AI is rapidly reshaping the way enterprises approach software development, promising increased efficiency, faster prototyping, and streamlined workflows. However, adopting AI (particularly agentic AI, which is designed to act autonomously) requires a clear strategy, grounded expectations, and robust risk management. The path to success involves not just selecting the right tools but also preparing teams and processes to maximize their potential.
Let’s Collaborate
This post only scratches the surface of what AI can do for your enterprise. Let’s collaborate to unlock AI’s full potential. Whether you need guidance navigating vendor solutions, building team readiness, or integrating AI into your workflows, I can help you align technology with your goals. Together, we can drive smarter, faster, and more secure outcomes.
Grounding Insights in Experience
For as long as I can remember, coding has been at the heart of my career—whether as an enterprise architect, digital transformation leader, or hands-on developer solving eCommerce challenges. The recent pause brought on by redundancy, while unplanned, has allowed me to sharpen my focus on personal projects that have been simmering in the background.
Between job hunting, which remains my top priority, I’ve been diving into developing modular, extensible capabilities that I know are missing from the eCommerce ecosystem. These are solutions I’ve seen every company struggle with, often resorting to custom, one-off implementations. Leveraging my experience in architecture and scalable design, I’ve been building tools designed to slot seamlessly into various systems—like plugins for a digital world.
This time has also enabled me to delve deeply into AI-powered coding tools like Visual Studio-based Cline, Cursor, Windsurf, and Aider. Combined with LLMs like ChatGPT, Gemini, DeepSeek, and Claude, these tools have proven invaluable for exploring how AI can accelerate development and enhance workflows. While tools like GitHub Copilot remain popular, they are notably weaker than the competition in many areas. I’ve navigated these tools’ strengths, such as rapid prototyping, and their limitations, such as managing context windows and refining prompts. Redundancy hasn’t reintroduced me to coding; it’s simply given me the space to go all-in, revisiting my roots with fresh challenges and late-night sessions that remind me why I love this work.
The Reality of AI in Enterprises
AI tools are frequently marketed as revolutionary, with vendors and suppliers promising to transform businesses by delivering higher revenue and reducing costs. At the same time, shareholders expect immediate gains, creating significant pressure on enterprises to adopt AI quickly. These external narratives often overlook a critical truth: while AI’s potential is transformative, realizing its value requires foundational changes that take time, effort, and investment.
For enterprises, particularly those exploring agentic AI (AI systems capable of autonomous decision-making), the challenges and risks multiply. Agentic AI shifts the focus from simple task automation to enabling systems that can act independently. While this unlocks powerful possibilities, it demands greater scrutiny, including how decisions are made, monitored, and aligned with business objectives. Enterprises must approach this technology thoughtfully, balancing innovation with control to avoid unintended outcomes.
Understanding Large Language Models (LLMs): Foundations and Implications
LLMs remain the backbone of AI development tools, offering capabilities that range from generating boilerplate code to drafting complex specifications. Their strengths and limitations are pivotal to understanding AI’s role in enterprise development:
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The Role of Model Choice Not all LLMs are created equal. ChatGPT, Gemini, DeepSeek, and Claude each have unique strengths. For example, reasoning-focused models like Claude excel at creative problem-solving, while others like Gemini offer cutting-edge multimodal capabilities. Enterprises must regularly evaluate their model choices, as capabilities evolve rapidly (often weekly).
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Prompting and Context Management My work with AI tools highlighted the importance of prompt engineering and understanding context windows. Each model processes input differently, with strict context windows determining how much information can be considered at once. For large enterprises, adapting workflows to these constraints is essential.
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Agentic AI and Autonomous Decisions Emerging agentic AI systems introduce reasoning capabilities that allow them to act with autonomy. This shift brings opportunities to offload complex decision-making but also amplifies risks, as decisions may deviate from expectations or lead to unintended consequences. Enterprises should deploy agentic AI strategically, pairing it with robust monitoring and validation processes.
What AI Can (and Cannot) Deliver
For enterprises, AI offers clear opportunities but also significant limitations. Understanding these distinctions is critical for aligning expectations across all levels of the organization.
What AI Can Deliver
- Efficiency Gains: Automate routine tasks like writing tests, generating boilerplate code, or summarizing technical specs.
- Faster Prototyping: Rapidly develop MVPs or exploratory features for validation and iteration.
- Enhanced Documentation: Draft initial versions of technical documents, freeing up developer time.
What AI Cannot Deliver
- Immediate Transformation: AI adoption requires foundational investments in team skills, workflows, and risk management.
- Contextual Understanding: Large, interconnected enterprise systems often exceed AI’s current capabilities.
- Autonomy Without Oversight: Agentic AI demands careful monitoring to ensure its actions align with organizational goals and compliance standards.
Preparing for AI: Grounding the Enterprise
For enterprises to harness AI’s potential, they need to align all levels of the organization around realistic goals and actionable strategies:
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C-Level Alignment Educate leadership on the realities of AI, including agentic AI’s risks and benefits, to counter vendor-driven hyperbole and ensure informed decision-making.
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Team Readiness Equip engineering teams with the skills needed for prompt engineering, integrating AI into workflows, and validating its outputs.
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Iterative Rollout Begin with small, targeted implementations, such as automating test generation, and expand as teams become proficient.
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Risk Management Address security, privacy, and compliance concerns proactively. In my experience leading digital transformation projects, addressing security and privacy from the outset has always enabled smoother scaling and integration.
Thought-Provoking Examples for Enterprises
To illustrate AI’s practical value, here are some workflows where it can drive meaningful impact:
- Specification Drafting: Use reasoning-focused models to create detailed specs for new features or systems.
- Code Review Assistance: Employ AI to identify inefficiencies, security vulnerabilities, or style inconsistencies during code reviews.
- Scaling Prototypes: Leverage agentic AI to manage dynamic aspects of scaling MVPs into production-ready solutions.
Building a Balanced Future
AI adoption, especially agentic AI, is a journey that requires pragmatism and aspiration. Enterprises must build the foundational capabilities to integrate AI responsibly while embracing its potential to revolutionize workflows. The future of development is not about replacing teams but empowering them to focus on innovation, strategy, and delivering scalable solutions.
Let’s Collaborate
This post represents only a fraction of the expertise and insights I can bring to your enterprise. AI has the potential to transform your business, but success requires a clear strategy, grounded expectations, and experienced guidance. With my background in eCommerce and enterprise architecture, I can help your organization:
- Evaluate and integrate AI tools effectively
- Prepare teams with the skills they need for success
- Navigate the complexities of agentic AI adoption and risk management
Let’s connect to unlock the full potential of AI for your business (whether through consulting engagements or as part of your team).