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Most AI pilots never reach production because disconnected workflows, manual processes, and poor model governance create deployment bottlenecks. MLOps bridges this gap by automating development, deployment, monitoring, and retraining, enabling scalable, reliable, and ROI-driven enterprise AI.
Master data management fixes dirty data behind retail stockouts, pricing errors & product chaos. Build a single source of truth across every
Master data management fixes dirty data behind retail stockouts, pricing errors & product chaos. Build a single source of truth across every channel.
Digital Transformation Services: 5 Myths Worth Dropping in 2026
Busting five myths about digital transformation services, legacy system modernization, AI, and whether it is only for big enterprises.
Let me say something that will annoy half the vendors out there. Most of what people believe about digital transformation services is wrong, and those wrong beliefs are exactly why so many projects stall.
The market itself is booming. Analysts expect the digital transformation services market to grow by billions through 2030, at a rate above 30% a year. So the money is there. The confusion is too.
Here are five myths worth dropping if you lead technology at any company, large or small. Some will sound obvious. The expensive part is how often good teams ignore them anyway.
What Is Digital Transformation, Really?
Quick version: it is when a company rebuilds how it runs on modern technology.
That means moving systems to the cloud, updating or replacing old software, and automating manual work. The point is faster decisions, lower costs, and data that moves freely between teams. That is it. The buzzwords just make it sound harder than it is.
Picture a retailer that still keys every order into three separate systems by hand. Transformation is what connects those systems so the order is entered once and everyone sees it. Same business, far less friction.
Is It Just About Buying New Tools?
Short answer: no. And this is the big one.
A tool does not transform anything by itself. If your process is broken, new software simply runs the broken process faster. BCG and McKinsey both find that the people side decides whether transformations succeed or fail, more than the technology ever does. Buy the platform if you need it. Just fix the way people actually work first.
Here is the pattern I see on repeat. A company buys an impressive platform, switches it on, and waits. Six months later the staff have built spreadsheets to work around it, and the new tool sits half-used. The software was fine. The workflow underneath it was the problem.
Do You Have to Replace Legacy Systems All at Once?
Short answer: please do not.
The big-bang rewrite is how transformations fail. The systems that have run your business for years connect to everything, so replacing them all overnight is the riskiest move you can make. The smarter play is a phased approach to legacy system modernization. You modernize one piece at a time, run old and new side by side, and prove each step before the next. That measured, lower-risk path is what a sound system integration and modernization program looks like. Boring? Maybe. Safe and effective? Yes.
Phased also means you can stop and adjust. Each step is small enough to measure, so a problem in one component never takes down the whole business.
Is Modernizing Legacy Systems Too Expensive?
Short answer: keeping them is what is expensive.
Old systems keep consuming your budget. Maintenance costs on aging software climb 10% to 15% every year, and the security risk grows with them. IBM’s 2025 research puts the average data breach in financial services at over $5 million, and unpatched legacy systems are a common entry point for attackers. Modernization is an investment with a real return. Doing nothing costs more every year.
And the cost compounds. Enterprises pour a large share of their IT budgets into simply keeping old systems alive, money that could fund the upgrades that actually move the business. Every year you wait, that share grows.
Will AI Fix a Broken Process by Itself?
Short answer: no, and this trips up a lot of smart teams.
Point AI at a messy workflow and you get an automated mess. Fix the process first, cut the steps that add nothing, then add AI to what is left. In that order, AI is genuinely powerful. Skip the order, and you have paid a lot for a faster version of the same problem.
Quick example: automating an approval chain with four pointless sign-offs just gets you to “no” faster. Remove the three that add nothing, then automate the one that matters.
Is This Only for Giant Enterprises?
Short answer: nope. The opposite, in some ways.
Here is a stat that surprises people: McKinsey finds that companies with fewer than 100 employees are 2.7 times more likely to report a successful transformation than companies with more than 50,000. Smaller teams move faster and have fewer old systems to deal with. SMBs are also the fastest-growing part of the market. So enterprise digital transformation discipline applies to small and mid-sized businesses too. The principles are the same. The scale is just smaller.
Why do smaller firms win more often? Fewer layers of approval, fewer old systems, and leaders who stay close to the work. The same discipline that helps a 50,000-person enterprise helps a 50-person one.
How Do You Actually Start?
Skip the endless strategy meetings. Start with one thing.
Pick the system that costs the most and frustrates people the most. Map how the work flows around it. Fix the flow, modernize the system behind it, and let that win fund the next step. Treat digital transformation services as an ongoing habit your company keeps investing in.
Want a sanity check before you spend? A clear digital transformation roadmap helps, and a quick chat with an experienced engineering team early on saves a lot of money and rework later.
Then repeat. Reassess every few months, retrain your people, and keep security current as you go.
Healthcare Data Engineering: Why Your Data Shows Up Late
Your systems can exchange data in seconds. Most of them still don’t. Here is the engineering gap behind the lag.
Quick question. When a patient arrives in your emergency department, how fast does the full picture show up? History, current meds, recent imaging, all in seconds?
For most health systems, the honest answer is no. The data exists. It just shows up late, in pieces, and in formats that do not agree. That lag is a healthcare data engineering problem, and it is fixable.
Why is real-time care running on batch data?
Because most systems were built to move data in scheduled batches, not as events happen.
Clinical data lives in separate systems: the EHR, the lab, imaging, pharmacy, devices. A single ED visit should trigger a cascade of events across all of them in seconds rather than minutes. In many hospitals it still runs on overnight feeds and manual lookups. By the time the full record lands, the first decision is already made.
This matters more every year. Industry analysts now describe healthcare as dependent on real-time information, and event-driven data like admissions and discharge alerts is becoming essential for risk and value-based care. HL7 integration moves the messages. Speed and consistency are the part teams underbuild. The standard is not the bottleneck. The pipeline underneath it is.
What is healthcare data engineering, really?
It is the work of turning data from every clinical system into one clean, current, usable model.
The approach that works treats FHIR data integration as the native format of the platform, applied as data arrives. FHIR, which HL7 maintains as an open standard, models clinical facts as structured resources like Patient, Observation, and Encounter.
A clinical data pipeline converts each source into those resources, validates them, and serves them fast. Fast means the data is ready the moment it arrives, not after an overnight job. Analytics and AI then read one clean structure instead of combining formats at query time. That is the difference between data you can query and data you can act on.
Why doesn’t FHIR alone fix it?
Because a system can expose a FHIR API and still send data your other systems read differently.
This is the semantic gap. One system codes a lab result in a standard terminology, another stores it as free text, a third uses a local label. The data looks interoperable because it moves, yet it cannot be combined or trusted without cleanup. Move that record in real time and the mismatch just arrives faster.
The field data confirms it. Federal reporting shows nearly 70% of hospitals still struggle to exchange patient information cleanly. Healthcare data interoperability is engineered, not handed to you by the standard.
What’s the cost of waiting on your data?
Money, time, and decisions made on incomplete information.
Poor data exchange costs the U.S. healthcare system more than $30 billion a year in duplicate testing, rework, and delays. And health IT staff spend 43% of their time extracting and harmonizing data before anyone can use it. That is time your team spends waiting on data instead of acting on it. Slow data is not a back-office issue. It changes the decision a clinician makes at the bedside.
How do you build a pipeline that works in real time?
By resolving data at the moment it arrives, then moving it where it is needed.
A clinical data pipeline does its work in stages. It collects from every source, including legacy feeds and HL7 v2 messages. It converts each record into FHIR resources and applies standard terminologies. It validates against the schema, then stores and serves the result through secure APIs.
For real time, it captures changes as events, so an admission, a lab result, or a discharge updates the record the moment it happens. Engineers call this change data capture, and it is what separates a live record from a nightly snapshot. FHIR is built for this, and its design supports real-time clinical decision support. The engineering behind a real-time data pipeline is what turns that capability into something clinicians can rely on.
Where do you start?
Start with meaning and timing, not with another point-to-point integration.
Map your sources and the code sets each one uses, then fix that terminology at ingestion. Decide which events need to move in real time, and build change capture for those first. Phase the rest system by system, with governance and HIPAA-grade access control from day one. A focused first phase shows results in weeks. Prove value there, and the rest of the rollout funds itself.
The standards are already in your stack. What decides whether your data arrives clean and on time is the engineering beneath it. Get that right, and healthcare data engineering turns slow, scattered records into one current, trusted flow. Clean, real-time data is also the only foundation real clinical AI runs on.
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Healthcare data engineering breaks when systems can't share clean data. A FHIR-first playbook to unify clinical pipelines and meet 2026 mand
Healthcare data engineering breaks when systems can't share clean data. A FHIR-first playbook to unify clinical pipelines and meet 2026 mandates.
Get a strategic roadmap for enterprise digital transformation services. Learn how leaders can modernize operations, scale growth, and drive
Get a strategic roadmap for enterprise digital transformation services. Learn how leaders can modernize operations, scale growth, and drive ROI.
Discover how AI-powered medical imaging helps radiologists detect abnormalities faster, improve diagnostic accuracy, reduce human error, and streamline workflows. Learn how healthcare organizations are leveraging AI to enhance patient outcomes and accelerate clinical decision-making.
5 Myths About Building an AI SaaS Stack in 2026
Five things people still get wrong about the AI SaaS stack, and what is actually true for technical teams in 2026.
AI is on every product roadmap in 2026, which means a lot of confident advice is flying around about how to build it. Some of it is right. A lot of it is myth. The teams that build well are usually the ones who stopped chasing the hype and focus on the boring fundamentals. If you are a CTO or architect deciding where to spend this year, telling the myth from the reality is the difference between a product that ships and a pile of expensive demos.
So let’s clear up five of the most common myths, because believing them gets expensive fast. Here is the short version of what actually goes into SaaS software development for AI products this year.
Myth 1: Do you need to train your own model?
No. Almost nobody should. Training a model from scratch costs a fortune and rarely beats what you can already rent. The real work is connecting an existing model to your own data and your product. Most SaaS application development for AI in 2026 is assembly: you pick a model, ground it in your data with retrieval, and fine-tune only when a narrow task needs it. Fine-tuning is cheaper and faster than training, and for most products even that is optional. You also avoid the trap of maintaining a model you cannot keep current as the field moves every few weeks. Save your budget for the data and the product, not the model.
Myth 2: Will an AI agent just figure things out on its own?
No. An agent is only as good as the rails you give it. An AI agent is a system that plans and runs multi-step tasks for a user, and it needs tools, limits, and a way for a human to step in. The hype is real, but the maturity is not. McKinsey’s State of AI report found 62% of organizations are experimenting with agents, yet only 23% have scaled them in even one function. Without rails, an agent can take a wrong action confidently and at scale, which is worse than a chatbot giving a wrong answer. Agents that work in production run on retrieval, policy, audit logs, and oversight, and building those rails is the core of AI-first SaaS engineering, not an afterthought.
Myth 3: Does RAG make hallucinations disappear?
Not by itself. Retrieval-augmented generation grounds a model’s answers in your data, which helps a lot, but it is not a magic fix. If your retrieval pulls the wrong context, the model still gives a wrong answer with full confidence. RAG quality depends on how you chunk, embed, and rank your data, and it adds a new surface that attackers can target. Good teams measure how often retrieval pulls the right context and fix it when the numbers slip. Treat retrieval as something you test and improve, not something you set once and forget, and keep checking the output instead of trusting it because it sounds right.
Myth 4: Is more AI always better?
No. More AI features can make a product worse. Users do not want ten half-working AI buttons. They want one that solves a real problem. The adoption numbers prove the gap: McKinsey found 88% of organizations now use AI, but only about 6% see real enterprise value from it. Every extra feature also adds cost, maintenance, and a new way for the product to confuse people. A focused product that does one AI thing well beats a crowded one that does five things badly. Ask what problem each AI feature solves before you build it. The winners are the ones who pointed AI at a problem that mattered and measured the result.
Myth 5: Should you always reach for the biggest model?
No. Bigger is not automatically better, and it is almost always more expensive. A smaller, cheaper model often handles routine, high-volume work just as well, while you save the large model for the genuinely hard requests. Bigger models are also slower, which users feel on every request. Routing each request to the right-sized model is how scalable SaaS with embedded AI keeps its costs under control. Test a smaller model first, and move up only when the task clearly needs it. Picking one giant model for everything is the fastest way to watch your margins disappear.
So what is actually true?
Here is the honest version. You rent the model, you build the parts that use your data, and you spend your effort on retrieval, evaluation, and the workflow your users rely on. You give agents rails, you point AI at real problems, and you right-size your models. None of that is flashy, and all of it is what works. Skip the myths, and you skip a lot of expensive rework. If you want the fuller picture of how this holds up as you grow, it helps to plan for scaling from MVP to enterprise early, and to treat the unglamorous parts as the serious work, the way good SaaS product engineering always has.
Digital Experience Design in 2026: The End of the One-Size-Fits-All Interface
Most products still show every user the same screen. Same buttons, same menus, same layout, whether someone is brand new or a daily power user. That approach is ending.
In 2026, the interface adapts. AI reads a user’s role, behavior, and context, then shapes the screen around them. This is the biggest change in digital experience design in a long time, and it is moving fast. For CTOs, technical architects, and IT managers, the static interface is becoming a liability.
Here is what is changing, and why it matters.
What is generative UI, and why does it matter now?
Generative UI means the interface builds itself around each user instead of staying fixed.
Traditional UI ships one layout to everyone. Generative UI uses AI to assemble the layout in real time, based on who the user is and what they are trying to do. Gartner expects 30 percent of new applications to use AI for adaptive interfaces by 2026, up from under 5 percent a couple of years ago. That is a steep curve. A product built on a fixed layout today is building for a standard that is already shifting. For users, the payoff is a product that feels lighter and more relevant. For the teams behind it, adaptive UI is turning into a baseline that buyers will expect.
Why is the same screen for every user becoming a problem?
Because a screen built for everyone fits no one well.
When a product shows the same dense dashboard to a new user and an expert, both lose. The new user feels overwhelmed. The expert wastes time hunting for the few things they use. Personalization fixes this, and it pays. McKinsey finds that faster-growing companies earn around 40 percent more of their revenue from personalization than slower-growing peers. A strong AI-powered digital experience shows each user a version of the product that fits their job, with the right tools in front and the rest out of the way. Teams that build Custom AI Solutions treat this as a core capability, because adaptation has to be designed into the product, not bolted on at the end.
What does an adaptive interface actually look like?
It looks like a product that quietly rearranges itself as it learns how you work.
Picture a few cases. A SaaS analytics tool moves the reports a user opens most to the top, and tucks away the ones they never touch. A finance app shows a first-time user a guided, simple flow, while giving an experienced admin a dense control panel. A support tool surfaces the one action a user is most likely to need next, based on what they did last time. None of these ask the user to configure anything. The product adapts on its own. The effect is quiet. Most users never notice the interface changing. They just notice that the product keeps getting easier to use.
How are people interacting with products beyond the screen?
More and more, they are talking, scanning, and pointing instead of only typing.
Adaptive design is not only about layout. It is about input too. Voice, image, and gesture are entering everyday products. Gartner expects 80 percent of enterprise software to be multimodal by 2030, up from under 10 percent in 2024. Good digital experience design plans for this from the start. The same task should work whether a user types it, says it, or shows it a photo, and the product should answer in whatever form fits best. It also widens who can use the product, since voice and visual input reach people that a keyboard-only design leaves out.
What does this mean for the teams building products?
The job shifts from drawing screens to designing a system that can build screens.
This is a real change for design and engineering. Teams stop hand-drawing every layout. Instead, they build a set of modular components and rules, and the AI assembles them per user. The hard work moves into the system: what data drives the adaptation, what rules keep it sensible, and how to keep the experience consistent as it changes. Strong SaaS Product Engineering is built for this kind of dynamic product. If your team wants to see how far your current product is from adaptive, it helps to look at it one user type at a time and ask whether each one is well served.
Where this is heading
The fixed interface worked for a long time. That period is ending. In 2026, digital experience design rewards products that adapt to each user, accept more than one kind of input, and keep getting sharper as they learn. The products that still show everyone the same screen feel dated next to the ones that feel made for the person using them. The shift is already underway, and the gap between adaptive and static products keeps widening.
Choosing a SaaS software development stack for AI products? Use this decision guide across frontend, backend, vector, and AI orchestration l
Choosing a SaaS software development stack for AI products? Use this decision guide across frontend, backend, vector, and AI orchestration layers.
Discover the top 2026 AI UX trends reshaping AI apps. Leverage expert digital experience design to boost user engagement, growth and ROI.
Discover the top 2026 AI UX trends reshaping AI apps. Leverage expert digital experience design to boost user engagement, growth & ROI.
87% of finance leaders report that poor data quality has undermined their AI investments, turning promising initiatives into costly disappointments. The solution isn’t more AI tools—it’s a modern data architecture built on governance, real-time integration, data observability, and trusted pipelines.
Legacy app modernization cost benchmarks, budgets by app complexity, six approach pathways, team mix economics & phase gate payment plan.
Legacy app modernization cost benchmarks, budgets by app complexity, six approach pathways, team mix economics & phase gate payment plan.
Your Old System Isn’t Cheap. The Bill Is Just Hidden.
Keeping an old system is not free. The real cost is hidden, it is big, and it grows every year you wait.
Quick reality check for anyone still running an old system because replacing it “costs too much.” Keeping it costs more. The problem is that the cost of keeping it never shows up as a line in your budget, so it feels free. It is not. And the longer you wait, the bigger that hidden cost gets. Let me break down where the money actually goes, and how to put a real number in front of your board.
So why does the old system look cheap?
Because you only see part of the bill.
The invoice shows licenses, support, and hosting. That is the visible part. McKinsey puts technical debt at 20 to 40 percent of a company’s entire tech estate, and adds another 10 to 20 percent to the cost of every project that runs on the old code. None of that shows up on the renewal invoice. So the system looks cheap, right up until you total what it actually costs you.
Okay, so what is the real cost?
Four things, and they add up fast.
First, downtime. When old systems fail, it gets expensive fast. Splunk pegs unplanned downtime at $400 billion a year across the world’s 2,000 largest companies, about $9,000 every minute. Aging systems fail more often than modern ones, so even a small slice of that lands on you.
Then breaches. Old, unpatched systems are the easiest target there is. A single breach now averages $4.44 million worldwide, and a record $10.22 million in the US, according to IBM.
There is also wasted time. Your engineers spend the week fixing old systems. Stripe found the average developer loses about 17.3 hours a week to technical debt and aging code. That is close to half of every week, spent keeping old code running instead of building anything new.
And the quiet one: stalled growth. The same Splunk report found 64 percent of companies say downtime stalls their innovation and 74 percent say it delays new releases. Every month you spend patching the old system is a month you are not shipping the Digital Transformation services your competitors already offer.
What is the cost of inaction, in one sentence?
It is everything the old system costs you while you decide not to replace it.
That includes the outages you have not had yet, the breach you are hoping to avoid, the engineer hours you keep spending, and the products you are not launching. Put a dollar figure on each, add them up across the next few years, and that total is your cost of inaction.
Why does waiting make it worse?
Because every one of those numbers climbs while you wait.
Three reasons. Support for old hardware gets more expensive each year. The people who know these systems are retiring, and there are very few replacements, so hiring them costs more. And every year you wait, more tools and data get tied to the old setup, which makes the eventual switch larger. Waiting does not make the cost go away. It just makes it bigger and moves it to a worse time.
But the system works fine. Why fix it?
Because “works fine” and “cheap” are not the same thing.
A system can run every day and still cost you money through all four costs above. “Works fine” usually means “has not failed badly yet.” That is not a plan. It is a bet that the breach, the outage, or your key engineer leaving all hold off a little longer. The cost of being wrong is the breach number above. Hope is not a budget line.
Does this apply to smaller companies?
Yes. Maybe more.
Big companies can absorb a bad month. A smaller business often cannot. A single serious outage, or one compliance failure, can hurt badly when money is tight and the team is small. The numbers are smaller, but the method does not change: total the cost of the old system, compare it to the fix, and decide.
So what do you actually do?
Put both numbers on one slide.
Add up what the old system really costs you each year: maintenance, downtime, breach risk, and lost engineering time. Project it out three to five years, with the increases built in. Then set it next to the price of fixing it. That comparison is the whole business case, and it is the part most teams skip. A short Legacy Modernization Services assessment, usually paired with a cloud migration consulting review, gives you those numbers in a few weeks. This is exactly what Legacy System Modernization is for: it clears those four costs at the same time. Phase the work so the riskiest systems go first, and once the board sees both columns, your Legacy App Modernization plan is an easy yes.
So the next time someone says modernization is too expensive, ask the obvious question back: compared to what? The old system has a price, too. Add it up, put it on the slide, and let the two numbers decide. Most of the time, they decide fast. That, in one line, is the whole case for Legacy Modernization Services.
Healthcare’s Most Expensive Habit Is Retyping Its Own Data
A fast, practical guide to intelligent document processing, data pipelines, and the governance that turns messy records into clean data.
Here is something nobody puts in a pitch deck: healthcare quietly spends a fortune paying people to retype data that software could read in seconds.
Faxes. Scanned PDFs. Handwritten notes. They pile up every day, and someone keys them into a system by hand. It is slow, it is expensive, and it is a big reason your data is messier than you think.
For a CTO or IT manager, this is not a paperwork problem. It is a data problem, and it quietly shows up later in your reports, your claims, and your audits.
The bigger issue is scale. Around 80% of healthcare data sits in unstructured documents, locked in files that systems cannot read directly, according to research on clinical data. The fix has a name: intelligent document processing. Here is how it works, fast.
What is intelligent document processing?
Simple version: intelligent document processing (IDP) reads a document, grabs the data that matters, checks it, and passes it on clean.
A few tools split the work. OCR turns scans into text. Natural language processing figures out what the text means, like medications, codes, and dates. Rules and models validate each value. Anything uncertain goes to a person.
Picture a faxed lab report going in, and a clean record coming out with the patient name, test, and result already in the right fields. That is the goal.
The smart part is the confidence score on every field. Reviewers only check what the system flags, so people stop re-reading entire pages.
Why is manual data entry such a problem?
Because it is dull, constant, and easy to get wrong.
The cost is not small. Poor data quality drains an average of $12.9 million a year from a single organization, says Gartner. And accuracy is all over the place. One review of clinical data entry found error rates from 2 to 2,784 per 10,000 fields. Multiply one small slip by thousands of fields a day, and the mistakes stop being rare.
There is upside on the table, too. The 2025 CAQH Index points to a remaining $21 billion savings opportunity from automating the manual transactions healthcare still does by hand. One wrong value at the document stage flows straight into billing and care, and AI only repeats it faster.
How does a data pipeline turn documents into clean data?
Once you stop typing, a pipeline does the job. The document arrives, the system sorts what it is, pulls the fields, checks them, and writes clean data into the EHR using standards like FHIR or HL7.
The real work is the engineering around it. Solid data pipeline development makes every step retryable, logged, and traceable, so nothing vanishes between stages. The model that reads the page gets the credit. The pipeline decides whether the data shows up accurate, complete, and auditable. Skip that work, and you get silent failures: a record that never lands, a value nobody can explain, an audit you cannot answer.
Why do governance, quality, and MDM matter?
Because clean today does not mean clean next year.
Data governance sets the rules: who sees a field, how changes get logged, how long data lives. Master data management (MDM) keeps one patient as one patient, not three near-identical records. Quality checks block the bad values that should never get in automatically.
This pays off. Research shows healthcare organizations can save up to $42.1 million over three years by improving data quality and interoperability. And compliance is built in, not bolted on. Teams put HIPAA Technical Safeguards into the pipeline from day one, including access control, audit trails, and encryption, as set out in 45 CFR §164.312. Get this part wrong, and clean data slowly drifts back into a mess.
Where do you actually start?
Start small. Pick one high-volume document type, like claims or lab results. Measure your current error rate and turnaround first. Set targets for speed and accuracy, route the uncertain fields to people, and let the system handle the rest.
Do not try to automate everything at once. One workflow, measured and proven, beats a big rollout that nobody trusts.
Most teams aim for about 60% fewer manual errors in document-heavy work, and they hit it once human review backs up the automation.
The timing is on your side. Healthcare and life sciences are the fastest-growing areas for IDP adoption, per market research; more than half of health plans already use AI in admin workflows, and FHIR-based exchange is expanding ahead of new 2027 rules.
That is the whole idea. Documents are not disappearing, but the manual keying behind them can. Pair intelligent document processing with reliable data pipelines and steady governance, and the data stuck in paperwork turns into something clean, trusted, and ready to build on. Want a head start? It helps to see how a focused approach to healthcare technology ties documents, pipelines, and compliance together.
Why AI Projects Keep Getting Scrapped — and How Digital Product Engineering Fixes It
Here is a number that should stop you. In 2025, 42% of companies abandoned most of their AI initiatives, up from just 17% the year before, according to S&P Global. The average company scrapped almost half of its AI proofs of concept before they ever reached production.
So what is going on? These teams have working models. They have budget. They have smart people. The projects still die.
The answer is almost never the model. It is everything around the model. And building that everything is the job of digital product engineering. Let’s break down what that means, and why it decides whether your AI platform ships or stalls.
So why do companies keep abandoning AI projects?
Because the AI works in a demo but not in production.
A demo runs on clean data, a handful of users, and a controlled setup. Production is messy. Traffic spikes without warning. Data shifts. The system has to connect to tools the company already runs. When a team hits these problems with no plan, the project stalls. S&P found that cost, data privacy, and security were the top reasons projects got scrapped. All three are engineering problems, not model problems.
What does digital product engineering actually fix?
It builds the system that keeps the model reliable once real users show up.
Digital product engineering covers the full lifecycle: cloud setup, data pipelines, deployment, and monitoring. Engineers design composable architecture so each part scales on its own. They automate testing and deployment so updates do not break the product. They watch for model drift and correct it before users notice. This is the core of AI product engineering. It treats the model as one piece of a much bigger product.
Why is the model never the real problem?
A strong model still fails without strong data and integration.
Feed a model messy data and it produces output you cannot trust. Cut it off from your workflows and it answers in a vacuum. Engineers build clean, governed data pipelines and secure APIs so the model gets accurate inputs and connects to the tools people actually use every day. That is what turns a clever model into something a business can depend on.
How does SaaS product engineering handle the load?
It lets thousands of customers use one secure system at the same time.
A standard web app manages structured, predictable data. An AI platform processes large volumes of unstructured data in real time. SaaS product engineering provides the multi-tenant architecture that carries this load. Strict data isolation keeps every client’s data private, and secure API hubs link the platform to existing CRM and ERP systems. One model becomes a product many teams can use without stepping on each other.
What about the cost? AI gets expensive fast.
Inference is the budget killer, and engineering keeps it in check.
Running the model on every request costs money, and inference is the biggest slice of AI compute spend. The bill climbs with every new user. Engineers cut the waste with caching, auto-scaling, and model compression, and they use FinOps to tie spend to real demand. Skip this work and the cost of serving a user can pass the revenue that user brings in. That math kills products quietly.
How do you keep enterprise buyers happy?
You build security and compliance in from the start.
Large customers check security long before they sign anything. Engineers add encryption, role-based access, audit logging, and data residency controls inside the architecture, not bolted on at the end. Compliance built into the core speeds up procurement and earns trust with the buyers who matter most.
Okay, so where do you start?
Start with the system, not the model.
Get your data ready. Design for scale while the platform is still small. Treat the AI product as a full product with a real lifecycle, from data pipeline to deployment to monitoring. Do that, and you keep your project out of the 42% that get scrapped.
If you are staring down the jump from prototype to production, the smart first move is a hard look at your data and architecture before you scale anything.