AI workflow - StrongestLayer: Why Conway's Law changes everything
This is a contributed piece for the Computer Weekly Developer Network written by Alan LeFort, CEO & co-founder at StrongestLayer…
StrongestLayer builds AI-native systems and platforms designed to transform organisational workflows through process redesign rather than simple automation, enabling companies to achieve gains by reimagining how work gets done around AI capabilities.
LeFort writes in full as follows…
Having built AI-native systems from the ground up and transformed our own product development processes using AI, I can tell you that most organisations are asking completely the wrong questions when evaluating AI workflow platforms.
The fundamental issue isn’t technological, it’s actually organisational.
Conway’s Law states that organisations design systems that mirror their communication structures. Most AI workflow evaluations assume you’ll bolt AI onto existing processes designed around human limitations: serial decision-making, risk-averse approval chains and domain-specific silos.
But AI doesn’t have those limitations. AI can parallelise activities that humans must do serially, doesn’t suffer from territorial knowledge hoarding and doesn’t need the elaborate safety nets we’ve built around human fallibility. When you try to integrate AI into human-designed processes, you get marginal improvements. When you redesign processes around AI capabilities, you get exponential gains.
A real-world process redesign
We recently transformed our front-end development process using this principle. Traditional product development flows serially. A product manager talks to customers, extracts requirements, hands to UX for design, PM approves and developers implement. This takes 18-24 months for a complete application rebuild.
Instead of bolting AI onto this process, we fundamentally reimagined it. We created a full-stack prototyper role paired with a front-end engineer focused on architecture. The key was building an AI pipeline that captured the contextual knowledge of each role: design philosophy, tech stack preferences, non-functional requirements, testing standards and documentation needs.
The results:
- Setup: 1 month
- Design work: 1 month
- Implementation: 2 months
- Total: 4 months vs. traditional 18-24 months
We achieved the same outcome in 25% of the time. Not necessarily by working faster, but by redesigning the workflow around AI’s ability to parallelise human sequential activities.
Leading through organisational resistance
I faced pushback, as expected. My response was to lead from the front. I paired directly with our CPO, Joshua Bass, to build the process, proving it worked before asking others to adopt it. We reframed success for our front-end engineer around velocity and pioneering new ways of working.
Critically, we ensured customer feedback remained central through personal development and jobs-to-be-done analysis. The AI pipeline creation became an opportunity for each role to codify their best practices and tribal knowledge, ensuring consistency that rarely happens in traditional approaches despite everyone’s best intentions.
Usable evaluation frameworks
When evaluating AI workflow platforms, organisations should ask vendors about their process redesign methodologies, not just integration capabilities. If a vendor has no notion of organisational transformation, that’s a yellow flag. It means they’re optimising for raw performance metrics rather than actual business outcomes.
The “efficiency through reduction” mindset: cutting headcount and giving survivors ChatGPT licenses. This misses AI’s real value, which means to enable people to become AI-assisted generalists who can manage tasks adjacent to their core roles. You probably can operate with fewer people, but until you understand the new organisational structure, you’ll cut from the wrong places.
Instead, organisations should focus on asking the right questions during vendor evaluations. Does this platform enable process redesign versus just automation? What organisational transformation methodologies does the vendor provide? Can this platform support AI-assisted generalist roles versus traditional silos? Most importantly, how does this force us to think differently about value creation workflows? These questions shift the conversation from technical features to strategic transformation capabilities.
True speed to value comes from two fundamental sources: eliminating slack time between value activities and accelerating individual activity completion through AI automation. But this requires upfront investment in process redesign rather than quick technology deployment. Organisations should evaluate AI workflow platforms based on their ability to enable fundamental process transformation, not seamless integration with existing inefficiencies.
Architecture, over features
StrongestLayer’s LeFort: AI pipeline creation is an opportunity to codify best practices & tribal knowledge.
The AI workflow market is bifurcating between AI-enhanced tools that automate existing processes to deliver incremental improvements and AI-native platforms that enable organisational redesign for sustainable competitive advantage.
When evaluating platforms, look for those that challenge your current process assumptions rather than promising easy integration. The real question isn’t “How does this work with our existing systems?” but “How does this enable us to work fundamentally differently?”
Key takeaways
The implications of Conway’s Law for AI implementation extend far beyond technology deployment—they fundamentally reshape how organisations must approach workflow transformation. Technology leaders who understand these dynamics can leverage them to create sustainable competitive advantages, while those who ignore them risk marginal returns despite significant AI investments.
Conway’s Law applies to AI i.e. your organisational structure will constrain AI value unless you redesign processes
- Process redesign beats technology integration: Exponential gains come from reimagining workflows, not automating existing ones.
- Lead from the front: Prove new approaches work before expecting organisational adoption.
- Evaluate for transformation capability: Choose platforms that enable organisational redesign, not just efficiency gains.
- Invest in setup for exponential returns: Upfront process design work pays dividends in execution speed.
Companies that get this right will pull ahead. The rest will keep throwing AI at broken processes and wonder why they’re not seeing results.
