What executives really mean when they say, “We want AI.”

Contributing expert: Vittesh Sahni, Senior Director of AI Engineering

Tony McDonald, Business Development Manager

When executives say, “We want AI,” they are often reacting to pressures around growth, margins, and digital performance. It sounds like a clear decision, demonstrating ambition, urgency, and an awareness of the current market. It also seems to reflect their desire to run the business more efficiently and adapt to industry changes.

However, these AI requests are rarely about technology alone because as one Forrester report notes, “AI does not automatically improve performance; it amplifies existing process, governance, and structural realities.”

This means that executives are not really asking for AI. They are reacting to deeper constraints in how their organizations operate. Most often, they are concerned about additional challenges like shrinking margins, higher digital expectations, disconnected systems, and productivity limits. Business leaders are seeking a higher level of performance, not just new technology, and small changes can’t fix this.

Ultimately, these pressures trace back to two business fundamentals, running leaner to protect margins and growing revenue through new opportunities. Leadership is also rethinking how work is distributed between people and systems, and how quickly the organization can respond to change because in today's market, the speed of that response is itself a competitive asset. This is what Coherent's Digital Value Creation framework is built around: anchoring every technology initiative to the outcomes that executives, investors, and boards actually measure.

For most leaders, the goals driving these pressures center around creating personalized experiences, automating repetitive work, evolving faster, and helping teams work smarter. These may seem like separate priorities, but they all lead to the same question: when a company implements AI, can it deliver, test, and learn as quickly as the technology demands? More often than not, what leaders describe as AI adoption challenges aren't technical limitations — they're structural constraints in how work is delivered, validated, and improved over time.

The importance of aligning AI to delivery systems

When leadership evaluates its AI goals, it often leads to structural changes.

For example, if personalization is the goal, it requires a structural change to provide feedback that may need to work in real time. Automation needs clear workflows and rules built into how things get done. Evolving faster requires responsive decision-making and frequent progress checks. And for teams to work smarter, it’s important to be clear about how people and machines share authority.

These are not just “nice-to-haves" when implementing AI. They are the basic requirements for aligning technology with an organization’s goals. When AI is added to a delivery system built for slow, steady change, these requirements quickly reveal the system’s limits. Additional speed compounds misalignment and results grow faster than oversight can monitor. Teams still learn in bursts, but decisions happen too quickly to properly implement feedback.

This is often where AI implementations stall. The AI models, platforms, and tools work, but the systems they’re added to struggle to keep up.

With this foundation, let’s examine and decode each leadership goal as an indicator of where delivery systems must evolve to use AI efficiently.

When leaders say, "We want personalization.”

What they really mean

“Leaders aim to connect with customers in smarter ways, to keep them coming back while offering individualized, responsive experiences. They know that today’s digital standards are set by platforms that react immediately to what’s happening,” says Tony McDonald, business development manager at Coherent Solutions.

Delivery system requirements for personalization

Personalization relies on more than just accurate predictions. It requires individual customer profiles built from connected data systems and dynamic feedback that reflects current behavior, not just past decisions. Customer interactions must be captured as they occur, processed into usable signals, and fed directly into decision systems that shape the user's experience in real time. Platforms must also be responsive and able to update quickly to meet changing client needs. Personalization depends on continuous data flow and embedded feedback loops.

Delivery workflows need to support quick updates, so teams can adjust recommendations, content, or offers right away, rather than waiting for scheduled releases. To do this, organizations should use shared data across channels, assign clear decision-making responsibility, and ensure changes can be tested as soon as they go live.

With these systems in place, organizations can turn raw data into personalized experiences. Platforms can react to user behavior in real time, making it possible to adjust content, create custom journeys, and keep interactions consistent across all channels. This allows organizations to translate user behavior into revenue-driving experiences, where personalization directly supports retention, conversion, and lifetime value.

If this structure is missing, personalization does not work well. Systems may use old or incomplete data or update too slowly to keep up with user behavior. When this occurs, customers notice inconsistencies. As people expect more from their digital experiences, these problems can reduce trust and hurt both retention and revenue.

Why personalization fails without structural change

Ultimately, for personalization to be successful, leadership must consider how its development lifecycle is structured. Adding AI tools to delivery systems and products won’t matter if feedback only comes in after a product is released. Additionally, if customer data is scattered across systems and teams, then personalization is patchy. And if updates only happen in big batches, risks increase, and it’s harder to adapt quickly.

The real challenge in personalization isn’t usually the AI model. It’s how quickly and safely a company can learn and make changes.

When leaders say, "We want automation.”

What they really mean

Leaders who want automation are looking to cut operating costs, reduce manual work, speed up processes, and get more predictable results. They expect AI to handle repetitive tasks and boost output without raising costs at the same rate. 

Delivery system requirements for automation

To automate successfully, you need clear workflows and well-defined decision points. AI systems should follow a structured plan with built-in oversight. Additionally, supervision must be part of the process, not separate from it.

In practice, automation restructures how work moves through delivery. Tasks are triggered by defined inputs, decisions are executed at predefined points, and exceptions are routed through controlled paths rather than handled ad hoc. This reduces execution variability and creates consistent conditions across environments.

This lets teams move faster without losing control of the process or jeopardizing output quality. Release cycles are then shortened because work no longer depends on manual handoffs, and outputs become more predictable because each step follows the same logic. Feedback from each cycle can also be captured and reused, allowing the system to improve accuracy over time instead of relying on repeated oversight. This shifts repetitive, manual work out of delivery workflows, allowing teams to focus on higher-value activities such as planning, decision-making, and customer engagement.

AI cannot fix unclear processes. Instead, it reveals ambiguity and amplifies its impact across the lifecycle. While automation increases speed, without embedded governance, teams can quickly lose control, potentially affecting system safety and compliance.

Why automation fails without structural change

If leadership doesn’t evaluate delivery systems for unclear processes and workflows prior to adding automation, the results will be unpredictable at best. Identifying where Inconsistent progress checkpoints are allowing risks to build up, and where unclear decision signals may cause AI to respond to random information, can be helpful. It's important to remember that automation does not eliminate ambiguity. It makes it visible.

When leaders say, "We want to evolve faster.”

What they really mean

In a competitive market, leaders want to release products faster and be able to adjust their strategies quickly. They also want to respond to new trends without lengthy planning processes. 

Delivery system requirements for faster evolution

Older delivery systems were built for steady, predictable changes. Teams set the scope at the start, checked progress at set times, and gathered feedback and insights only after product launch.

AI changes how organizations work by increasing productivity and accelerating decision-making, often through shared choices between people and machines. If companies increase speed but still check progress only at set times, risks can build up without anyone noticing.

Trying to move faster without changing delivery systems and processes can lead to more instability, not additional flexibility. If decisions cannot be revised quickly, speed only increases the cost of mistakes and small misalignments compound before anyone can respond. Acceleration without embedded oversight turns momentum into volatility.

However, when supported by the right delivery systems, faster evolution allows organizations to respond to market signals in real time, reducing the gap between change and action.

When leaders say, "We want our teams to work smarter.”

What they really mean

As Tony McDonald sees it, “leaders want teams to get more done and deliver better results without adding more people. They believe AI can help by reducing mental strain and allowing experienced employees to focus on more consequential tasks.”

Organizational requirements to help teams work smarter

When implemented correctly, AI can be used in many different roles across an organization, not just in engineering. The organization, however, needs clear rules and guidelines to ensure AI doesn’t pose a security risk or derail productivity. It is also important to evaluate workflows when AI is added to decide who makes decisions at each step—people or systems.

With these structures in place, AI can make work easier and more efficient. Systems can take care of routine analysis, data processing, and basic decision support. This enables teams to spend more time on judgment, setting priorities, and handling exceptions, moving their focus from repetitive tasks to more valuable work. AI augments team capabilities by handling routine analysis and initial outputs, allowing experienced staff to focus on judgment, prioritization, and complex problem-solving.

Over time, this leads to more consistent results across teams. Work follows clear rules, and knowledge is built into workflows instead of being held by just a few people. Decisions can be scaled up more easily, so teams can get more done without extra effort. AI also improves visibility into how work is performed, helping leaders identify bottlenecks, monitor workload distribution, and detect risks such as burnout earlier.

AI changes the way decisions are made in a company. If responsibility and roles aren’t clear, any productivity gains from AI can be lost due to confusion and duplication of effort. Senior staff might end up doing more review work, and informal processes can become more complicated rather than simpler.

Leaders can boost both productivity and team satisfaction by updating how their teams work to support these changes.

Why working smarter fails without structural change

While AI can be strategically used to help teams work smarter, leaders must evaluate workflows and processes to anticipate how their teams will interact with and use AI. Failing to do so can cause serious issues. If decision-making between people and AI is unclear, execution becomes inconsistent, with work cycling between systems and teams without clear ownership.

When feedback on AI output is not systematically captured, quality declines over time as errors repeat instead of being corrected. Without defined ownership in workflows, smarter tools increase coordination overhead and slow execution instead of improving performance.

Why AI projects fail even when the models work

Most enterprise delivery systems were designed for predictable change. Teams set the scope early, check progress at planned points, and learn after releasing a product. This approach worked well when output was slower and depended on manual processes.

AI changes this environment by increasing how much work gets done, shortening the time between decision-making and results, and distributing decision-making between people and automated systems.

As a result, AI does not break delivery systems, it exposes them. As a Forrester report notes, “AI accelerates whatever organizational foundation already exists — whether that foundation is strategically sound or fundamentally broken.”

This is where scaling AI becomes difficult. The technology increases the output, but the delivery system cannot sustain that speed without losing alignment or control.

Legacy structures vs. new technology

In these legacy delivery structures, the tools change, but the architecture governing intent, validation, and learning remains the same.

If teams set goals once at the beginning of the delivery process, speeding up workflows and output without adding additional reviews can cause project drift. Additionally, delaying checks allows risks to go unnoticed until later in the process, when it’s costly and time-intensive to correct. If oversight only reacts to results instead of being part of the process, it becomes harder to manage as output scales. And finally, if learning and feedback start over each cycle, teams never really build on what they know.

With these limitations, it’s clear that AI doesn’t break old systems; it simply reveals where they are ill-equipped to handle the technology.

Before implementing AI in their delivery systems, leaders must ask whether their systems are ready to handle constant change, faster output, and the additional oversight that AI needs.

Adopting an operating model to scale AI

To turn experimentation into successful AI implementation, organizations need to adopt an operating model designed for continuous, AI-driven delivery. Without a defined AI operating model, successfully adopting AI becomes incredibly difficult. The technology can increase productivity and output, but the surrounding system cannot support the speed, which negatively impacts product quality. There are three key changes, however, that leaders can make to help integrate AI into systems, turning the technology’s speed into a permanent strength.

Prioritize intent

Set a clear strategic direction early in the delivery process and keep reinforcing it as work speeds up. If intent is only defined once and not maintained, teams can lose alignment as projects grow. This can cause misaligned results, security issues, and a poor user experience.

With AI speeding up and expanding work, intent serves as a steady guide across all workflows. It helps both people and systems make decisions that stay in line with business goals, even when things change.

When intent is built into the delivery process, teams can make decisions faster without having to escalate issues. Work stays consistent, results better match the initial expectations, and there is less rework because direction is clear.

Leaders should make intent central to every stage of the delivery process and connect it across different cycles and related projects.

Embed AI governance and validation

Traditional validation models use checkpoints after work is finished. As AI speeds up and increases output, this method becomes less effective. Validation and AI governance need to happen during the delivery process, not just at the end.

In practice, this means adding checks right into the workflow. Teams review outputs as they are created, validate decisions as they happen, and flag exceptions right away instead of waiting until after release. Governance becomes part of daily work, not a separate approval step.

With validation built into the process, teams can increase output while controlling quality and oversight. They can spot risks sooner, make corrections right away, and keep systems stable as they grow.

Increase feedback and learning

AI leads to more experiments and decisions in the delivery process. Teams should focus on gathering and streamlining feedback at every development phase, automating the process where possible to ensure insights can be used across teams and projects. If leadership fails to prioritize increasing feedback when implementing AI, output will increase, and teams will look busy, but the quality of the products will not improve. Skills and knowledge should be built over time, helping teams work smarter and improve performance with each cycle.

Making these shifts can help leaders redefine how their organizations deliver results.

Using a Continuous Delivery Loop in AI-driven delivery systems

As organizations shift from traditional software delivery to using AI for faster results, their delivery methods need to change as well. As most companies head towards AI-driven engineering, teams will need systems that can keep pace with the technology and advancements.

When Coherent Solutions began integrating AI into its delivery systems, leaders quickly realized we needed a new delivery approach. So, we’ve developed the Continuous Delivery Loop (CDL). CDL is not an add-on to current processes; it is a continuous operating model for AI-driven delivery, structured around phases such as Identify, Validate, Deliver, and Observe & Scale, with feedback loops connecting each stage. CDL defines how work moves, how decisions are validated, and how learning compounds across the delivery lifecycle.

The benefits of CDL

For executives, CDL maintains alignment between business intent and delivery output, even as priorities shift. Governance and validation are embedded directly into the operating model, rather than applied as end-of-process controls. Additionally, CDL prioritizes capturing and implementing feedback throughout the delivery process. This leads to ongoing improvements, so each cycle strengthens the system instead of repeating old mistakes.

More frequent feedback and validation separate temporary technology wins from lasting implementation success. With them, organizations can scale decision-making, respond faster to change, and create new value streams without rebuilding their delivery systems.

If organizations apply AI on top of legacy delivery methods, they often get quick efficiency gains. Output increases. Timelines compress. Activity rises. The underlying system, however, remains brittle, and its brittleness becomes visible at higher speeds.

Organizations that refocus their delivery process around a continuous loop can create a flexible solution that grows alongside AI. As a result, their teams will be able to work smarter without losing control over output quality or consistency. This approach is not just automation for its own sake. It is a disciplined way to help systems keep adapting.

A key question for executives

When you say, “We want AI,” do you know what you’re asking for?

Successful AI integration doesn’t begin and end with choosing and installing tools. Upgrading your tools might bring small improvements, but redesigning your delivery architecture can produce a lasting advantage. The real competitive edge isn’t about choosing the right AI model. It’s about building a system that can keep learning, validating, and adapting over time while controlling output and performance. CDL is one way to operationalize that architecture.

Leaders who understand this will not simply deploy AI tools. Instead, they will rethink how they operate so that intelligence, validation, and learning are embedded into workflows and processes.

This kind of structural change is the foundation for long-term successful AI adoption.