Innovation at Speed Requires Responsible Guardrails

The rush to adopt generative AI has created a paradox for engineering leaders in consulting and technology services: how do we innovate quickly without undermining trust? The recent Thomson Reuters forum on ethical AI adoption highlighted a critical point: innovation with AI must be paired with intentional ethical guardrails.

For leaders focused on emerging technology, this means designing adoption frameworks that allow teams to experiment at pace while ensuring that the speed of delivery never outpaces responsible use.

Responsible Does Not Mean Slow

Too often, “responsible” is interpreted as synonymous with “sluggish.” In reality, responsible AI adoption is about being thoughtful in how you build, embedding practices that reduce downstream risks and make innovation more scalable.

Consider two examples:

Model experimentation vs. deployment
A team can run multiple experiments in a sandbox, testing how a model performs against client scenarios. But before deployment, they must apply guardrails such as bias testing, data lineage tracking, and human-in-the-loop validation. These steps do not slow down delivery; they prevent costly rework and reputational damage later.
Prompt engineering at scale
Consultants often rush to deploy AI prompts directly into client workflows. By introducing lightweight governance—such as prompt testing frameworks, guidelines on sensitive data use, and automated logging, you create consistency. Teams can move just as fast, but with a higher level of confidence and trust.

Responsibility as a Product Opportunity

Using AI responsibly is not only a matter of compliance, it is a product opportunity. Clients increasingly expect trust and verification to be built into the services they adopt. For engineering leaders, the question becomes: are you considering verification as part of the product you are building and the services you are providing?

Examples where verification and trust become differentiators include:

OpenAI’s provenance efforts: With watermarking and provenance research, OpenAI is turning content authenticity into a feature, helping customers distinguish trusted outputs from manipulated ones.
Salesforce AI Trust Layer: Salesforce has embedded a Trust Layer for AI directly into its products, giving enterprise clients confidence that sensitive data is masked, logged, and auditable.
Microsoft’s Responsible AI tools: Microsoft provides built-in Responsible AI dashboards that allow teams to verify fairness, reliability, and transparency as part of the development lifecycle.
Google’s Fact-Check Explorer: By integrating fact-checking tools, Google is demonstrating how verification can be offered as a productized service to combat misinformation.

In each case, verification and trust are not afterthoughts. They are features that differentiate products and give customers confidence to scale adoption.

Guardrails Enable Speed

History offers parallels. In cloud adoption, the firms that moved fastest were not those who bypassed governance, but those who codified controls as reusable templates. Examples include AWS Control Tower guardrails, Azure security baselines, and compliance checklists. Far from slowing progress, these frameworks accelerated delivery because teams were not reinventing the wheel every time.

The same applies to AI. Guardrails like AI ethics boards, transparency dashboards, and standardized evaluation metrics are not bureaucratic hurdles. They are enablers that create a common language across engineering, legal, and business teams and allow innovation to scale.

Trust as the Multiplier

In consulting, speed without trust is a false economy. Clients will adopt AI-driven services only if they trust the integrity of the process. By embedding responsibility and verification into the innovation cycle, engineering leaders ensure that every breakthrough comes with the credibility clients demand.

Bottom Line

The message for engineering leaders is clear: responsible AI is not a constraint, it is a catalyst. When you integrate verification, transparency, and trust as core product features, you unlock both speed and scale.

My opinion is that in the next 12 to 24 months, responsibility will become one of the sharpest competitive differentiators in AI-enabled services. Firms that treat guardrails as optional will waste time fixing missteps, while those that design them as first-class product capabilities will win client confidence and move faster.

Being responsible is not about reducing velocity. It is about building once, building well, and building trust into every release. That is how innovation becomes sustainable, repeatable, and indispensable.

Turning Shadow IT into Forward-Facing Engineers

Across industries, shadow IT and citizen developers are no longer fringe activities; they are mainstream. The reason this is true is that the friction to get started has dropped to zero: with vibe coding, low-code platforms, and simply having access to ChatGPT, anyone can prototype solutions instantly. Business-side employees are building tools in Excel, Power Automate, Airtable, and other platforms to close gaps left by official systems. Instead of blocking these efforts, forward-looking organizations are embracing them and creating pathways for these employees to become forward-facing engineers who can deliver secure, scalable, client-ready solutions.

Why This Works

Bridge Business and Tech: Citizen developers deeply understand workflows and pain points. With the right training, they can translate business needs into technical delivery.
Accelerate Innovation: Harnessing shadow IT energy reduces bottlenecks and speeds delivery, without sacrificing governance.
Boost Engagement: Recognizing and investing in shadow IT talent motivates employees who are already passionate about problem-solving.
AI as an Equalizer: AI copilots and low-code tools lower the barrier to entry, making it easier for non-traditional technologists to scale their impact.

Risks to Manage

Security & Compliance: Shadow IT often overlooks governance. Retraining is essential.
Technical Debt: Quick wins can become brittle. Guardrails and code reviews are non-negotiable.
Cultural Resistance: Engineers may see this as encroachment. Clear roles and communication prevent friction.
Sustainability: The end goal is not just prototypes; it is enterprise-grade solutions that last.

The Playbook: From Shadow IT to Forward-Facing Engineers

The transition from shadow IT to forward-facing engineers is not a single leap; it is a guided journey. Each stage builds confidence, introduces new skills, and gradually shifts the employee’s mindset from quick fixes to enterprise-grade delivery. By laying out a clear progression, organizations can reduce risk while giving employees the structure they need to succeed.

Stage 1: Discovery & Assessment

This is about spotting hidden talent. Leaders should inventory shadow IT projects and identify who built them. The emphasis here is not on perfect code, but on curiosity, persistence, and problem-solving ability.

Inventory shadow IT solutions and identify their creators.
Assess aptitude based on curiosity and problem-solving.
Example: A bank’s operations team mapped its shadow macros before deciding who to upskill into engineering apprentices.

Stage 2: Foundations & Guardrails

Once talent is identified, they need a safe place to learn. Provide basic training, enterprise-approved platforms, and the guardrails to prevent compliance issues. This stage is about moving from “hacking things together” to “building responsibly.”

Train on secure coding, APIs, cloud, version control, and AI copilots.
Provide sandbox environments with enterprise controls.
Pair learners with senior mentors.
Example: Microsoft used Power Platform “fusion teams” to let business users build apps in sanctioned environments.

Stage 3: Structured Apprenticeship

Now comes immersion. Participants join product pods, experience agile rituals, and begin contributing to low-risk tasks. This apprenticeship gives them firsthand exposure to engineering culture and delivery standards.

Place candidates in agile product pods.
Assign low-risk features and bug fixes.
Example: At Capital One, former business analysts joined pods through internal engineering bootcamps, contributing to production code within six months.

Stage 4: Forward-Facing Engineering

At this stage, participants step into the spotlight. They start owning features, present solutions to clients, and earn recognition through internal certifications or badging. This is the pivot from being a learner to being a trusted contributor.

Provide recognition via certifications and badging.
Assign bounded features with client exposure.
Example: ServiceNow’s “CreatorCon” has highlighted employees who transitioned from shadow IT builders to client-facing solution engineers.

Stage 5: Leadership & Scaling

Finally, graduates help institutionalize the model. They mentor newcomers, run showcases, and measure success through metrics like migrated solutions and client satisfaction. This is where the cycle becomes self-sustaining.

Create a champions network where graduates mentor new entrants.
Establish a community of practice with showcases and hackathons.
Measure outcomes: number of solutions migrated, number of participants, client satisfaction.
Example: Deloitte formalized its citizen development program to scale across service lines, reducing tool duplication and client risk.

Pathways for Talent

Forward-facing engineering can also be a strong entry point for early-career engineers. Given the rapid impact of AI in the market, new engineers can gain confidence and real-world exposure by starting in these roles, where business context and AI-powered tools amplify their ability to contribute quickly. It provides a practical on-ramp to enterprise delivery while reinforcing secure, scalable practices.

Technical Track: Forward-facing engineer, automation specialist, platform engineer.
Product Track: Product owner, solution architect, business analyst.
Hybrid Track: Citizen developer + AI engineer, combining business know-how with AI copilots.

Keys to Success

Executive Sponsorship: Lends legitimacy and resources.
Visible Wins: Showcase transformations from shadow IT to enterprise product.
Continuous Learning: Invest in AI, cloud, and security enablement.
Cultural Alignment: Frame this as empowerment, not replacement.

Bottom Line

Turning shadow IT into forward-facing engineers transforms a risk into an innovation engine. Organizations like Microsoft, Capital One, and Deloitte have shown how structured programs unlock hidden talent. With the right framework, shadow IT contributors can evolve into enterprise-grade engineers who deliver secure, scalable, and client-facing solutions that drive competitive advantage.

🕸️ The Creepiest Part: The Curve Is Still Rising

Somewhere between the thunderclaps of innovation and the quiet hum of data centers, a strange chill fills the air. It’s not the wind. It’s not the ghosts. It’s the sound of AI adoption still accelerating long after everyone thought it might slow down.

Because if there’s one thing scarier than a monster rising from the lab,
it’s realizing it’s still growing.

⚡ The Laboratory of Limitless Growth

Deep inside a candlelit castle, lightning flashes across the stone walls. Test tubes bubble with neural networks, and electricity hums through old copper wires. At the center of it all, Frankenstein’s monster stands hunched over a chalkboard.

On it are three jagged lines, one for the Internet, one for Mobile, and one, glowing ominously in neon green, for AI.

Dr. Frankenstein peers at the data through cracked goggles.
“Impossible,” he mutters, flipping through a pile of parchment labeled St. Louis Fed and eMarketer. “Every curve must flatten eventually. Even the mobile revolution reached a plateau.”

The monster turns, bolts sparking from his neck. “But master,” he says in a low rumble, “the curve… it’s still rising.”

📈 The Data Doesn’t Die

The Count appears in the doorway, cape sweeping dramatically behind him.

Dracula, the eternal observer of technological transformation, carries a tablet glowing with eerie blue light.
“Ah, my dear doctor,” he says, “you’re still studying your creature? You forget, I’ve watched centuries of human obsession. Printing presses, telegraphs, the telephone, the internet. Each one rose, and then rested.”

He smirks, his fangs catching the candlelight.
“But this new creation, this Artificial Intelligence, it refuses to sleep.”

Frankenstein gestures at the graph.
“See here, Count. The Internet took a decade to reach 1 billion users. Mobile took about five. But generative AI? It’s measured in months.”

Dracula’s eyes narrow.
“Yes, I read that in the mortal scholars’ scrolls. The Federal Reserve Bank of St. Louis found AI adoption outpacing every major technology in history, even those bloodthirsty smartphones.”
(source)

He taps his screen, revealing another chart.
“And look here, eMarketer reports that generative AI reached 77.8 million users in two years, faster than the rise of smartphones or tablets.”
(source)

The monster grunts. “Even the villagers use it now. They ask it for recipes, resumes, love letters.”

Dracula raises an eyebrow. “And blood type analyses, perhaps?”

They both laugh, the uneasy laughter of men who realize the experiment has escaped the lab.

🧛 The Curse of Exponential Curiosity

Dracula glides to the window, staring out into the storm. “You see, Frankenstein, mortals cannot resist their reflection. Once they taste a new tool that speaks back, they feed it endlessly. Every prompt, every query, every midnight whisper, more data, more growth.”

“Like feeding a beast,” Frankenstein says.

“Exactly,” Dracula grins. “And this one feeds itself. Every interaction strengthens it. Every mistake teaches it. Even their fears become training data.”

He twirls his cape dramatically. “You’ve not created a machine, my dear doctor. You’ve unleashed an immortal.”

⚙️ Why the Curve Keeps Climbing

The monster scribbles four words on the wall: “No friction. Infinite feedback.”

“That’s the secret,” Frankenstein explains. “Unlike the old revolutions, electricity, mobile, internet, AI doesn’t require factories or towers. It scales through code, not concrete. The more people use it, the more valuable it becomes. That’s why the line won’t flatten.”

Dracula nods. “A perfect storm of seduction: zero cost to start, instant gratification, and endless novelty. Even I couldn’t design a better addiction.”

Together, they stare at the graph again.
The AI line doesn’t level off. It bends upward.

The candles flicker. Somewhere, a server farm hums, millions of GPUs glowing like a field of jack-o’-lanterns in the dark.

🦇 The Night Is Still Young

Dracula turns to Frankenstein. “Do you fear what comes next?”

The doctor sighs. “I fear what happens when the curve stops rising and starts replacing.”

Dracula’s grin fades. For a moment, the immortal looks mortal.
“Perhaps,” he says, “but revolutions always come with a price. The villagers feared your monster once, and now they fear their own machines.”

Lightning cracks across the sky.

“But remember, Doctor,” he continues, “progress is a creature that cannot be killed, only guided.”

The monster, now quiet, whispers, “Then let’s hope we are still the ones holding the switch.”

🎃 The Bottom Line

AI’s adoption curve hasn’t flattened because we’re still discovering what it is.
It’s not a single invention like the phone or the PC. It’s a living layer that spreads through APIs, integrates into tools, and evolves faster than we can measure.

The mobile revolution connected us.
The AI revolution is re-creating us.

And if the trendlines are right, we’re still only at the first act of this gothic tale. The lab lights are still on. The storm still rages.

And somewhere, in the distance, the curve is still rising.

Further Reading (for those who dare look deeper):

Trapdoor Decisions in Technology Leadership

Imagine walking down a corridor, step by step. Most steps are safe, but occasionally one of them collapses beneath you, sending you suddenly into a trapdoor. In leadership, especially technology leadership, “trapdoor decisions” are those choices that look innocuous or manageable at first, but once taken, are hard or impossible to reverse. The costs of reversal are very high. They are decisions with built-in asymmetric risk: small misstep, large fall.

Technology leaders are especially vulnerable to them because they constantly make decisions under uncertainty, with incomplete information, rapidly shifting contexts, and high stakes. You might choose a technology stack that seems promising, commit to a vendor, define a product architecture, hire certain roles and titles, or set norms for data governance or AI adoption. Any of those might become a trapdoor decision if you realize later that what you committed to locks you in, causes unexpected negative consequences, or limits future options severely.

With the recent paradigm shift brought by AI, especially generative AI and large-scale machine learning, the frequency, complexity, and severity of these trapdoors has increased. There are more unknowns. The tools are powerful and seductive. The incentives (first-mover advantage, cost savings, efficiency, competitive pressure) push leaders toward making decisions quickly, sometimes prematurely. AI also introduces risks of bias, automation errors, ethical lapses, regulatory backlash, and data privacy problems. All of these can magnify what would otherwise be a modest misstep into a crisis.

Why Trapdoor Decisions Are Tricky

Some of the features that make trapdoor decisions especially hard:

Irreversibility: Once you commit, and especially once others have aligned with you (teams, customers, vendors), undoing becomes costly in money, reputation, or lost time.
Hidden downstream effects: Something seems small but interacts with other decisions or systems later in ways you did not foresee.
Fog of uncertainty: You usually do not have full data or good models, especially for newer AI technologies. You are often guessing about future constraints, regulatory regimes, ethical norms, or technology performance.
Psychological and organizational biases: Sunk cost, fear of missing out, confirmation bias, leadership peer pressure, and incentives to move fast all push toward making premature commitments.
Exponential stakes: AI can amplify both upside and downside. A model that works may scale quickly, while one that is flawed may scale widely and cause harm at scale.

AI Creates More Trapdoors More Often

Here are some specific ways AI increases trapdoor risk:

Vendor lock-in with AI platforms and models. Choosing a particular AI vendor, model architecture, data platform, or approach (proprietary versus open) can create lock-in. Early adopters of closed models may later find migration difficult.
Data commitments and pipelines. Once you decide what data to collect, how to store it, and how to process it, those pipelines often get baked in. Later changes are expensive. Privacy, security, and regulatory compliance decisions made early can also become liabilities once laws change.
Regulatory and ethical misalignment. AI strategies may conflict with evolving requirements for privacy, fairness, and explainability. If you deprioritize explainability or human oversight, you may find yourself in regulatory trouble or suffer reputational damage later.
Automation decisions. Deciding what to automate versus what to leave human-in-the-loop can create traps. If you delegate too much to AI, you may inadvertently remove human judgment from critical spots.
Cultural and organizational buy-in thresholds. When leaders let AI tools influence major decisions without building culture and process around critical evaluation, organizations may become over-reliant and lose the ability to question or audit those tools.
Ethical and bias traps. AI systems have bias. If you commit to a model that works today but exhibits latent bias, harm may emerge later as usage grows.
Speed versus security trade-offs. Pressure to deploy quickly may cause leaders to skip due diligence or testing. In AI, this can mean unpredictable behavior, vulnerabilities, or privacy leaks in production.
Trust and decision delegation traps. AI can produce plausible output that looks convincing even when the assumptions are flawed. Leaders who trust too much without sufficient skepticism risk being misled.

Examples

A company picks a proprietary large-language model API for natural language tools. Early cost and performance are acceptable, but later as regulation shifts (for example, demands for explainability, data residency, and auditing), the proprietary black box becomes a burden.
An industrial manufacturer rushed into applying AI to predictive maintenance without ensuring the quality or completeness of sensor data and human-generated operational data. The AI model gave unreliable alerts, operators did not trust it, and the system was abandoned.
A tech firm automated global pricing using ML models without considering local market regulations or compliance. Once launched, they faced regulatory backlash and costly reversals.
An organization underestimated the ethical implications of generative AI and failed to build guardrails. Later it suffered reputational damage when misuse, such as deep fakes or AI hallucinations, caused harm.

A Framework for Navigating Trapdoor Decisions

To make better decisions in environments filled with trapdoors, especially with AI, technology leaders can follow a structured framework.

Stage	Key Questions / Activities	Purpose
1. Identify Potential Trapdoors Early	• What decisions being considered are irreversible or very hard to reverse?• What commitments are being made (financial, architectural, vendor, data, ethical)?• What downstream dependencies might amplify impacts?• What regulatory, compliance, or ethical constraints are foreseeable or likely to shift?• What are the unknowns (data quality, model behavior, deployment environment)?	To bring to light what can go wrong, what you are locking in, and where the risks lie.
2. Evaluate Impact versus Optionality	• How big is the upside, and how big is the downside if things go wrong?• How much flexibility does this decision leave you? Is the architecture modular? Is vendor lock-in possible? Can you switch course?• What cost and time are required to reverse or adjust?• How likely are regulatory, ethical, or technical changes that could make this decision problematic later?	To balance between pursuing advantage and taking on excessive risk. Sometimes trapdoors are worth stepping through, but only knowingly and with mitigations.
3. Build in Guardrails and Phased Commitments	• Can you make a minimum viable commitment (pilot, phased rollout) rather than full scale from Day 0?• Can you design for rollback, modularity, or escape (vendor neutral, open standards)?• Can you instrument monitoring, auditing, and governance (bias, privacy, errors)?• What human oversight and checkpoints are needed?	To reduce risk, detect early signs of trouble, and preserve ability to change course.
4. Incorporate Diverse Perspectives and Challenge Biases	• Who is around the decision table? Have you included legal, ethics, operations, customer, and security experts?• Are decision biases or groupthink at play?• Have you stress-tested assumptions about data, laws, or public sentiment?	To avoid blind spots and ensure risk is considered from multiple angles.
5. Monitor, Review, and Be Ready to Reverse or Adjust	• After deployment, collect data on outcomes, unintended consequences, and feedback.• Set metrics and triggers for when things are going badly.• Maintain escape plans such as pivoting, rollback, or vendor change.• Build a culture that does not punish change or admitting mistakes.	Because even well-designed decisions may show problems in practice. Responsiveness can turn a trapdoor into a learning opportunity.

Thoughts

Trapdoor decisions are not always avoidable. Some of the riskiest choices are also the ones that can produce the greatest advantage. AI has increased both the number of decision points and the speed at which choices must be made, which means more opportunities to misstep.

For technology leaders, the goal is not to become paralyzed by fear of trapdoors, but to become more skilled at seeing them ahead of time, designing decision pathways that preserve optionality, embedding oversight and ethics, and being ready to adapt.

The Role of the Directly Responsible Individual (DRI) in Modern Product Development

Why This Matters to Me

I have been in too many product discussions where accountability was fuzzy. Everyone agreed something mattered, but no one owned it. Work stalled, deadlines slipped, and frustration grew. I have also seen the opposite, projects where one person stepped up, claimed ownership, and pushed it forward.

That is why the Directly Responsible Individual (DRI) matters. It is more than a process borrowed from Apple or GitLab. It is a mindset shift toward empowerment and clarity.

What Is a DRI?

A DRI is the single person accountable for a project, decision, or outcome. They may not do all the work, but they ensure it gets done. Steve Jobs made the practice famous at Apple, where every important task had a DRI so ownership was never in doubt. (handbook.gitlab.com, bitesizelearning.co.uk)

In my experience, this clarity is often the difference between projects that deliver and those that linger.

Strengths and Weaknesses

The DRI model works because it removes ambiguity. With a clear owner, decisions move faster, resources are coordinated, and teams feel empowered. Assigning someone as a DRI is a signal of trust: we believe you can make this happen. (tettra.com)

The risks are real too. A DRI without proper authority can be set up to fail. Too much weight on one individual can stifle collaboration or lead to burnout. And if organizations treat the role as a label without substance, it quickly collapses. (levelshealth.com, dbmteam.com)

Examples in Practice

GitLab: Embeds DRIs across the organization, with clear documentation and real authority. (GitLab Handbook)
Levels Health: Uses DRIs in its remote-first culture, often as volunteers, supported by “buddies” and documentation. (Levels Blog)
Coda: Assigns DRIs or “drivers” for OKRs and pairs them with sponsors for balance. (Coda Blog)

The lesson is clear. DRIs succeed when paired with support and clear scope. They fail when given responsibility without authority.

Rolling Out DRIs

Adopting DRIs is a cultural shift, not just a process tweak. Some organizations roll them out gradually, starting with a few high-visibility initiatives. Others go all in at once. I lean toward gradual adoption. It builds confidence and proves impact before scaling.

Expect the early days to feel uncomfortable. Accountability brings clarity but also pressure. Some thrive, others resist. Over time, the culture shifts and momentum builds.

Change management matters. Leaders must explain why DRIs exist, provide support structures like sponsors, and create psychological safety. If failure leads to punishment, no one will volunteer.

The Clash with Command-and-Control IT

The DRI model often collides with the command-and-control style of traditional enterprise IT. Command-and-control relies on centralized approvals and shared accountability. The DRI approach decentralizes decisions and concentrates accountability.

I believe organizations that cling to command-and-control will fall behind. The only path forward is to create space for DRIs in product teams while still meeting enterprise compliance needs.

How AI Is Shaping DRIs

AI is becoming a force multiplier for DRIs. It can track progress, surface risks, and summarize input, giving individuals more time to focus on outcomes. But accountability cannot be outsourced to an algorithm. AI should make the DRI role easier, not weaker.

Empowerment and Conclusion

At its core, the DRI model is about empowerment. When someone is trusted with ownership, they rise to the challenge. They move faster, make decisions with confidence, and inspire their teams. I have seen people flourish under this model once they are given the chance.

For senior leaders, the next steps are clear. Identify accountability gaps, assign DRIs to a few strategic initiatives, and make those assignments visible. Pair them with sponsors, support them with AI, and commit publicly to backing them.

If you want empowered teams, faster results, and less ambiguity, DRIs are one of the most effective levers available. Those that embrace them will build stronger cultures of ownership. Those that resist will remain stuck in command and control. I know which side I want to be on.

Why DIY: A ChatGPT Wrapper Isn’t the Best Enterprise Strategy

TL;DR: The Buy vs Build

Challenge	Build (DIY Wrapper)	Buy (Enterprise Solution)
Cost	Tens to hundreds of thousands in build plus ongoing maintenance (applifylab.com, softermii.com, medium.com)	Predictable subscription model with updates and support
Security	Vulnerable to prompt injection, data leaks, and evolving threats (en.wikipedia.org, wired.com, wsj.com)	Enterprise-grade safeguards built in such as encryption, RBAC, and monitoring
Reward	Limited differentiation and fragile ROI	Faster time to value, scalable, and secure

Do not fall for the trap of thinking “we are different” or “we can do this better with our framework.” Building these wrapper experiences has become the core product that multi-billion-dollar model makers are selling. If this is an internal solution, think very carefully before taking that path. Unless your wrapper directly connects to a true market differentiator, it is almost always wasted effort. And even then, ask whether it can simply be implemented through a GPT or an MCP tool that already exists in commercial alternatives like Microsoft Copilot, Google Gemini, or ChatGPT Enterprise.

This is a textbook example of a modern buy vs build decision. On paper, building a ChatGPT wrapper looks straightforward, it’s just an API after all right. In practice, the costs and risks far outweigh the benefits compared to buying a purpose-built enterprise solution.

Don’t fall for the trap that “we are different” or “we can do this better with our framework” as building these experiences have become the core experience these multi-billion dollar model makers are now selling. If this is an internal solution, thing hard before falling for this trap. Unless this is somehow linked to your market differentiator. Even then think can this simply be a GPT or a MCP tool used by a commercial alternative like Co-Pilot, Gemini, or ChatGTP enterprise.

1. High Costs Upfront with Diminishing Returns

Even a seemingly modest AI wrapper quickly escalates into a significant investment. According to ApplifyLab, a basic AI wrapper app often costs $10,000 to $30,000, while a mid-tier solution ranges from $30,000 to $75,000, and a full enterprise-level implementation can exceed $75,000 to $200,000+, excluding ongoing costs like infrastructure, CI/CD, and maintenance (applifylab.com).

Industry-wide estimates suggest that launching complete AI-powered software, particularly in sectors such as fintech, logistics, or healthcare, can cost anywhere from $100,000 to $800,000+, driven by compliance, security, robust pipelines, and integration overhead (softermii.com).

Even just a proof-of-concept (POC) to test value can run $50,000 to $150,000 with no guarantee of ROI (medium.com).

Buy vs Build Takeaway: By the time your wrapper is ready for production, the cost-to-benefit ratio often collapses compared to simply adopting an enterprise-ready platform.

2. Security Risks with Low Visibility and High Stakes

DIY wrappers also tend to fall short on enterprise-grade security.

Prompt Injection Vulnerabilities
LLMs are inherently vulnerable to prompt injection attacks where crafted inputs (even hidden in documents or websites) can manipulate AI behavior or expose sensitive data. OWASP has flagged prompt injection as the top risk in its 2025 LLM Applications report (en.wikipedia.org).
Advanced variations, such as prompt-to-SQL injection, can compromise databases or trigger unauthorized actions via middleware such as LangChain (arxiv.org).
Real-world cases have already shown indirect prompt injection manipulating GPT-powered systems such as Bing chat (arxiv.org).
Custom GPT Leaks
OpenAI’s custom “GPTs” have been shown to leak initialization instructions and uploaded files through basic prompt injection, even by non-experts. Researchers easily extracted core data with “surprisingly straightforward” prompts (wired.com).
Broader LLM Security Risks
Generative AI systems are now a target for malicious actors. Researchers have even demonstrated covert “AI worms” capable of infiltrating systems and exfiltrating data through generative agents (wired.com, wsj.com).
More broadly, the WSJ notes that LLMs’ open-ended nature makes them susceptible to data exposure, manipulation, and reliability problems (wsj.com).

Building your own ChatGPT wrapper may feel like innovation, but it often ends up as a costly distraction that delivers little competitive advantage. Buying enterprise-ready solutions provides scale, security, and speed while allowing your team to focus on higher-value work. In the modern AI landscape, where risks are growing and the pace of change is accelerating, this is one of the clearest examples of why buy often beats build.

#AI #DigitalTransformation #CTO

The AI Crossroads: Why Professional Services Must Rethink Their DNA to Compete

Ben Thompson’s Paradigm Shifts and the Winner’s Curse highlights a brutal truth: the very strengths that make incumbents dominant in one era often become the shackles that hold them back in the next. This is not just a lesson in tech history. It is a warning flare for today’s professional services leaders.

For decades, consulting, legal, and advisory firms have built empires on human capital, billable hours, and hard-earned reputations. A new breed of competitors is emerging. These are AI-native professional services firms, built from the ground up with algorithms rather than org charts as the core engine. They do not play by your rules.

Real-World Examples: How AI Is Already Impacting Professional Services

McKinsey & Company has deployed around 12,000 AI agents to assist consultants with tasks like data analysis and presentation preparation, helping them shift toward outcome-based work, which now makes up about 25% of their revenue. LexisNexis, Wall Street Journal
EY launched its EY.ai Agentic Platform, deploying 150 AI agents to support 80,000 tax professionals with tasks like data collection and compliance. The firm sees AI as a productivity enhancer that could enable growth without cutting headcount. Business Insider
PwC is reworking training for junior accountants who now oversee AI-run audit tasks rather than execute routine work. They have also introduced “assurance for AI” services to help clients manage responsible AI use. Business Insider
Crete Professionals Alliance, backed by Thrive Capital, plans to invest $500 million in AI-powered roll-ups, integrating tools for data mapping and audit memo writing to enhance accounting efficiency. Reuters
UBS is focusing AI efforts on boosting productivity, with about 60% of its efforts targeting onboarding, KYC, and internal chatbots. Their internal AI assistant “Red” is now used by 52,000 employees. Financial News London
Legal sector: Top Australian law firms, including Baker McKenzie and Clayton Utz, use generative AI tools such as Harvey and RelativityOne for contract analysis and legal research. Human lawyers review and validate all AI outputs. The Australian
A Reuters podcast highlights how AI is reducing time-consuming tasks such as research across law, audit, and consulting, but also threatens traditional billable-hour models. Reuters

The Paradigm Shift: From Human Hours to Autonomous Intelligence

This is the moment when the rules change. For decades, the traditional model has run on human expertise applied client by client and hour by hour. Much of that work has been presented as highly bespoke and uniquely tailored, but in reality it often draws from pre-existing playbooks, templates, and solution sets that are lightly customized for each engagement. The perception of deep customization has been part of the value proposition, even when the underlying methods are largely standardized.

AI is beginning to break this illusion. The shift is away from labor-intensive delivery, often masked as handcrafted expertise, toward scalable agent-based autonomous intelligence. Instead of a team of humans manually adapting familiar solutions, AI agents can ingest a client’s specific context, synthesize relevant patterns from vast data sets, and generate responses or solutions that are genuinely unique in structure, scope, and speed.

In this new model, scalability is not about hiring more associates to serve more clients. It is about orchestrating fleets of specialized AI agents that can operate in parallel, adapt instantly to new inputs, and continuously improve as they learn from each engagement. The economics and the client experience both change. Solutions arrive faster, are more precisely aligned to the problem at hand, and can be iterated in real time rather than across billing cycles.

Traditional + AI vs. AI-Native: A Side-by-Side Look

Dimension	Traditional Firm Using AI	AI-Native Firm
Core Model	AI enhances human work	AI is the engine, with humans as supervisors
Client Delivery	AI supports humans in research and drafting	AI produces deliverables, humans provide trust and context
Pricing	Billable hours with some fixed-fee experimentation	Subscription, outcome- or usage-based from day one
Talent	AI skills added to human-led roles	Roles around AI system design, governance, integration
Scalability	Capped by human capacity	Scaled by compute power and data access
Culture	Risk-averse and legacy-bound	Experiment-driven, nimble, innovation-focused

A traditional firm “with AI” remains tied to its legacy model. An AI-native firm has engineered its escape from that orbit entirely.

Why Change Is Not Optional

Disruption theory shows incumbents fall not because they cannot see the future, but because acting hurts their current model. The billable-hour structure is a prime example. AI reducing junior hours hurts near-term economics even if the long-term upside is massive.

Delay is dangerous. AI-native firms may start small, but they improve fast and climb the value chain rapidly. By the time they rival traditional firms in quality, their cost base will be so much lower that competing feels like swimming upstream.

The Playbook for Leaders

If you are part of the C-suite in a professional services firm, you have a choice: treat AI as a tool to make the old model faster, or make it the foundation for a new model. That means:

Reimagine roles so humans emphasize judgment, trust, and strategic creativity.
Shift pricing to reflect outcomes delivered, not hours spent.
Build internal AI-native teams that can move fast and ship without legacy constraints.
Own AI governance and ethics as a competitive differentiator.

The Inflection Point

The next decade will test whether traditional firms can compete with those born into the AI era. The advantages are there. Established firms bring trust, client relationships, and domain expertise. AI-native challengers bring speed, scalability, and cost-efficiency.

The winners will be those who fuse the trust and insight of the old world with the scale and velocity of the new. Standing still is not an option.

Strategic Planning vs. Strategic Actions: The Ultimate Balancing Act

Let’s be blunt: If you are a technology leader with a brilliant strategy deck but nothing shipping, you are a fraud. If you are pumping out features without a clear strategy, you are gambling with other people’s money. The uncomfortable truth is that in tech leadership, vision without execution is delusion, and execution without vision is chaos.

Think about the companies we have watched implode. Kodak literally invented the digital camera but failed to commit to shifting their business model in time (Investopedia). Blockbuster had a roadmap for streaming before Netflix took off but never acted decisively, choosing comfort over speed. Their strategies looked great on paper right up until the moment they became cautionary tales.

The reverse problem of being all action and no plan is just as dangerous. Teams that constantly chase shiny objects, launch half-baked features, or pivot every few months might look busy, but they are building on quicksand. Yes, they might get lucky once or twice, but luck does not scale. Without a coherent plan, every success is an accident waiting to be reversed.

The leaders who get it right treat plans and actions as inseparable. Procter & Gamble’s OGSM framework aligns global teams on objectives, strategies, and measurable actions (Wikipedia). The Cascade Model starts with vision and values, then connects them directly to KPIs and delivery timelines (Cascade). Best Buy’s turnaround in the early 2010s, with price matching Amazon, investing in in-store experience, and expanding services, worked because it was both a clear plan and a relentless execution machine (ClearPoint Strategy). Nike’s 2021–2025 roadmap is another example, with 29 public targets supported by measurable actions (SME Strategy).

If you are leading tech without both vision and velocity, you are either drifting or spinning in place. Neither wins markets. Your job is not just to make a plan, it is to make sure the plan lives through your delivery cadence, your roadmap decisions, and your metrics.

Applying the Balance to AI Adoption

The AI revolution is no longer approaching, it is here. Nearly half of Fortune 1000 companies have embedded AI into workflows and products, shifting from proving its value to scaling it across the organization (AP News). But AI adoption demands more than flashy pilots. It requires the same balance of strategic planning and relentless execution.

Many organizations are experiencing AI creep through grassroots experiments. A recent survey found that 72% of employees using AI report saving time weekly, yet most businesses still lack a formal AI strategy (TechRadar). This gap is risky. Spontaneous adoption delivers early wins, but without an intentional rollout these remain one-off tricks rather than transformative advances.

The shift is forcing companies to formalize leadership. Chief AI Officers are now often reporting directly to CEOs to steer AI strategy, manage risks, and align use cases with business priorities (The Times). Innovators like S&P Global are mandating AI training, moving developer AI use from 7% to 33% of code generation in months, and building “Grounding Agents” for autonomous research on proprietary data (Business Insider).

Steering AI at scale requires a framework, not spontaneity. Gartner’s AI roadmap outlines seven essential workstreams, from strategy, governance, and data to talent, engineering, and value portfolios, so leaders can prioritize AI with clarity and sequence (Gartner). AI adoption also succeeds only when trust, transparency, and cultural fit are embedded, particularly around fairness, peer validation, and organizational norms (Wendy Hirsch).

Introducing AI into your product development process without a strategic scaffold is like dropping nitro on a house of cards. You might move fast, but any misalignment, governance gap, or cultural mismatch will bring it all down. The antidote is to anchor AI initiatives in concrete business outcomes, empower cross-functional AI working groups, invest in upskilling and transparency, and govern with clear risk guardrails and metrics.

Your Next Action

In your experience, which derails AI transformation faster: lack of strategic planning or reckless execution without governance? Share the AI initiatives that flamed out or flipped your company upside down, and let us unpack what separates legendary AI adoption from another shiny pilot. Because in tech leadership, if vision and velocity are not joined in your AI strategy, you are either running illusions or waiting for a miracle.

One-Word Checkout: The Small Ritual That Cuts Through Complexity and Accelerates Product Development

Why Meetings Need a Cleaner Landing

Even the best‑run product teams can let a meeting drift at the end. Action items blur, emotional undercurrents go unspoken, and complexity silently compounds. A concise closing ritual refocuses the group and signals psychological completion.

What the One‑Word Checkout Is

The one‑word checkout is a brief closing round in which each attendee offers a single word that captures their current state of mind or key takeaway;“aligned,” “blocked,” “energized,” “unclear,” “optimistic,” and so on. This micro‑ritual forces clarity, surfaces concerns that might otherwise stay hidden, and guarantees every voice is acknowledged. Embedding the checkout into recurring meetings builds shared situational awareness, spots misalignment early, and stops complexity before it cascades into rework.

How One Word Tames Complexity

Forces Synthesis
Limiting expression to one word pushes each person to distill the swirl of discussion into its essence, reducing cognitive load for everyone listening.
Surfaces Hidden Signals
Words like “anxious” or “lost” flag misalignment that polite silence might otherwise hide. Early detection prevents rework later.
Creates Shared Memory
A rapid round of striking words is easier to recall than lengthy recap notes, strengthening collective understanding of the meeting’s outcome.
Builds Psychological Safety
Knowing that every voice will be heard, even briefly, reinforces inclusion and encourages honest feedback in future sessions.

When to Use One‑Word Checkout

Apply this technique in meetings where fast alignment and shared ownership are critical; examples include daily stand‑ups, backlog refinement, sprint planning, design reviews, and cross‑functional workshops. Use it when the group is small enough that everyone can speak within a minute or two (typically up to 15 people) and when the meeting’s goal is collaborative decision‑making or problem‑solving. The ritual works best once psychological safety is reasonably high, allowing participants to choose honest words without fear of judgment.

When Not to Use One‑Word Checkout

Skip the ritual in large broadcast‑style meetings, webinars, or executive briefings where interaction is minimal and time is tightly scripted. Avoid it during urgent incident calls or crisis huddles that require rapid task execution rather than reflection. It is also less helpful in purely asynchronous updates; in those cases, a written recap or status board is clearer. Finally, do not force the exercise if the team’s psychological safety is still forming; a superficial round of safe words can mask real concerns and erode trust.

Direct Impact on Product Development

Challenge in Product Work	One‑Word Checkout Benefit
Requirements creep	“Unclear” highlights ambiguity before it snowballs into code changes.
Decision latency	“Decided” signals closure and lets engineering start immediately.
Team morale dip	“Drained” prompts leaders to adjust workload or priorities.
Stakeholder misalignment	“Concerned” from a key stakeholder triggers follow‑up without derailing the agenda.

Implementation Guide

Set the Rule
At the first meeting, explain that checkout words must be one word. No qualifiers or back‑stories.
Go Last as the Facilitator
Model brevity and authenticity. Your word sets the tone for future candor.
Capture the Words
A rotating scribe adds the checkout words to the meeting notes. Over time you will see trends such as morale swings or recurring clarity issues.
Review in Retros
In sprint retrospectives, display a word cloud from the last two weeks. Ask the team what patterns they notice and what should change.
Measure the Effect
Track two metrics before and after adopting the ritual:
• Decision cycle time (idea to committed backlog item)
• Rework percentage (stories reopened or bugs logged against completed work)
Many teams see a 10‑15 percent drop in rework within a quarter because misalignment is caught earlier.

Case Snapshot: FinTech Platform Team

A 12‑person squad building a payments API introduced one‑word checkout at every stand‑up and planning session. Within six weeks:

Average user‑story clarification time fell from three days to same day.
Reopened tickets dropped by 18% quarter over quarter.
Team eNPS rose from 54 to 68, driven by higher psychological safety scores.

The engineering manager noted: “When two people said ‘confused’ back‑to‑back, we paused, clarified the acceptance criteria, and avoided a sprint’s worth of backtracking.”

Tips to Keep It Sharp

Ban Repeat Words in the same round to encourage thoughtful reflection.
Watch for Outliers. A single “frustrated” amid nine “aligned” words is a gift; dig in privately.
Avoid Judgment during the round. Follow‑up happens after, not during checkout.

Alternatives to One‑Word Checkout

If the one‑word checkout feels forced or does not fit the meeting style, consider other concise alignment rituals. A Fist to Five vote lets participants raise zero to five fingers to show confidence in a decision; low scores prompt clarification. A traffic‑light round—green, yellow, red—quickly signals risk and readiness. A Plus/Delta close captures one positive and one improvement idea from everyone, fueling continuous improvement without a full retrospective. Choose the ritual that best matches your team’s culture, time constraints, and psychological safety level.

Thoughts

Complexity in product development rarely explodes all at once. It seeps in through unclear requirements, unvoiced concerns, and meetings that end without closure. The one‑word checkout is a two‑minute ritual that uncovers hidden complexity, strengthens alignment, and keeps product momentum high. Small habit, big payoff.

Try it out

Try the ritual in your next roadmap meeting. Collect the words for a month and review the patterns with your team. You will likely find faster decisions, fewer surprises, and a clearer path to shipping great products.

#ProductStrategy #TeamRituals #CTO

Widen Your AI Surface Area and Watch the Returns Compound

Cate Hall’s surface-area thesis is simple: serendipity = doing × telling. The more experiments you run and the more publicly you share the lessons, the more good luck finds you. (usefulfictions.substack.com)

Generative AI is the ultimate surface-area amplifier. Models get cheaper, new use cases emerge weekly, and early wins snowball once word spreads. Below is a playbook, rooted in real-world data, for technology leaders who want to stay ahead of the AI wave and translate that edge into concrete gains for their organizations and their own careers.

1. Run More (and Smaller) Experiments

Tactic	Recent proof-point
Quarterly hack-days with a “ship in 24 hours” rule.	Google Cloud’s Agentic AI Day gathered 2,000+ developers who built 700 prototypes in 30 hours, earning a Guinness World Record and seeding multiple production pilots. (blog.google, The Times of India)
30-day “two-pizza” squads on nagging pain points.	Walmart’s internal “Associate” and “Developer” super-agents started as 30-day tiger-teams and are now rolling out across stores and supply-chain tools. (Reuters, Forbes)

Organizational upside: frequent, low-cost trials de-risk big bets and surface unexpected wins early.
Career upside: you become the executive who can reliably turn “weekend hacks” into measurable ROI.

2. Create an Adoption Flywheel

“AI is only as powerful as the people behind it.” – Telstra AI team

Levers

Default-on pilots. Telstra rolled out “Ask Telstra” and “One Sentence Summary” to every frontline agent; 90% report time-savings and 20% fewer follow-up calls. (Microsoft)
Communities of practice. Weekly show-and-tell sessions let power users demo recipes, prompts, or dashboards.
Transparent metrics. Publish adoption, satisfaction, and hours-saved to neutralise fear and spark healthy competition.

Organizational upside: time-to-value shrinks, shadow-IT falls, and culture shifts from permission-based to experiment-by-default.
Career upside: you gain a track record for change management, a board-level differentiator.

3. Build Platforms, Not One-Offs

Platform move	Result
Expose reusable agent frameworks via internal APIs.	Walmart’s “Sparky” customer agent is just one of four AI “super-agents” that share common services, accelerating new use-case launches and supporting a target of 50% online sales within five years. (Reuters)
Offer no-code tooling to frontline staff.	Telstra’s agents let 10k+ service reps mine CRM history in seconds, boosting first-contact resolution and agent NPS. (Telstra.com, Microsoft)

Organizational upside: every new bot enriches a shared knowledge graph, compounding value.
Career upside: platform thinking signals enterprise-scale vision, which is catnip for CEO succession committees.

4. Broadcast Wins Relentlessly

“Doing” is only half the surface-area equation; the other half is telling:

Internal road-shows. Add Ten-minute demos into your team meetings.
External storytelling. Publish case studies or open-source prompt libraries to attract talent and partners.
Metric snapshots. Microsoft found Copilot adoption surged once leaders shared that 85% of employees use it daily and save up to 30% of analyst time. (Microsoft, The Official Microsoft Blog)

Organizational upside: shared vocabulary and proof accelerate cross-team reuse.
Career upside: your public narrative positions you as an industry voice, opening doors to keynote slots, advisory boards, and premium talent pipelines.

5. Quantify the Payoff

Outcome	Evidence you can quote tomorrow
Productivity	UK government Copilot trial: 26 minutes saved per employee per day across 14,500 staff. (Barron’s)
Client speed	Morgan Stanley advisors auto-generate meeting summaries and email drafts, freeing prep time for higher-margin advice. (Morgan Stanley)
Revenue	Walmart expects agentic commerce to accelerate its push to $300 B online revenue. (Reuters)

Use numbers like these to build cost-benefit cases and secure funding.

6. Personal Career Playbook

Focus Area	Action	Why It Pays Off
Public Credibility	Share what you learn, whether on LinkedIn, Github, YouTube, or other channel.	Consistently sharing insights brands you as a thought leader and attracts high-caliber talent.
Hands-On Insight	Pair with an engineer or data scientist for one sprint each quarter.	Staying close to the build process sharpens your intuition about real-world AI capabilities and constraints.
Continuous Learning	Commit to one AI-focused certification or course each year.	Ongoing education signals a growth mindset and keeps your expertise relevant in a fast-moving field.

Make your own luck

Boosting your AI surface area is not about chasing shiny tools. It is a disciplined loop of many small bets + aggressive storytelling. Organizations reap faster innovation, richer data moats, and happier talent. Leaders who orchestrate that loop accrue reputational capital that outlives any single technology cycle.

Start widening your surface area today, before the next wave passes you by.