Stop Paving the Cow Path: The Truth About AI & Digital Transformation
Move from a flawed "digital-first" mindset to an "outcome-first" strategy that actually delivers value
This one is going to have my former consulting colleagues convulsing in apoplectic fits. 🤪
Table of Contents
The Flawed "Digital First" Mentality
For the better part of a decade, "Digital Transformation" has been the rallying cry in boardrooms worldwide. Billions have been invested in new software, cloud infrastructure, and now, the great gold rush towards Artificial Intelligence. Yet, for all this sound and fury, the results are often underwhelming. Projects run over budget, adoption lags, and the promised efficiency gains remain elusive.
The core of the problem lies in a flawed, foundational belief: the "digital first" mentality. We've become obsessed with acquiring and implementing technology for its own sake. The most common mistake is layering sophisticated AI onto existing, often broken, processes. This is the digital equivalent of paving a cow path—you get a slightly smoother ride, but you're still stuck on the same inefficient route.
It's time for a radical shift in perspective. We need to move from "digital-first" to "outcome-first" thinking. True transformation isn't about the tools you buy; it's about fundamentally rethinking the work to be done. By applying the Jobs-to-be-Done (JTBD) framework, we can cut through the hype and build an AI strategy that delivers real, measurable value.
Deconstructing Digital Transformation with Jobs-to-be-Done
What is the real Job that organizations are trying to get done with "digital transformation"?
No executive wakes up wanting to buy "AI" or "digital transformation." These are solutions—or more accurately, solution categories—they hire to achieve a higher-level objective. When you peel back the layers of buzzwords, you find the real jobs organizations are trying to get done. These are timeless business goals, such as:
Increase the profitability of a customer relationship
Enhance the quality of strategic decision-making
Accelerate the process of product innovation
Minimize exposure to operational risk
Innovate the customer value proposition
The "digital" part is merely the how. The job is the why. When you start with the job, your entire approach changes. You stop asking "What AI tool should we use?" and start asking "What is the most direct path to achieving our desired outcome?"
Moving beyond features
A technology-first approach leads to a feature-focused roadmap. A JTBD approach leads to an outcome-focused roadmap. One is about what the product does; the other is about what the customer achieves.
This shift is critical because it liberates you from legacy thinking and opens the door to genuine innovation.
The Role of AI - From Tool to Transformation Engine
Current State: AI as a bolt-on solution
Today, most AI adoption is incremental. We see AI-powered tools that help us do existing jobs a little better or faster.
Chatbots field basic customer service inquiries, assisting human agents.
Predictive analytics help maintenance crews anticipate machine failures.
AI writing assistants help marketers draft copy more quickly.
These are not bad things. They provide real, but limited, value. This is AI as a helper, a bolt-on solution that improves a step within an existing workflow. It doesn’t change the workflow itself.
Future State: AI as a means to achieve a higher level of abstraction
The true promise of AI is not incremental improvement; it's total transformation through abstraction. This is where AI evolves from a "helper" to a "doer."
The novel concept is this: Instead of using AI to assist a human navigating a complex, multi-tool process, a new generation of solutions will use AI to get the entire, higher-context job done, often making the original process obsolete.
Example 1: From Analyst Assistant to Strategy Generator
Today: An AI tool helps a team of financial analysts sort through massive datasets to identify trends. The analysts then interpret these trends and build a strategic recommendation for leadership. The job is "prepare a strategic recommendation."
Future: A business leader inputs a strategic goal (e.g., "determine the optimal market entry strategy for Product X in Southeast Asia"). The AI autonomously analyzes market data, competitive landscapes, economic indicators, and internal capabilities to generate a comprehensive, data-backed strategic plan. The job executor changes from a team of analysts to a single business leader, and the work changes from analysis to decision.
Example 2: From Pipeline Management to Autonomous Growth
Today: A sales team uses a CRM, a sales intelligence tool, an email outreach platform, and a reporting dashboard to manage their sales pipeline. They are the glue holding the process together.
Future: A company defines its target customer profile and growth goals. An autonomous system then manages the entire customer acquisition lifecycle—from identification and outreach to nurturing and closing—with minimal human intervention. The solution has vastly fewer visible features because it has abstracted away the dozens of manual tasks. The outcome is superior and the cost is lower.
This is the power of elevating the level of abstraction. It creates solutions that get a bigger job done, better, and with far less complexity for the user.
A Practical Framework for an Outcome-Driven DX Strategy
How do you put this into practice? It's a four-step process that reorients your strategy around outcomes, not technology.
Step 1: Define the Core Job.
Forget technology for a moment. Ask the most important question: What is the fundamental progress our organization or our customers are trying to achieve? Are you trying to "implement a new ERP system," or are you trying to "minimize the time and resources required to deliver products to customers"? The former is a task; the latter is a Job. Focus on the Job.
Step 2: Map the Desired Outcomes.
Once the Core Job is defined, break it down into a set of desired outcomes. These are technology-agnostic success metrics. Using outcome-focused verbs, create a "job map" of what success looks like at every step. For the job of "minimize the time and resources required to deliver products," outcomes might include:
Minimize the time it takes to process a new order.
Increase the accuracy of inventory level data.
Reduce the number of handoffs between internal teams.
Eliminate the need for manual data entry.
These outcomes become your true requirements list.
Step 3: Identify Constraints and Antiquated Solutions.
With your outcome map in hand, analyze your current state. Where are the biggest struggles? Which outcomes are underserved? What disparate tools, services, and manual processes are your employees (or customers) stitching together to get the job done today? This "solution collage" is a map of your innovation opportunities.
Step 4: Ask the Abstraction Question.
This is the transformative step. Look at the entire job map and the mess of current solutions. Ask your team: How could AI get this entire job done in a completely novel way, requiring fewer steps and different actors? Don't ask how AI can improve Step 7. Ask how AI could make Steps 3 through 12 completely unnecessary. This question forces you to think beyond incremental improvements and envision a future state where the job is done differently, more efficiently, and at a lower cost.
Your Next Move Is Not About Technology
True digital transformation isn't a technology problem; it's a strategy problem. It’s not about buying the latest AI. It's about deeply understanding the job to be done and the outcomes associated with it. When you have that clarity, you can see AI for what it truly is: a powerful engine for abstracting away complexity and creating elegant, efficient solutions.
The competitive advantage of the next decade will not go to the companies with the most AI tools. It will go to the companies with the clearest, most precise understanding of their customers' and their own desired outcomes. They will leverage AI not to build more complex systems, but to achieve a radical simplicity that delivers value at a scale we're only just beginning to imagine. Your next move isn't to buy more tech; it's to get clear on the job.
What is one process in your business that you suspect could be completely abstracted away by AI, rather than just improved by it? Where are you stitching together multiple tools to get a single, high-level job done? Share your thoughts in the comments below.
If you’d like to take action, I would love to help. Here’s are some steps you can take to make that a reality for us:
Join my community and get access to more content and tools
Apply for coaching so we can do projects together and build a new business-as-usual with someone who will share the knowledge, and hold you accountable. (I have limited seats so hurry!)
I do project work as well. Use the coaching link and we can discuss.
Why Me?
Due to the intensive nature of my coaching, I can only take on a handful of clients at a time.
I’ve been trained by the best in Outcome-Driven Innovation. Part of that training involved how to understand what the future should look like. As a result, I’ve taken what I’ve learned and begun innovating so I can get you to the outcomes you’re seeking faster, better, and even more predictably. Anyone preaching innovation should be doing the same; regardless of how disruptive it’ll be.
How am I doing this?
I’ve developed a complete toolset that accelerates qualitative research to mere hours instead of the weeks or months it used to take. It’s been fine-tuned over the past 2+ years and it’s second-to-none (including to humans). That means we can have far more certainty that we’ve properly framed your research before you invest in a basket of road apples. They don’t taste good, even with whipped cream on top.
I’m also working on a completely new concept for prioritizing market dynamics that predict customer needs (and success) without requiring time-consuming and costly surveys with low quality participants. This is far more powerful and cost effective than the point-in-time surveys that I know you don’t want to do!
I believe that an innovation consultant should eat their own dog food. Therefore, we must always strive to:
Get more of the job done for our clients
Get the job done better for our clients
Get the job done faster for our clients
Get the job done with with fewer features for our clients
Get the job done in a completely different and novel way for our clients
Get the job done in a less costly manner for our clients
But more importantly, I strive to deliver high quality and high availability. That's why I also have to be choosy.
All the links you need are a few paragraphs up. Or set up some time to talk … that link is down below. 👇🏻
Mike Boysen - www.pjtbd.com
Why fail fast when you can succeed the first time?
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