logic—not by guessing, but by calling documented resources your teams control. For engineers, it means you can expose functions, services, or datasets via an MCP server, enabling LLMs to perform meaningful actions without hardcoding every step. 

The result? AI that doesn’t just respond—it executes, using tools it finds and understands in real time. 

With MCP, you can: 

In short: MCP allows generative AI to break free of the chat window and take real-world action.  

Real Integration, Not Just Model Tuning 

With MCP servers already available in AWS, your teams can start building agentic AI products that can utilize your unique business logic, customer data, and internal systems. This isn’t hypothetical. It’s real and ready to deploy today. 

At Robots & Pencils, we’re already using this pattern with our clients: 

We call this approach Emergent Experience Design, a framework for building systems where agents adapt, interfaces evolve, and outcomes unfold through interaction. If you’re rethinking UX in the age of AI, this is where to start. 

And when you combine this with what we covered in The Future Is Agentic, Modernization Reloaded, and From AI to Execution, you start to see the bigger picture: Agentic AI isn’t just a new model. It’s a new way of working. And context is the infrastructure it runs on. 

Plug AI into the Business, Not Just the Cloud 

The hype phase of generative AI is behind us. What matters now is how well your systems can support intelligent action. If you want AI that drives real outcomes, you don’t just need better models. You need better context. That’s the promise of MCP—and the opportunity ahead for organizations ready to take the next step. 

If you’re experimenting with GenAI and want to connect it to your real-world data and systems, we should talk. 

Nathan Carmon Named Chief Operating Officer of Robots & Pencils

Seasoned transformation leader joins the executive team to scale AI-first growth, elevate client delivery, and drive operational excellence

Robots & Pencils, an AI-first, global digital innovation firm specializing in cloud-native web, mobile, and app modernization, today announced the appointment of Nathan Carmon as Chief Operating Officer (COO). A battle-tested operator and longtime business partner to CEO Leonard Pagon, Carmon joins at a pivotal moment to bring sharper execution, deepen client value, and accelerate the company’s AI-first momentum.

Carmon’s mandate isn’t just about growing fast—it’s about growing right. That means investing in people, refining delivery systems, and ensuring every client engagement is marked by clarity, care, and measurable impact. His arrival marks another major milestone in Robots & Pencils’ evolution from mobile pioneer to AI-first consultancy.

For more than 20 years, Carmon and Pagon have built, scaled, and sold consulting businesses together—from growing Brulant from a 25-person startup into a 500-person powerhouse, to doubling Rosetta’s scale post-acquisition. As an Operating Partner at Next Sparc, Carmon has spent the last decade turning strategy into results across a portfolio of high-growth ventures. Now, he brings that same transformation expertise to Robots & Pencils.

“Nathan is the rare kind of operator who makes complex things simple and ambitious things achievable,” said Leonard Pagon, CEO of Robots & Pencils and President of Next Sparc. “When we were building Brulant, I could count on Nathan to turn big vision into day-to-day momentum. We scaled fast because he knew how to align teams, deliver value, and move with speed and precision. With Nathan as COO, we’re not just ready for what’s next—we’re built for it.”

Carmon joins at a time when every business is under pressure to adopt AI, modernize legacy systems, and compete at digital speed. Robots & Pencils is meeting that moment with a challenger mindset, deploying high-impact, Navy SEAL-style teams that combine elite engineering with intuitive UX design. With global delivery across North America, Latin America, and Eastern Europe, and partnerships with AWS, Salesforce, and Databricks, the firm is uniquely positioned to help clients lead in the age of AI.

“I’ve helped scale consulting firms from the ground up, and I know what great looks like,” said Carmon. “Robots & Pencils has it. The talent here is exceptional, the leadership is bold, and the market timing couldn’t be better. This team has everything we need to do it again—only bigger, faster, and smarter.”

“With Nathan leading our internal operations, I’m freed up to spend more time where I create the most value—engaging directly with clients, exploring strategic opportunities, and helping shape the future of Robots & Pencils to meet market demand,” said Pagon. “That’s the power of having a leader you trust.”

Carmon holds a BS in Computer Science and an MBA from the University of Michigan. When he’s not accelerating digital transformations, he enjoys wake surfing, biking, model rocketry, and spending time with his daughter and four grandchildren.