The AI Shouting Match: Why We Are Drowning in Outputs & Starving for Outcomes
Are we building value, or just checking boxes? In the frantic rush to monetize AI, a destructive pattern of "solutioning backwards" has emerged.
Feb 5, 2026
9 minutes
Tech | Product | Leadership
Tania Makroo | Transformation Strategist
People | Jobs | Digital Work | AI
If you’ve been near a corporate boardroom or a Virtual all-hands lately, you’ve heard the noise. It’s deafening. We are in the middle of massive organizational flattening, agile restructuring, and a frantic, panicked rush to monetize AI.
Yet, amidst all this movement, I’m seeing a destructive and hypocritical pattern emerge. There is a shouting match happening among leaders right now. Everyone is yelling about the value they are supposedly showing, but very few are held accountable for the value realized by the humans at the other end of the screen.
We are drowning in outputs… use cases, features, deployed agents, (vibe) code, configuration: while starving for actual business outcomes.
My philosophy has always been that true product delivery is a trifecta: it’s the intersection of Business Strategy, Human-Centered Change, and Experience Design. You cannot have one without the others.
Yet, in this rush to deploy new technology (specifically AI Features such as AI agents), many organizations are abandoning impact discovery in favor of speed. They are choosing the illusion of progress over the reality of impact.
Baseline Callout: AI can be implemented in the form of Augmented AI, Assistive AI, Agentic AI, Autonomous AI, and much more. For this conversation, we are talking about the ecosystem of all of these.
Here are three ways I see this ‘Outputs over Outcomes’ mentality threatening the promise of AI.
If you’ve been near a corporate boardroom or a Virtual all-hands lately, you’ve heard the noise. It’s deafening. We are in the middle of massive organizational flattening, agile restructuring, and a frantic, panicked rush to monetize AI.
Yet, amidst all this movement, I’m seeing a destructive and hypocritical pattern emerge. There is a shouting match happening among leaders right now. Everyone is yelling about the value they are supposedly showing, but very few are held accountable for the value realized by the humans at the other end of the screen.
We are drowning in outputs… use cases, features, deployed agents, (vibe) code, configuration: while starving for actual business outcomes.
My philosophy has always been that true product delivery is a trifecta: it’s the intersection of Business Strategy, Human-Centered Change, and Experience Design. You cannot have one without the others.
Yet, in this rush to deploy new technology (specifically AI Features such as AI agents), many organizations are abandoning impact discovery in favor of speed. They are choosing the illusion of progress over the reality of impact.
Baseline Callout: AI can be implemented in the form of Augmented AI, Assistive AI, Agentic AI, Autonomous AI, and much more. For this conversation, we are talking about the ecosystem of all of these.
Here are three ways I see this ‘Outputs over Outcomes’ mentality threatening the promise of AI.
If you’ve been near a corporate boardroom or a Virtual all-hands lately, you’ve heard the noise. It’s deafening. We are in the middle of massive organizational flattening, agile restructuring, and a frantic, panicked rush to monetize AI.
Yet, amidst all this movement, I’m seeing a destructive and hypocritical pattern emerge. There is a shouting match happening among leaders right now. Everyone is yelling about the value they are supposedly showing, but very few are held accountable for the value realized by the humans at the other end of the screen.
We are drowning in outputs… use cases, features, deployed agents, (vibe) code, configuration: while starving for actual business outcomes.
My philosophy has always been that true product delivery is a trifecta: it’s the intersection of Business Strategy, Human-Centered Change, and Experience Design. You cannot have one without the others.
Yet, in this rush to deploy new technology (specifically AI Features such as AI agents), many organizations are abandoning impact discovery in favor of speed. They are choosing the illusion of progress over the reality of impact.
Baseline Callout: AI can be implemented in the form of Augmented AI, Assistive AI, Agentic AI, Autonomous AI, and much more. For this conversation, we are talking about the ecosystem of all of these.
Here are three ways I see this ‘Outputs over Outcomes’ mentality threatening the promise of AI.
1. The Great Process Inversion (Solutioning Backwards)
The most glaring hypocrisy right now is the inversion of basic product methodology.
In my recent observations, I surmise that teams are operating under extreme pressure. I’ve seen teams that should be solving problems escaping the reality of true systems thinking with the skip button: "Give us use cases and we can get started."
They promise that in 12-16 weeks you will have an AI system that will save you "tons." But they do this by bypassing the investigation of the why, who, what, how, and where… the primary directives of Discovery (& Define phases of Design Thinking).
It looks like a pre-defined solution where they apply "Jobs to be Done" (JBTD) retroactively to justify it. The cracks in this approach will be significant.
1. The Great Process Inversion (Solutioning Backwards)
The most glaring hypocrisy right now is the inversion of basic product methodology.
In my recent observations, I surmise that teams are operating under extreme pressure. I’ve seen teams that should be solving problems escaping the reality of true systems thinking with the skip button: "Give us use cases and we can get started."
They promise that in 12-16 weeks you will have an AI system that will save you "tons." But they do this by bypassing the investigation of the why, who, what, how, and where… the primary directives of Discovery (& Define phases of Design Thinking).
It looks like a pre-defined solution where they apply "Jobs to be Done" (JBTD) retroactively to justify it. The cracks in this approach will be significant.
1. The Great Process Inversion (Solutioning Backwards)
The most glaring hypocrisy right now is the inversion of basic product methodology.
In my recent observations, I surmise that teams are operating under extreme pressure. I’ve seen teams that should be solving problems escaping the reality of true systems thinking with the skip button: "Give us use cases and we can get started."
They promise that in 12-16 weeks you will have an AI system that will save you "tons." But they do this by bypassing the investigation of the why, who, what, how, and where… the primary directives of Discovery (& Define phases of Design Thinking).
It looks like a pre-defined solution where they apply "Jobs to be Done" (JBTD) retroactively to justify it. The cracks in this approach will be significant.
It is equivalent to building a house and then bringing in an architect to justify why you put the front door on the roof. While, like building a house, the planning aspect is crucial; it cannot be sped up to the lightning speed of pressing a button to activate it.
Here is the reality check: Yes, AI workflows have significantly helped speed up the process of many parts of discovery and delivery. But the time it takes for comprehension and creative affinity mapping is the human speed that we alone can foster.
“My capacity to recognize patterns and make strong inferences is something I need to keep studying and practicing. It requires reading, experimenting without the need for immediate success, and a need for fidelity. Read, create, try, fail, keep trying different experiments... see what doing the wrong thing leads to even. That's my advice. I digress… ”
When we prioritize speed over discovery, we don't get agile; we get fragile. We end up building highly technical solutions for problems that don't exist, while ignoring the messy, human realities of the actual business process and the core job to be done.
2. The "Just Press a Button" Leadership Mindset
We are seeing a significant shift where leaders with strong Pre-Sales or pure Sales backgrounds are being placed in charge of complex technical delivery teams.
There is logic to this in a ‘go-to-market’ focused world, but it creates a dangerous gap in reality.
The Sales Mindset is often about the art of the possible: the glossy demo.
The Delivery Mindset is about the grinding reality of implementation: the dependencies, the legacy debt, the change management.
When Sales leads Delivery, complex AI implementation is treated as a ‘turnkey’ activation. Leaders assume AI just works when you press a button.
This leads to unrealistic timelines and a trigger-happy approach that is more chaos than strategy. It forces senior strategists to spend their time acting as buffers to protect the client from our own internal disorganization, rather than focusing on implementation and adoption strategy.
3. The Gaslighting of "Strategy" as "Non-Technical"
Perhaps the most frustrating trend is the rebranding of roles that signals a de-prioritization of design strategy in favor of implementation perception.
In this new world, if you aren't coding the agent, you aren't considered "technical enough." I’ve been in rooms with brilliant strategists, these folks understand the entire business ecosystem and are dismissed by leaders who themselves don't understand the difference between AI Tools and AI Agents.
Let’s be clear: AI implementation is 20% configuration and 80% strategy, data governance, and human workflow design.
Alienating your business, design, and change strategists because they don't fit a narrow definition of technical is a fast track to failure. You will end up with technically functional AI features that nobody uses because they don't fit the human context of the job.
The Way Forward: Slow Down to Speed Up
If we want to stop the sea of solutions that solve nothing and start delivering real value, we have to return to the fundamentals of Design Thinking, Product Management, and Human-Centered Strategy.
We need to invest in the why and refrain from jumping to code or configure without ironing out the basics.
We need to stop measuring success by how many AI features we deploy and start measuring it by how many problems we solve. We need leaders who understand that delivery is hard, tangible work, not magic. And we need to respect that strategy is a technical skill in the age of AI.
Technology changes. The human need for coherent, valuable experiences does not. Let's focus on the outcome, not just the output.
It is equivalent to building a house and then bringing in an architect to justify why you put the front door on the roof. While, like building a house, the planning aspect is crucial; it cannot be sped up to the lightning speed of pressing a button to activate it.
Here is the reality check: Yes, AI workflows have significantly helped speed up the process of many parts of discovery and delivery. But the time it takes for comprehension and creative affinity mapping is the human speed that we alone can foster.
“My capacity to recognize patterns and make strong inferences is something I need to keep studying and practicing. It requires reading, experimenting without the need for immediate success, and a need for fidelity. Read, create, try, fail, keep trying different experiments... see what doing the wrong thing leads to even. That's my advice. I digress… ”
When we prioritize speed over discovery, we don't get agile; we get fragile. We end up building highly technical solutions for problems that don't exist, while ignoring the messy, human realities of the actual business process and the core job to be done.
2. The "Just Press a Button" Leadership Mindset
We are seeing a significant shift where leaders with strong Pre-Sales or pure Sales backgrounds are being placed in charge of complex technical delivery teams.
There is logic to this in a ‘go-to-market’ focused world, but it creates a dangerous gap in reality.
The Sales Mindset is often about the art of the possible: the glossy demo.
The Delivery Mindset is about the grinding reality of implementation: the dependencies, the legacy debt, the change management.
When Sales leads Delivery, complex AI implementation is treated as a ‘turnkey’ activation. Leaders assume AI just works when you press a button.
This leads to unrealistic timelines and a trigger-happy approach that is more chaos than strategy. It forces senior strategists to spend their time acting as buffers to protect the client from our own internal disorganization, rather than focusing on implementation and adoption strategy.
3. The Gaslighting of "Strategy" as "Non-Technical"
Perhaps the most frustrating trend is the rebranding of roles that signals a de-prioritization of design strategy in favor of implementation perception.
In this new world, if you aren't coding the agent, you aren't considered "technical enough." I’ve been in rooms with brilliant strategists, these folks understand the entire business ecosystem and are dismissed by leaders who themselves don't understand the difference between AI Tools and AI Agents.
Let’s be clear: AI implementation is 20% configuration and 80% strategy, data governance, and human workflow design.
Alienating your business, design, and change strategists because they don't fit a narrow definition of technical is a fast track to failure. You will end up with technically functional AI features that nobody uses because they don't fit the human context of the job.
The Way Forward: Slow Down to Speed Up
If we want to stop the sea of solutions that solve nothing and start delivering real value, we have to return to the fundamentals of Design Thinking, Product Management, and Human-Centered Strategy.
We need to invest in the why and refrain from jumping to code or configure without ironing out the basics.
We need to stop measuring success by how many AI features we deploy and start measuring it by how many problems we solve. We need leaders who understand that delivery is hard, tangible work, not magic. And we need to respect that strategy is a technical skill in the age of AI.
Technology changes. The human need for coherent, valuable experiences does not. Let's focus on the outcome, not just the output.
It is equivalent to building a house and then bringing in an architect to justify why you put the front door on the roof. While, like building a house, the planning aspect is crucial; it cannot be sped up to the lightning speed of pressing a button to activate it.
Here is the reality check: Yes, AI workflows have significantly helped speed up the process of many parts of discovery and delivery. But the time it takes for comprehension and creative affinity mapping is the human speed that we alone can foster.
“My capacity to recognize patterns and make strong inferences is something I need to keep studying and practicing. It requires reading, experimenting without the need for immediate success, and a need for fidelity. Read, create, try, fail, keep trying different experiments... see what doing the wrong thing leads to even. That's my advice. I digress… ”
When we prioritize speed over discovery, we don't get agile; we get fragile. We end up building highly technical solutions for problems that don't exist, while ignoring the messy, human realities of the actual business process and the core job to be done.
2. The "Just Press a Button" Leadership Mindset
We are seeing a significant shift where leaders with strong Pre-Sales or pure Sales backgrounds are being placed in charge of complex technical delivery teams.
There is logic to this in a ‘go-to-market’ focused world, but it creates a dangerous gap in reality.
The Sales Mindset is often about the art of the possible: the glossy demo.
The Delivery Mindset is about the grinding reality of implementation: the dependencies, the legacy debt, the change management.
When Sales leads Delivery, complex AI implementation is treated as a ‘turnkey’ activation. Leaders assume AI just works when you press a button.
This leads to unrealistic timelines and a trigger-happy approach that is more chaos than strategy. It forces senior strategists to spend their time acting as buffers to protect the client from our own internal disorganization, rather than focusing on implementation and adoption strategy.
3. The Gaslighting of "Strategy" as "Non-Technical"
Perhaps the most frustrating trend is the rebranding of roles that signals a de-prioritization of design strategy in favor of implementation perception.
In this new world, if you aren't coding the agent, you aren't considered "technical enough." I’ve been in rooms with brilliant strategists, these folks understand the entire business ecosystem and are dismissed by leaders who themselves don't understand the difference between AI Tools and AI Agents.
Let’s be clear: AI implementation is 20% configuration and 80% strategy, data governance, and human workflow design.
Alienating your business, design, and change strategists because they don't fit a narrow definition of technical is a fast track to failure. You will end up with technically functional AI features that nobody uses because they don't fit the human context of the job.
The Way Forward: Slow Down to Speed Up
If we want to stop the sea of solutions that solve nothing and start delivering real value, we have to return to the fundamentals of Design Thinking, Product Management, and Human-Centered Strategy.
We need to invest in the why and refrain from jumping to code or configure without ironing out the basics.
We need to stop measuring success by how many AI features we deploy and start measuring it by how many problems we solve. We need leaders who understand that delivery is hard, tangible work, not magic. And we need to respect that strategy is a technical skill in the age of AI.
Technology changes. The human need for coherent, valuable experiences does not. Let's focus on the outcome, not just the output.
