By Hiren Dhaduk, CTO, Simform
As AI becomes increasingly embedded in software development and enterprise operations, the challenges companies face are shifting from adoption to execution. Next year, success will depend less on how many AI tools companies deploy and more on how effectively they integrate them into business processes, measure their impact and manage the risks. We are sharing five predictions about how enterprises will need to rethink their operating models, engineering investments and security practices to turn AI’s promise into measurable business value.
Prediction 1: Companies inject AI agents into an operating model intended for humans
Companies have released agents into an operating model built exclusively for humans, and that creates a huge gap. Most have the tools, a few pilots and a budget line. Very few have decided who owns an agent’s outcome, which decisions it can make alone, where a human must approve and what happens when it gets something wrong. Gartner’s survey of 1,303 leaders, run between January and April 2026, found that only 22% of organizations have successfully scaled AI across multiple business units. I’d argue the missing operating model explains most of that gap.
The companies that scale will be running an agentic operating model by the end of 2027. It starts with measurable value, so every agent is tied to a business outcome before anyone writes code. Work is redesigned around people and agents together, with a named business owner for each agent and clear human-in-the-loop boundaries. Delivery comes from business-owned agent teams, backed by a central function that sets the standards.
Most programs fall short when it comes to guardrails. They are written as policy documents and bolted onto each agent after the pilot. The pattern that works is a shared, governed platform, so every new agent inherits identity, access limits, data boundaries, evaluations and cost controls from day one. From there, each agent is run as a production system with continuous monitoring.
Most companies will achieve this quicker by co-engineering with partners who have already rebuilt their own delivery around AI. Those that keep adding agents to an unchanged organization will still be showing their boards pilots.
Prediction 2: Enterprises will shift engineering costs from developing software to sustaining it
Next year, companies will no longer treat software as a project that is built, handed over and kept running via a maintenance ticket queue. The shift is already visible in how engineering budgets are moving. Spend on new product development and routine maintenance is shrinking, and investment in product sustenance keeps growing.
AI is responsible for this new reality. Developing a feature now costs far less than it did a few years ago, and agents handle a good share of the break-fix work that once filled maintenance contracts. Yet enterprises are running more software than ever, much of it written quickly. More of it now carries models and agents that drift over time, sit on fast-moving frameworks and need constant security attention. Keeping a product current secure and improving in production is where the difficult engineering now lives.
The contract value of application development and maintenance fell 8% year over year in the third quarter of 2026, according to ISG’s latest index, even as overall technology services spending hit a record.
CIOs will likely begin funding products as long-lived assets. Each product will have a standing sustenance team accountable for reliability, upgrades, model performance and steady improvement. Budgets that still follow the old build-then-maintain cycle will keep paying for work AI has already made cheap. They will also underfund the work that decides whether a product is still competitive in three years.
Prediction 3: By the end of 2027, teams that measure every shipped outcome will own AI’s value
The enterprises that glean real value from AI will be the ones that measure it the way they measure performance or uptime, inside the delivery pipeline and from the first commit. Everyone else will still be reporting seat counts, prompt volumes and estimated hours saved, which tell a CFO almost nothing.
That gap is already wide, according to Gartner’s research. Companies that consistently track the ROI of their AI initiatives, and cut or redirect the ones that underperform, reported positive returns on 81% of them. Low performers could not say what return they were getting on 29% of theirs.
When dealing with software, that discipline rests on two measurements. The first is whether the AI is right on your own data. That means an evaluation suite built from your own queries, tickets and documents, run on every model, prompt or code change the way regression tests already are. The second is what one shipped outcome really costs once you add up tokens, retries, tool calls, rework and the review time most business cases conveniently forget. When teams do that honestly, some of the flashiest use cases turn out to lose money at scale. Meanwhile, quieter work in testing, integration and modernization often delivers most of the return.
My recommendation to CTOs is simple. Before you approve a second pilot, get the evaluation suite and the cost meter into the pipeline.
Prediction 4: AI-native products will need agents designed in as first-class users
CTOs are most likely to regret allowing AI agents inside their products that are run on borrowed identity. Right now, the quickest way to a working demo is to hand an agent a service account or a user’s delegated token. Teams then plug it into an orchestration framework and let it call whatever that credential can reach. It works, which is exactly why teams keep doing it. By 2027, it will sit at the root of a lot of incidents. The agent quietly inherits every permission its owner ever picked up, and afterward nobody can say whether a given action came from the person or from the agent working on their behalf.
Gartner recognizes this danger and predicts that by 2029, over half of successful cyberattacks against AI agents will exploit access control issues, with prompt injection as a primary attack vector.
In an AI-native tool, guardrails are built into the system itself, and from the first architecture review, agents are treated as users of that system. Each one gets its own identity and permissions scoped to the task in front of it, which expire when the task ends. Its network access is limited to an allowlist, and its memory is protected like any other privileged write path. Every action lands in a log the agent itself cannot change. That takes a few sprints to build properly at the start, and far longer to retrofit once hundreds of agents are already live.
Prediction 5: 2027 will bring the admission that coding was never the bottleneck
I believe that 2027 to be the year that the software engineering industry admits that writing code was never the real bottleneck. AI has made generating code and tests almost free, yet most product teams aren’t shipping proportionally faster. The cost of delivering software has always sat in unclear requirements, integration work and rework. Above all, it has sat in checking that what was built actually does what it should.
Gartner forecasts that by 2027, over 65% of engineering teams using agentic coding will treat the IDE as optional, shifting control, governance and validation to automated platforms. Once code is no longer written line by line in an editor, the real engineering moves to the system that decides what is allowed to merge. Enterprises that roll out coding agents without building that system will watch pull requests pile up faster than their senior engineers can review them.
Successful teams will use AI to address the costly aspects of delivery. They’ll use it to turn business intent into specifications that can be tested, to write and maintain integration and regression tests, and to check each change against the architecture rules the team has agreed on. Their senior engineers will spend less time writing code and more time defining the constraints that make AI output trustworthy.
I believe that engineering leaders will judge AI on time to production and defect escape rates by the end of 2027. The lines-of-code numbers will quietly disappear from their slides.
Measuring AI’s ROI
The message underlying these predictions is clear: AI can accelerate software development and business operations, but speed is not the single hallmark of better results. Enterprises will need to rethink how work gets done, invest in ongoing software quality and security, and measure AI against tangible business outcomes. In 2027, those who thrive will build the processes and safeguards to make AI deliver.
##
ABOUT THE AUTHOR
Hiren Dhaduk is the CTO and co-founder of Simform, an AI-native digital engineering company focused on product engineering, cloud, data, and agentic AI. With over 15 years of experience, he leads engineering teams specializing in AI engineering, system design, microservices, and cloud-native architecture.
