Forem: Scott McMahan

Your Teams Are Using AI in Silos and It's Quietly Killing Your Productivity

Scott McMahan — Fri, 17 Apr 2026 13:30:28 +0000

The Problem Nobody Planned For

Most organizations have rolled out AI tools by now. The problem is that engineering adopted one set of tools, marketing adopted another, and finance is doing something else entirely. Nobody planned for how these tools would work together across departments, and that gap is costing teams more than they realize.

What an AI Collaboration Model Actually Is

An AI collaboration model is essentially a blueprint for how humans and AI divide tasks, share information, and make decisions across the whole organization. It sounds formal, but it does not have to be complex. The goal is simply to make sure AI is working with your teams instead of deepening the silos that already exist.

What the Research Says

The research backs this up pretty clearly. McKinsey found that AI-enabled organizations see gains in both productivity and decision quality across functions, but only when the integration is intentional. Dropping a tool into a workflow and hoping for the best does not cut it.

The Three Structures Worth Knowing

There are three common structures: centralized, where one dedicated AI team manages everything; decentralized, where each department runs its own tools independently; and hybrid, which blends both. Davenport and Mittal found that the hybrid model tends to perform best in larger organizations because it balances local flexibility with org-wide consistency.

The Part Most Teams Overlook

Adoption stalls when employees feel excluded from the process. Short, role-specific training sessions beat long generic ones every time, and involving teams in selecting tools makes a real difference in how quickly things stick.

Read the Full Breakdown

If you want to go deeper on how to actually structure this inside your org, I covered everything here: https://aitransformer.online/ai-collaboration-model/
How is your team currently handling AI across departments? Would love to hear what's working and what isn't.

ai #productivity #teamwork #softwaredevelopment #devops

AI Cloud Security Is Broken. Here Is How to Fix It.

Scott McMahan — Thu, 16 Apr 2026 13:16:41 +0000

If you are shipping AI workloads to production, your security strategy is probably not keeping pace. Only 18% of teams can fix vulnerabilities as fast as they release code. That means for most engineering teams, the gap between what is deployed and what is secured keeps growing with every sprint.

Last year, 99% of organizations with AI systems experienced an attack on them. Over a third were breached. These are not edge cases. This is the baseline reality for teams building and deploying AI in the cloud right now.

The attack surface is growing for a few reasons that are worth understanding. Most organizations are running across multiple cloud providers, which adds complexity that security tooling was not originally designed to handle. AI workloads are being pushed into production environments that were built for traditional software, without the access controls and monitoring to match. And identity management, which should be the foundation of any cloud security posture, is still being neglected at scale. Orphaned accounts, unrotated credentials, and overpermissioned service roles are some of the most common entry points attackers are exploiting today.

The encouraging part is that AI is also changing what defenders can do. Modern security platforms can monitor behavioral patterns across your entire infrastructure in real time, flag anomalies automatically, and respond to threats in seconds rather than hours. Organizations using these tools are cutting response times by up to 30%. That kind of speed matters when attackers on the other side are also running automated tooling.

A solid strategy here is not about ripping everything out and starting over. It starts with locking down identity and access management, adopting a Zero Trust model so that nothing inside your network is trusted by default, and building automated detection workflows that escalate to humans for the decisions that actually require judgment.

I put together a thorough breakdown of what this looks like end to end, including how to approach it if you are dealing with legacy systems or a small security team.

Read it here: https://aitransformer.online/ai-cloud-security-strategy/

security #cloud #ai #devops #zerotrust

AI Is Coming for Your CI Pipeline. That's a Good Thing.

Scott McMahan — Wed, 15 Apr 2026 14:33:30 +0000

Your CI pipeline is still playing catch-up. AI is about to change that.
73% of development teams are not using AI in their CI/CD workflows yet. That number comes from a 2025 JetBrains survey, and it is surprising given how loudly AI dominates every other corner of the software engineering conversation right now.
The gap has practical roots. Cost is a real barrier. So is uncertainty about value. Security concerns around introducing AI into build and deployment pipelines are legitimate and worth taking seriously. But the direction the industry is moving is unmistakable.

AI CI automation shifts pipelines from reactive to proactive. Predictive analytics flags failure scenarios before a build even breaks. Smarter test selection cuts run times by identifying which tests actually matter for a given code change. Self-healing workflows resolve common failures automatically without waiting for a developer to intervene. Security checks move earlier into the pipeline where catching a vulnerability costs a fraction of what it costs downstream.

The CI/CD tool market sits at around $35 billion today and is projected to hit $94 billion by 2035. AI is one of the primary drivers of that growth.

Getting started does not require a complete infrastructure overhaul. A focused experiment with AI-assisted test selection or predictive build analytics can deliver meaningful results quickly. From there teams can expand their use of AI with confidence at each step.

The engineers building this knowledge now will have a real advantage as the tools continue to mature.

Full breakdown here: https://aitransformer.online/ai-ci-automation/
Tags: devops, cicd, ai, automation, programming

AI Is Writing Your Code Docs, Blog Posts, and Technical Content. What Happens When It Gets It Wrong?

Scott McMahan — Tue, 14 Apr 2026 14:46:19 +0000

Generative AI has changed the content game for dev teams. Faster documentation, quicker blog turnaround, automated release notes. The efficiency gains are real.
But so are the risks.

Hallucinated API references. Outdated code examples presented as current. Biased outputs baked into training data that nobody ever audited. These are not edge cases. They are happening right now in organizations that moved fast without building any governance around their AI content workflows.

AI content governance is the framework that keeps your team out of that situation. Clear policies, human review at key stages, regular audits, and a plan for when things go wrong. It is not overhead. It is how responsible engineering teams operate at scale.

We just published a full breakdown covering what AI content governance actually is, why the regulatory landscape is shifting fast, and how to build a practical framework without grinding your workflow to a halt.

Worth a read if AI is touching any part of your content pipeline.
👉 https://aitransformer.online/ai-content-governance/

Why AI Time Series Forecasting Is Worth Your Attention Right Now

Scott McMahan — Mon, 13 Apr 2026 14:33:15 +0000

Most developers have heard of time series forecasting. Fewer have kept up with how dramatically the tooling and underlying models have changed over the past couple of years. If your last mental model of this space involves ARIMA and seasonal decomposition, it is worth a refresh.

The gap between classical statistical methods and modern AI-driven approaches has grown large enough that it changes what is practical to build, who can build it, and how much effort it takes to get production-quality results.

The Problem with Classical Methods

ARIMA and its variants were the standard for a long time for good reasons. They are interpretable, computationally cheap, and well-supported by decades of statistical theory. The problem is that they assume linearity and stationarity in the underlying data. Real-world time series rarely cooperates with those assumptions for long.

When a demand signal shifts, a financial instrument spikes, or a sensor reading starts drifting, classical models degrade. They were not designed to handle nonlinear dynamics or sudden distributional shifts, and no amount of tuning changes that fundamental constraint.

What Deep Learning Brought to the Table

LSTMs and GRUs were the first architectures to make a real dent in this problem. They were specifically designed to model long-range dependencies in sequential data, which made them far better suited to the kinds of patterns that break classical models. Transformers followed, and despite being designed for language tasks, they turned out to be remarkably effective for long-horizon forecasting.

A comprehensive review in the Journal of Big Data quantified the improvement: deep learning approaches outperform classical statistical methods by up to 14% on forecasting accuracy, with the gap growing as data complexity increases. That is not a marginal difference at production scale.

Foundation Models Changed the Deployment Story

The bigger shift for practitioners is what foundation models have done to the cost of getting started. Google's TimesFM was pre-trained on over 100 billion time-series data points and delivers strong zero-shot performance on datasets it has never encountered. Amazon's Chronos tokenizes numerical values and applies transformer-based techniques borrowed directly from large language models, benchmarking well across 42 diverse datasets.

What this means in practice is that you no longer need a large domain-specific training set to build a useful forecasting system. You start from a strong pre-trained baseline and fine-tune from there. For teams without dedicated data science resources, that is a significant change in what is feasible.

A Technique Worth Knowing: Future-Guided Learning

One of the more interesting recent developments comes from a paper published in Nature Communications. The technique, called Future-Guided Learning, runs two models in parallel. A detection model analyzes future data to identify critical events, while a forecasting model learns to predict those events from current data. When predictions diverge from detections, the forecasting model updates more aggressively to close the gap.

The results were a 23% reduction in prediction error on nonlinear dynamical systems and a 44.8% improvement in AUC-ROC for seizure prediction. The interesting part is the design philosophy: rather than minimizing average error, you are training the model to recognize and actively correct its own failure modes.

What Actually Breaks Forecasting Systems in Production

Model architecture is only part of the challenge. Production forecasting systems fail for reasons that have nothing to do with which transformer variant you chose. Data quality is the most common culprit. Time series data arrives with gaps, duplicate entries, inconsistent sampling rates, and outliers that distort training in ways that are hard to detect until something downstream goes wrong.

Evaluation methodology is another area where teams get tripped up. Mean squared error is the default, but it rewards models that predict the mean and discourages variance. Depending on your use case, directional accuracy, peak detection, or calibration might be far more relevant. And once a model is in production, you need monitoring in place to catch performance degradation as the underlying data distribution shifts over time.

Read the Full Breakdown

The full post covers all of this in depth, including a look at real-world applications across finance, healthcare, supply chain, and energy, along with practical guidance on architecture selection and getting started without overengineering the solution.

Read it here: https://aitransformer.online/ai-time-series-forecasting/

Your KPIs Are Already Too Late

Scott McMahan — Fri, 10 Apr 2026 14:25:11 +0000

Most project KPIs are built to explain what already happened. They show delays, budget overruns, and missed targets after the fact. That might feel useful, but it does not help teams prevent failure. It only helps them document it.

That is the real limitation of traditional metrics. They are backward-looking by design.

AI KPI Development Changes the Timing

AI KPI development shifts the focus from reporting to anticipation. Instead of static dashboards, AI-driven metrics analyze patterns, detect early signals, and surface risks before they become visible in standard reports.

This changes how project leaders operate. Decisions are no longer based only on past performance. They are shaped by what is likely to happen next.

That shift creates a real advantage, especially in complex environments where small issues can escalate quickly.

Better Metrics Do Not Fix Broken Systems

There is a common misconception that better dashboards will automatically improve outcomes. That is not true.

If the underlying data is inconsistent, the goals are unclear, or the team does not trust the numbers, AI will only amplify those problems. It will produce faster insights, but not better ones.

AI KPI development works best when it is built on strong foundations. Clear objectives, clean data, and aligned teams are still essential.

The Real Shift Is Operational

The move to AI-driven KPIs is not just a technical upgrade. It is a change in how organizations think about performance and decision-making.

It requires leaders to move away from static reporting and toward continuous evaluation. It requires teams to trust evolving metrics instead of fixed benchmarks.

That is not a small change. It is a fundamental shift in how projects are managed.

Rethink What Your KPIs Are Telling You

If your KPIs still feel like a post-mortem, they are not doing enough. The goal is not just to understand what failed. It is to prevent failure in the first place.

AI KPI development is one way to move closer to that goal, but only if it is implemented with the right structure and mindset.

Read the full post here: https://aitransformer.online/ai-kpi-development/

Hackers Are Not Breaking In Anymore; They Are Logging In

Scott McMahan — Thu, 09 Apr 2026 14:28:22 +0000

Identity Is the Weakest Link in Modern Security

For years, security strategies focused on infrastructure. Networks, endpoints, and applications were the priority.

That is no longer where attackers are concentrating their efforts.

They are targeting identity.

Deepfakes, synthetic identities, credential abuse, and account takeovers are allowing attackers to bypass traditional defenses without triggering obvious alarms. Instead of breaking systems, they are logging in and operating as legitimate users.

Why Traditional Detection Is Failing

Most detection systems still rely on rules.

If a login happens from a new location, flag it. If behavior looks unusual, trigger an alert. That worked when threats were slower and easier to predict.

It does not work when attackers can generate thousands of realistic identities or automate login attempts at scale.

By the time a rule is triggered, the attacker may already be inside.

How AI Changes Identity Threat Detection

AI introduces a different approach.

Instead of relying only on predefined rules, it analyzes patterns across behavior, access, and context. It can detect subtle anomalies that would not be obvious in isolation.

This allows organizations to move earlier in the attack lifecycle and reduce the window of exposure.

It also enables more adaptive responses as threats evolve.

The Tradeoffs You Cannot Ignore

AI is not a perfect solution.

False positives can interrupt legitimate users. Bias in models can lead to uneven outcomes. Privacy concerns grow as more identity data is analyzed and stored.

The challenge is not just adopting AI, but implementing it in a way that balances detection with trust.

Where This Is Going

Identity is becoming the new security perimeter.

Organizations that rethink identity protection with AI will be better positioned to handle modern threats. Those that rely only on legacy detection models will continue to react after the damage is done.

If you are working in security, fraud prevention, or platform engineering, this shift is already affecting your systems.

Full breakdown here: https://aitransformer.online/ai-identity-threat-detection/

devto #cybersecurity #ai #identitysecurity #fraudprevention

Your AI Architecture Is Probably Doing Too Much

Scott McMahan — Wed, 08 Apr 2026 14:25:00 +0000

A lot of AI backends grow in a very predictable way.

You start simple. One model call. One workflow.

Then you add features.

Another endpoint. Another integration. Another layer of logic. Maybe some caching. Maybe a queue. Maybe a workaround for latency. Maybe a second model.

Nothing feels wrong in the moment.

But over time, the system becomes harder to reason about.

Complexity Doesn’t Announce Itself

AI systems rarely break with a clear failure.

Instead, they become harder to operate.

Small changes take longer. Costs become less predictable. Performance varies in ways that are difficult to explain. Fixes create side effects somewhere else.

This is not a scaling problem yet.

It is a structure problem.

The Backend Starts to Drift

Without clear patterns, AI backends tend to evolve into tightly coupled systems.

Inference logic mixes with orchestration. Data handling leaks into request handling. Retry logic lives in random places. Observability becomes an afterthought instead of a built-in capability.

At that point, every new feature increases risk.

Not because the idea is bad.

Because the system is no longer easy to extend safely.

Good Patterns Reduce Friction

The goal is not to eliminate complexity.

It is to contain it.

Well-designed AI backends separate concerns early. They make it clear where decisions are made, where failures are handled, and how data moves through the system.

That clarity makes everything else easier.

Scaling becomes more predictable. Debugging becomes faster. Iteration becomes safer.

This Is Where AI Products Are Won or Lost

The model gets attention.

The backend determines outcomes.

If the system is structured well, it can absorb growth, change, and new capabilities without falling apart.

If it is not, every improvement becomes harder than the last.

I break this down in more detail here:

https://aitransformer.online/ai-backend-development-patterns/

AI #SoftwareEngineering #BackendDevelopment #MLOps #SystemDesign

Your AI Is a Black Box Because You Didn’t Document It

Scott McMahan — Tue, 07 Apr 2026 14:20:16 +0000

AI Systems Are Failing for a Different Reason

AI systems are not just failing because of bad models.

They are failing because no one can explain them.

No clear data lineage. No record of decisions. No understanding of how the model evolved over time. Just systems that work until they don’t, and when they break, no one knows why.

This Is Not a Modeling Problem

This is not a modeling problem.

It is a documentation problem.

Most teams still treat documentation as cleanup work. Something to do after training. Something to patch together before deployment. Something to revisit only when governance or compliance forces the issue.

That approach does not scale.

The Lifecycle Is Where It Breaks

AI documentation has to follow the full lifecycle.

It starts at planning. It continues through data collection, model development, evaluation, deployment, and monitoring. It evolves as the system evolves.

Without that, teams lose traceability. They lose reproducibility. They lose trust.

Why This Is Now a Real Risk

Organizations are being asked to explain how their models work, what data shaped them, and how decisions are made.

If the documentation is weak, those answers do not exist.

That is where systems fail, not just technically, but operationally.

Documentation Is Infrastructure

Documentation is not overhead.

It is infrastructure.

It connects data to models, models to decisions, and decisions to accountability. Without it, everything else becomes harder to manage and easier to break.

Read the Full Breakdown

I wrote a deeper breakdown of the AI documentation lifecycle and what teams need to change.

https://aitransformer.online/ai-documentation-lifecycle/

Tags:
ai, machine-learning, technical-writing, mlops, devops, data-engineering

AI Data Pipeline Optimization: Why Most AI Data Pipelines Are Quietly Failing

Scott McMahan — Mon, 06 Apr 2026 14:44:52 +0000

Most AI data pipelines are quietly failing.

They are not always breaking in obvious ways. Instead, they are slowing decisions, degrading data quality, and creating hidden risks that compound over time. That is why AI data pipeline optimization is becoming essential.

The Problem With Traditional Pipelines

As pipelines scale, complexity increases. More data sources. More transformations. More dependencies.

Traditional approaches struggle to keep up because they rely on static workflows and manual fixes. When something goes wrong, teams react. By then, the damage is already done.

AI Changes How Pipelines Behave

AI introduces a different model.

Pipelines can detect anomalies early, adapt to changing conditions, and optimize how data moves through systems. They stop being passive infrastructure and start acting like intelligent systems.

This reduces downtime, improves data quality, and removes a lot of the manual overhead that slows teams down.

Why This Impacts Everything

Your pipeline is not just a backend system. It affects every decision your business makes.

If your pipeline is slow, your insights are delayed. If your data is inconsistent, your outputs cannot be trusted.

AI data pipeline optimization improves reliability, speed, and accuracy at the same time.

The Competitive Gap Is Growing

Teams that adopt AI-driven pipelines are moving faster and operating with fewer failures.

Teams that do not are stuck fixing issues, dealing with delays, and working with data they cannot fully trust.

That gap is getting wider.

Final Thought

AI data pipeline optimization is not optional anymore. It is becoming a requirement for teams that want to stay competitive.

If you want a deeper breakdown of how this works in practice, read the full post:
https://aitransformer.online/ai-data-pipeline-optimization/

ai #dataengineering #mlops #datascience #machinelearning

AI Knows Your Project Budget Will Fail Before You Do

Scott McMahan — Fri, 03 Apr 2026 14:37:26 +0000

Most project budgets do not fail all at once. They drift.

A small variance here. A missed assumption there. Then suddenly, the numbers no longer reflect reality and no one can clearly explain when things went wrong.

If you have worked on a tech project, you have seen this pattern. It is not usually the result of poor planning. It is the result of using static forecasting in an environment that never stops changing.

The Limits of Spreadsheets

Traditional budget forecasting depends on periodic updates. Teams check in weekly or monthly and adjust based on what has already happened.

The problem is that modern projects do not move in neat cycles. Costs shift in real time. Scope evolves during execution. Dependencies change without warning. By the time a spreadsheet catches up, it is already behind.

That delay is where most budget problems begin.

What AI Changes

AI budget forecasting works differently because it does not rely on fixed update cycles. It continuously evaluates cost signals, usage patterns, and historical trends while adapting to what is happening in the present.

This creates something teams rarely have with traditional methods. Earlier visibility into where things are heading.

It does not eliminate uncertainty, but it reduces the gap between reality and what teams think is happening.

Why This Matters for Developers and PMs

When budgets drift, the impact shows up quickly in delivery. Features get cut. Timelines move. Priorities shift midstream.

With AI-driven forecasting, teams can see those pressures forming sooner. That makes it possible to adjust direction before problems become visible at the executive level.

The result is not just better financial control. It is more stable execution.

The Shift You Should Pay Attention To

This is not about replacing project managers or finance teams. It is about improving how decisions are made when conditions are constantly changing.

Teams that adopt AI forecasting earlier will not just manage budgets more effectively. They will operate with fewer surprises and more confidence in their plans.

That difference compounds over time.

Read the Full Breakdown

If you are working in software development, project management, or tech leadership, this is worth understanding now.

https://aitransformer.online/ai-budget-forecasting/

Tags: ai, projectmanagement, finops, softwaredevelopment, forecasting

AI Is Creating Security Failures You Cannot See

Scott McMahan — Thu, 02 Apr 2026 14:37:41 +0000

AI is changing how security teams detect threats, analyze signals, and support decisions.

It is also creating a new category of risk that many organizations still do not fully understand.

When AI is used in security, the issue is not just model performance. It is also resilience, governance, and trust. Adversarial attacks, data poisoning, model inversion, and model drift can all undermine systems that appear effective on the surface.

That is why AI model risk management matters.

Security teams need to think beyond accuracy and start asking harder questions about failure, manipulation, monitoring, and accountability.

I wrote a post breaking down the risks, the controls, and the governance issues surrounding AI model risk management in security.

Read it here:
https://aitransformer.online/ai-model-risk-management-in-security/

ai #cybersecurity #security #machinelearning #devops #infosec #riskmanagement