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Disciplined Creativity

Sharing a solution that lives in your head with AI can be surprisingly difficult. But these two key principles have really helped me get closer to the results I strive to achieve.

It’s this idea of disciplined creativity.

Disciplined creativity is the sweet spot between structure and imagination. And AI thrives in the sweet spot between structure and imagination.

AI’s responses are creative, organic, yet articulate and structured. But the seemingly infinite space it operates within is too broad. The results include incorrect and hallucinogenic responses. So let’s define the space that AI must operate within. The closer it understands what’s in your head, the closer it can think with you.

This principle has changed the way I’ve been prompting my AIs. I don’t ask for solutions, I design the conditions that shape them and the outcome we want to achieve. I set boundaries, define intent, and give context.

When writing prompts I keep these two things in mind:

1. Restrictions breed creativity.

  • Give AI your constraints. Tell it what you don’t want. What it shouldn’t do. What you don’t want to see. Define the domain it should be operating within. Set the stage, the boundaries, the box it lives in.

2. Requirements reinforce discipline.

  • Next, what are the non-negotiables? The requirements of what must live in the box. Be explicit about what matters. This tells the AI the core components needed in your solution.

We’ve now set the stage and set the bar. Let’s let AI live in this space for a moment and it’ll Tetris your requirements into your restrictions. Be inspired by the output you receive.

And before you submit your prompt, end it with this:

Before you provide me a solution, ask me key questions that will make sure we are on the same page.

AI prompting is as much of an art as it is form. These two principles help define this art form.

A Rekindle with Humanity

The internet is one of humanity’s great achievements, if not the greatest. It has connected people in a way that brought us closer to each other. It taught us the beauty of different cultures. It changed the way we communicate and stay in touch. It helped create an entirely new type of entrepreneur. It made the location where we work agnostic.

It’s truly incredible to think about what the world was like before we had computers in our pockets. As we continue to evolve this connected experience, there’s a whole new frontier quickly overtaking the horizon.

AI is here, and it’s fucking good. In just a few short years, the way we think and generate content has been assisted through the lens of AI. This snowballing of technology is powerful. But there are some existential concerns happening alongside it.

Over the last 40 years, the internet has documented and collected more information about history, commerce, entertainment, photos and videos, psychology, and so much more.

AI is a powerful system that takes advantage of both advanced processing abilities and sophisticated algorithms. It can associate, compound, and execute ideas faster than what’s humanly possible.

This is an exciting future to think about, how we work and how we solve problems. AI is evolving so fast, and it’s getting better. It’s already at the point where it’s becoming impossible to know what a human created with or without AI. As AI increasingly trains on content created by other AI, it begins feeding itself. This recursive loop risks amplifying errors, reinforcing false narratives, and slowly skewing our shared sense of reality further from the truth.

This means the content you consume could be incorrect. It could easily be confused with reality. The digital world we live in could begin to disassociate from reality. The technology we created to better connect us could create such a fog of truth that we return to the offline world to reconnect. So much so that a return to the physical world could see a rebirth.

It’s going to be exhausting trying to decipher what’s real anymore. The more we return to the physical world, the more we’ll be connected again.

  • Live plays will help us appreciate the raw, true talent humans have.
  • Concerts will evoke emotion and energy that connect us through shared feelings.
  • Live sports will continue to give us something to cheer for together, along with an appreciation of what humans are physically capable of.
  • Board games and card games will give us casual, competitive fun in our own homes.
  • How we work will require a certain amount of in-person interaction.
  • Comedy shows and storytelling nights will remind us of timing, presence, and shared laughter.
  • Lectures, debates, and live discussions will bring nuance and depth that’s hard to replicate digitally.
  • Art galleries and museums will let us experience scale, texture, and intention in ways screens can’t reproduce.
  • Workshops and classes (cooking, woodworking, dance, fitness) will reinforce learning through doing, not consuming.
  • Community events and festivals will foster belonging through proximity and shared experience.
  • Spas and wellness retreats will offer a return to the body, creating grounding experiences that can’t be replicated or simulated digitally.
  • Face-to-face mentorship and apprenticeship will deepen trust, intuition, and tacit knowledge transfer.
  • Family meals and celebrations will remain anchors for connection, memory, and tradition.

Don’t get me wrong, I’m optimistic about how we’ll coexist with technology, but I don’t want us to lose what makes us human along the way. We will learn and mature in how we use this technology, and part of that maturity is learning what belongs online and what needs to be human to human. We will find a new renaissance by rekindling our connection to humanity.

What is Enterprise Software?

When you hear “enterprise software,” you may think of Fortune 500 giants with thousands of employees and complex hierarchies. But enterprise software isn’t defined by company size. It’s defined by how it’s used and more importantly, how flexible it can be configured to meet the unique needs of its users. “Enterprise” is not a customer segment, it’s a product capability. It’s not about company size; it’s about software pliability, scalability, and accountability.

We’ll break that down by:

  1. How features make software pliable, scalable, and accountable.
  2. How consulting makes enterprise software possible.
  3. How to prioritize and measure a SaaS platform on its enterprise maturity.

Enterprise Features: The Real Definition of Enterprise-Grade

Here’s a table outlining core features that signal a product is ready for enterprise use:

🔄 Collaboration & Workflow Management

Feature Why It Matters
Templates Streamlines recurring workflows, ensuring consistency.
Customizable Automations Enables logic like “if X happens, do Y”, flexible workflows without coding.
Change Request System Ensures sensitive changes are gated through approval and/or review.
WYSIWYG Editors Empowers non-technical users to create rich content (i.e. common formatting, link creation, file uploads, embeds, etc.) in records or documentation.
Favorites / Quick Access Enhances daily usability, particularly for power users navigating deep datasets.
Bulk Actions Boosts efficiency, ideal for operations teams who need to complete bulk actions (i.e. delete, edit or export) with larger datasets.
Searchable Dropdowns Reduces friction in filter selections when working with large or complex lists for further filtering of data.

🧩 Scalability & System Management

Feature Why It Matters
APIs Enables customers to extend the product and integrate with their stack.
Configurable Integrations (ETL) Supports data movement across systems with flexibility on mapping, frequency, and logic.
Batch Imports Simplifies initial setup and large-scale data migrations.
Tenant Management Supports orgs with multiple business units or sub-brands (parent-child relationships).
Environment Separation (Prod, Dev, QA) Helps enterprises safely test changes or onboard new teams.
Rate Limiting & API Throttling Prevents abuse and ensures platform performance under high load.
Soft Deletes / Archiving Enables reversible deletion for safety, or decluttering without losing historical data.

🤝 Customer Success & Administration

Feature Why It Matters
Custom Fields Allows modeling of domain-specific data, one customer’s “Job ID” might be another’s “Case #.”
Custom Tags Improves data organization and searchability with internal taxonomy.
In-App Training or Tours Supports onboarding at scale—especially helpful for decentralized organizations.
Third-Party Marketplace A third-party marketplace allows other organizations to build features into your system and sell them.

📊 Enterprise Reporting & Analytics

Feature Why It Matters
Custom Reports Empowers teams to define KPIs and analyze what matters to them.
Scheduled Reports / Subscriptions Automatically delivers recurring reports to stakeholders.
Role-Based Dashboards Tailors dashboards by function or seniority, execs see different info than field managers.
Saved Views Preserves complex filters and sorts for repeat access.
Shared Views and Reports Enables secure cross-team visibility without data leakage.
Advanced Search Allows sophisticated queries (i.e. quoted queries, positive/negative keyword searches, advanced filters, etc.) to navigate large datasets.
Exportability Supports external analysis and compliance documentation through CSV, PDF, etc.

🔐 Governance & Security

Feature Why It Matters
Roles and Permissions Controls access to features and data based on user responsibility.
Scopes and Hierarchies Reflects organizational structure, allowing access control by team, department, or geography.
SSO (Single Sign-On) Centralizes identity management via tools like Okta or Azure.
MFA (Multi-Factor Authentication) Adds an extra security layer, critical for compliance and IT policy alignment.
Field-Level Permissions Limits visibility/edit access at the field level, useful for sensitive data like salaries or personal info.
Data Retention Policies Controls how long data is stored to meet regulatory or internal policies.
Encryption at Rest/In Transit Ensures sensitive data is protected in storage and while being transferred.
Compliance Software that is compliant through common frameworks such as ISO and SOC 2 ensure security and stability.
IP Whitelisting / Network Restrictions Restrict access to certain IPs or networks for any interface to the system (i.e. app, API, etc.)
Audit Logs Tracks who changed what and when, down to individual field values.
Activity Logs Captures general user actions across the platform.
Change Logs for Configuration Shows historical config changes, critical for tracking unintended or unauthorized adjustments.

🌐 Localization & Compliance

Feature Why It Matters
I18N (Internationalization) Supports different languages, currencies, number/date formats, and measurement systems.
Time Zone Awareness Makes scheduling and reporting accurate across regions.
Region-Specific Compliance Modes Toggles features needed for GDPR, HIPAA, or CCPA (e.g., data access logs, cookie banners).
Language Overrides / Glossaries Lets orgs rename interface terms to align with internal vocabulary.

Grouping these features is what makes it enterprise

No single feature makes software “enterprise.” Enterprise happens in combinations:

  • Roles + Scopes + SSO/MFA = secure, compliant access management
  • Custom Fields + Reports + Views = tailored insights at scale
  • Templates + Automations + Tags = process standardization with flexibility
  • APIs + ETL + Imports/Exports = open data ecosystems
  • Audit Logs + Activity Logs + Permissions = trust and traceability

When software enables this kind of composition, it becomes more than a tool, it becomes infrastructure. It adapts to each customer’s world instead of asking them to adapt to yours.

Enterprise software features do the following:

  • Meets the security & compliance needs of IT and InfoSec teams
  • Delivers insights and analytics tailored to business outcomes
  • Facilitates collaboration and business process orchestration
  • Scales with the organizational complexity of your customers
  • Supports global teams with localization and regulatory readiness
  • Provides administrative tooling that eases rollout, training, and governance

Enterprise without consulting is a churn risk waiting to happen

Enterprise software doesn’t succeed on features alone, it succeeds when those features are understood, configured, and embedded into the way a business actually operates. That’s where consulting services come in. Because enterprise software is built to be pliable, it requires an intentional, guided approach to implementation. Consulting services bridge the gap between what the software can do and how it should be set up to support a customer’s unique workflows, policies, and strategic goals.

Customer success in the enterprise space hinges on accelerating time to value, ****helping customers realize impact sooner by aligning the system to their context. This can’t always be done through self-serve onboarding or a few help articles. It often requires a blend of subject matter expertise, change management, and technical configuration. A great consulting engagement doesn’t just deploy features; it delivers outcomes. It ensures the software fits the customer’s reality, drives adoption across teams, and sets the foundation for long-term success and scale. In this way, consulting is not just an add-on, it’s a multiplier of enterprise value.

Enterprise Software Is About Fit, Not Force

When you build for enterprise, you’re not just building for scale, you’re building for adaptability. True enterprise software bends to the way an organization works, not the other way around. Whether you’re selling to a 50-person firm or a 10,000-person organization, your software becomes “enterprise” the moment it can flex to their needs, connect to their systems, and operate securely and reliably in their environment. It’s not about how big the customer is. It’s about how tailored your product can be.

Because of its configurability and complexity, enterprise software often depends on consulting services to ensure successful implementation, especially when deep domain expertise is needed to align the platform with specialized workflows or vertical requirements. Exceptional pliability supported through consulting.

Enterprise SaaS Maturity Model

Now that we better understand what Enterprise SaaS means, let’s consider the maturity level and priority of features an organization will go through in this journey. The Enterprise SaaS Maturity Model redefines “enterprise-grade” not by customer size, but by the pliability, configurability, and extensibility of software. It outlines five progressive stages, each representing deeper capabilities that enable your product to adapt to complex organizational needs.

Level Maturity Stage Definition Key Features Typically Present
1 Basic SaaS Fit Designed for individual teams or SMBs with minimal config needs. Roles & Permissions, Basic Search, Exportability, Custom Fields
2 Configurable Foundations Adds customization and integrations that enable broader use across departments. Templates, Saved Views, Searchable Dropdowns, Batch Imports, APIs, Custom Tags
3 Operational Flexibility Supports multi-department use with controls, collaboration, and deep reporting. Scopes & Hierarchies, Shared Reports, Custom Reports, Audit Logs, Custom Automations, Task Assignment, Role-Based Dashboards
4 Enterprise-Ready Meets compliance, governance, and security requirements for large or regulated orgs. SSO, MFA, Field-Level Permissions, Data Retention, Encryption, Audit Trail Visuals, Approval Workflows, Delegated Admin
5 Global-Scale Extensibility Offers localization, sandbox environments, advanced integrations, and region-specific compliance. I18N, Time Zones, Region Modes, ETL Configs, Environment Separation, IP Whitelisting, Language Overrides, Rate Limiting, Tenant Management

The AI Software Developer

The role of a software developer has grown through layers of abstraction, evolving from writing commands tied to specific line numbers to using frameworks that handle state management and make garbage collection an after thought. Yet, for much of the past 80 years, programming has been seen as a cryptic, computer-nerd language—an instant license to fix every relative’s computer. (And maybe your coworker’s son’s friend’s dad’s computer too, but that might just be me.)

Every decade or so, a new major abstraction emerges, fundamentally changing the way developers work:

  • 1950s: Compiling to binary machine code with Assembly Language.
  • 1960s: Introducing loops, conditionals, and functions with High-Level Languages.
  • 1970s: Creating classes, objects, and inheritance with Object-Oriented Programming.
  • 1980s: Connecting computers to share information through The Internet.
  • 1990s: Enhancing visual experiences and file management with Integrated Development Environments (IDEs).
  • 2000s: Reusing others’ code through Package Managers.
  • 2010s: Providing generic, reusable starting points with Frameworks.
  • 2020s: Building business-critical apps using interfaces with No-Code Platforms.

But the next big layer of abstraction might truly be paradigm-shifting, fundamentally altering how we perceive what a software developer types into their device. What if this seismic shift blurs the lines between the language we use to communicate and the language we use to code?

It’s me, AI, I’m the problem, it’s me. The next software developer will rely on natural language (prompts) as a new “software language.”

But here’s the thing: natural language is messy. Unlike programming languages, where every comma and semicolon serves a purpose, human language is ambiguous, inconsistent, and sometimes downright nonsensical. This creates challenges for AI systems, particularly large language models (LLMs), which are trained on massive swaths of human language. These models inherit biases and occasionally hallucinate information—problems that stem from the messy, historical nature of human language itself. In a way, you could say we have a bug in our human language compiler. (lolz)

Yet when LLM’s are trained on code, the output needs to be specific. A great developer will know whether or not it will compile/execute and ultimately function as intended.  This means the ambiguity in AI generated code is not as forgiving.  It must be accurate. And to be accurate, it will require explicits.

This is where the future professional developer can truly shine. Developers will shift from being syntax-driven to being more explicit-driven. The techniques used to prompt AI to write code will become a framework and skill in their own right.

The ability to craft clear, unambiguous prompts will become a superpower. “Prompt engineering” will require a better discipline in telling a machine more of what not to do than what to do. Great prompts will require the developer to truly consider how to handle edge cases and not just focus on the happy paths. It will change the way we implement business logic.

AI isn’t a career shortcut—it’s a transformation in how we innovate. Developers will focus on bigger problems, design smarter systems, and create better interfaces. If you’re a seasoned developer with 20+ years of experience and this frustrates you, don’t let it. None of this would be possible without you. These layers of abstraction happen because of the brilliant work you’ve done. We all stand on the backs of giants and we thank you for your contribution to solving meaningful problems. As you evolve in your career, lean in to what makes a great software experience and consider how AI will write your code.

This next level of abstraction will undoubtedly be a massive shift in how new software applications are created and launched.

The AI Software Developer will be one of the hottest careers of the next decade.

Random Recordings #59

This is part of a series I call random recordings. You can learn more here: https://anthonymontalbano.com/random-recordings-1/

The following recordings are for November 2021:

  1. Humanity’s capacity for destruction is matched only by it’s capacity for love and empathy.
  2. Success is going from failure to failure without a loss of enthusiasm.
  3. The first step towards getting somewhere is to decide you’re not going to stay where you are.
  4. He who loses wealth loses much, he who loses a friend loses more, but he who loses courage loses all.
  5. Anyone who lives within their means suffers from a lack of imagination.

Random Recordings #57

This is part of a series I call random recordings. You can learn more here: https://anthonymontalbano.com/random-recordings-1/

The following recordings are for September 2021:

  1. The cost of being wrong is less than the cost of doing nothing.
  2. It’s always the start that requires the greatest effort.
  3. It gets easier every day, it gets a little easier, but you got to do it every day. That’s the hard part.
  4. Ideas are easy, implementation is hard.
  5. Learn from the mistakes of others, you can’t live long enough to make them yourself.

Random Recordings #55

This is part of a series I call random recordings. You can learn more here: https://anthonymontalbano.com/random-recordings-1/

The following recordings are for July 2021:

  1. Relationship building is key to being human. And that’s not just relationships with other humans.
  2. Don’t assume the worst thing is going to happen, becauase on the off chance it does, you’ll have to live through it twice. So why not do the inverse?
  3. Confidence is a funny thing. You have to somehow believe that the worst outcome simply won’t happen. Sometimes you have to do that while knowing for a fact that the worst outcome is happening, all at the same time. It’s a very interesting space to live in.