Tagged: LLM

enterprise_software_is_broken

Enterprise SaaS is Broken. AI Agents Can Fix It.

Let’s talk about enterprise software.

Everyone knows the dirty secret: it’s complex, bloated, slow to change, and ridiculously expensive to customize. It’s a million dollar commitment for a five-year implementation plan that still leaves users with clunky UIs, missing features, and endless integration headaches.

And yet, companies line up for enterprise software as a service (SaaS) products. Why? Because the alternative, building custom systems from scratch, can be even worse.

But what if there was a third way?

I believe there is. And I believe AgenticOps and AI agents are the key to unlocking it.

The Current Limitation: AI Agents Can’t Build Enterprise Systems (Yet)

There’s a widely held belief that AI agents aren’t capable of building and maintaining enterprise software. And let’s be clear: today, that’s mostly true.

Enterprise software isn’t just code. It’s architecture, security, compliance, SLAs, user permissions, complex business rules, and messy integrations. It’s decades of decisions and interdependencies. It requires long-range memory, system-wide awareness, judgment, and stakeholder alignment.

AI agents today can generate CRUD services and unit tests. They can refactor a function or scaffold an API. But they can’t steward a system over time, not without help.

The Disruptive Model: Enterprise System with a Core + Customizable Modules

If I were to build a new enterprise system today, I wouldn’t sell build a monoliths or one-off custom builds.

I’d build a base platform, a composable, API-driven foundation of core services like auth, eventing, rules, workflows, and domain modules (like claims, rating engines, billing, etc. for insurance).

Then, I’d enable intelligent customization through AI agents.

For example, a customer could start with a standard rating engine, then they could ask the system for customizations:

> “Can you add a modifier based on the customer’s loyalty history?”

An agent would take the customization request:

  • Fork the base module.
  • Inject the logic.
  • Update validation rules and documentation.
  • Write test coverage.
  • Submit a merge request into a sandbox or preview environment.

This isn’t theoretical. This is doable today with the right architecture, agent orchestration, and human-in-the-loop oversight.

The Role of AI Agents in This Model

AI agents aren’t building without engineers. They’re replacing repetition. They’re doing the boilerplate, the templating, the tedious tasks that slow innovation to a crawl.

In this AgenticOps model, AI agents act as:

  • Spec interpreters (reading a change request and converting it into code)
  • Module customizers (modifying logic inside a safe boundary)
  • Test authors and validators
  • Deployment orchestrators

Meanwhile, human developers become:

  • Architects of the core platform
  • Stewards of system integrity
  • Reviewers and domain modelers
  • Trainers of the agent workforce

The AI agent doesn’t own the system. But it extends it rapidly, safely, and repeatedly.

This Isn’t Just Faster. It’s a Better Business Model.

What we’re describing is enterprise software as a service as a living organism, not a static product. It adapts, evolves, and molds to each client’s needs without breaking the core.

It means:

  • Shorter sales cycles (“Here’s the base. Let’s customize.”)
  • Lower delivery cost (AI handles the repetitive implementation work)
  • Faster time to value (custom features in days, not quarters)
  • Higher satisfaction (because the system actually does what clients need)
  • Recurring revenue from modules and updates

What It Takes to Pull This Off

To make this AgenticOps model work, we need:

  • A composable platform architecture with contracts at every boundary (OpenAPI, MCP, etc.)
  • Agents trained on domain-specific architecture patterns and rules
  • A human-in-the-loop review system with automated guardrails
  • A way to deploy, test, and validate changes per client
  • Observability, governance, and audit logs for every action an agent takes

Core build with self serve client customizations.

AI Agents Won’t Build Enterprise Software Alone. But They’ll Change the Game for Those Who Do.

In this vision, AI Agents aren’t here to replace engineers. In reality, they may very well replace some engineers, but they could also increase the need for more engineers to manage this agent workforce. Today, AI Agents can equip engineers and make them faster, freer, and more focused on the work that actually moves the needle.

This is the future: enterprise SaaS that starts composable, stays governable, and evolves continuously to meet client needs with AI-augmented teams.

If you’re building this kind of Agentic system, or want to, let’s talk about it.

2025: The Year AI Transforms Work and Industry at Scale

Happy New Year! Here are my bet’s for 2025.

The rapid evolution of artificial intelligence is reshaping industries and redefining how we work. In 2025, I predict that several transformative trends will reshape the landscape of software development, industry specialization, and workforce dynamics. Below, we explore these predictions, provide additional insights, and examine the opportunities and risks that come with these changes.

1. Accelerating Improvements in LLMs

Large Language Models (LLMs) will continue to push the boundaries of what AI can do. With advancements in transformer, SSM, and other architectures, fine-tuning, and multimodal learning, LLMs will deliver faster, more accurate, and contextually rich results. We may even see an new architecture that pushes LLMs ahead even faster.

Opportunities

  • Enhanced productivity and creativity in tasks like content creation, research, and customer interaction.
  • Multimodal capabilities enabling seamless integration of text, images, and even video into workflows.

Challenges

  • Ethical concerns around misuse, bias, and misinformation.
  • Increased compute requirements may exacerbate energy consumption concerns.

2. Smaller Models, Big Potential

Compact LLMs leveraging distillation, pruning, and quantization will achieve near-parity with today’s largest models. This shift will democratize AI, making it accessible for edge devices, IoT applications, and industries with limited compute resources.

Opportunities

  • Cost-effective AI solutions for SMBs.
  • Expanding AI’s footprint into rural and underserved areas via low-power devices.

Challenges

  • Balancing efficiency with accuracy in critical applications like healthcare diagnostics or autonomous vehicles.

3. Industry-Specific AI Takes Center Stage

The rise of verticalized AI solutions tailored to specific industries, such as marketing, healthcare, finance, and legal, will dominate the market. These solutions will provide unparalleled domain expertise, driving faster ROI for businesses.

Opportunities

  • Precision and relevance in solving domain-specific challenges.
  • Increased trust in AI adoption as models demonstrate real-world impact.

Challenges

  • Heavy reliance on proprietary data could lead to monopolistic behavior or widen the gap between industry leaders and smaller players.

4. AI-Driven Development as the Norm

AI coding assistants like GitHub Copilot, Cursor, and Aider are already changing the way developers work. In 2025, AI will be fully integrated into development ecosystems, assisting with ideation, debugging, and even deployment.

Opportunities

  • Streamlined development cycles, reducing time to market.
  • Better accessibility for non-traditional developers, diversifying the talent pool.

Challenges

  • Over-reliance on AI could erode foundational coding skills.
  • Potential loss of creativity in problem-solving as AI-driven patterns become standardized.

5. Coding Becomes a Commodity

As AI handles the technical details of coding, the value of knowing how to code will diminish. Instead, the ability to guide AI assistants, define clear objectives, and solve high-level problems with code will become paramount. The experience to know when code is good or bad becomes more important than the just the ability to code.

Opportunities

  • Broader inclusion of non-technical professionals in tech projects.
  • Emergence of new roles focused on prompt engineering, strategy, and oversight.

Challenges

  • A potential skills gap for current developers who fail to adapt.
  • Risk of job displacement without sufficient upskilling initiatives.

6. Shippable AI-Driven Products with Minimal Oversight

In 2025, performant coding agents capable of turning product requirements into deployable solutions will emerge. These agents will handle well-defined scopes but may still require human oversight for complex or high-stakes applications.

Opportunities

  • Faster MVP development for startups.
  • Automated maintenance of legacy systems, freeing up human resources for innovation.

Challenges

  • Ensuring quality control and accountability in AI-generated products.
  • Difficulty in generalizing complex, nuanced requirements.

7. OpenAI Dominates but Faces Competition

OpenAI will maintain a stronghold on the market, but competition from Google, Meta, Anthropic, Cohere, and open-source ecosystems will heat up.

Opportunities

  • Diverse options for businesses to choose from, fostering innovation and reducing costs.
  • Strengthening open-source movements that promote transparency and collaboration.

Challenges

  • Risk of market fragmentation, making it harder for businesses to standardize solutions.
  • Proprietary dominance could limit interoperability.

8. AI Agencies Replace Traditional Agencies

Small AI agencies will rise to prominence, offering specialized services in automation, data modeling, and AI-driven marketing and development. These agencies will cater to SMBs, replacing traditional creative and technical firms.

Opportunities

  • Affordable, tailored AI solutions for local markets.
  • Innovation in personalized customer experiences and hyper-local strategies.

Challenges

  • Ethical dilemmas in hyper-targeted advertising.
  • Limited oversight in emerging markets where regulation lags behind.

9. Data Becomes the New Gold for Real This Time

Businesses will finally fully realize the value of their proprietary data, using it to train domain-specific models. Data-rich companies will dominate their industries by leveraging AI in unique and powerful ways.

Opportunities

  • Competitive differentiation through unique datasets.
  • Greater investment in data quality, security, and governance.

Challenges

  • Risk of data monopolies exacerbating inequality.
  • Increased cybersecurity threats targeting proprietary datasets.

Navigating Risks and Building for the Future

While the predictions for 2025 are exciting, they also come with challenges that require proactive measures:

  • Upskilling the Workforce: Governments, businesses, and educational institutions must collaborate to prepare the workforce for AI-driven roles.
  • Regulating Ethically: Establishing global standards for AI use will be crucial to avoid misuse and ensure equitable benefits.
  • Driving Sustainability: Advancements in AI must prioritize energy efficiency and sustainable practices.

As we move into 2025, businesses that embrace these changes while navigating risks will unlock unprecedented opportunities. The future of work and industry is brighter, more efficient, and deeply collaborative—driven by AI.

Are you ready to harness the power of AI for your business? Let’s talk about it and get your business ready for our AI future.