Why do ML / AI Strategies Misalign?
3 common ways an operations wall can stop good execution
👋 Hello! I’m Matt, Co-Founder and AI Strategist at Aspire360. Welcome to Datalife360, where we talk about AI strategy, data careers, and data science mindsets. As always, find me on LinkedIn. If you enjoyed this post, follow, like or share.
Intro
Just last month, I found myself once again in the observer's seat in a zoom meeting. There were individuals from business, data, and machine learning teams, each presenting their own meticulously planned roadmaps. Yet, as I've noticed multiple times before, the conversation hit a wall when it came to integrating these plans.
Each roadmap was its own world, with unique priorities, jargon, and metrics. But there was a disconnect. They were all speaking the same language and had the same goals.
Metrics had been created, objectives had been set, but shared language, agreed on objectives, and strategic alignment were missing. The strategy didn’t align with the tactical execution at a team level. And people were struggling to understand the value and significance. They hit what I call the operations wall.
The operations wall is something you’ll notice more as your business and team scale. Smashing through the wall is key to any strategy or operating model. It’s a critical priority.
Let's talk about three problems I often see that create the operations wall.
Shared Language is Often Missing
When business and data teams operate without a shared language, it's like working in different dialects. Everyone speaks the same language but has different terms for the same things. Businesses and teams may know what they want, but with different dialects, basic communication in projects becomes inefficient and the costs mount.
Product Managers (PMs) serve a critical role in this part of the operations wall. For business, they set the critical KPIs and discover use cases and opportunities. For data teams they translate the business objectives into features. More importantly? They define the shared language.
Businesses and teams struggle if a PM or even a data scientist acting as a PM isn’t there. They end up operating within a silo, confined by specialized jargon. Even the most effective PMs can find it challenging to streamline communication.
When there's a jagron barrier, this operations wall gets taller. PMs and data teams can take the lead by doing events such as lunch and learns, debriefs, and sessions that define terms, processes, and highlight the importance and value of current efforts.
Regular training sessions and lunch and learns led by PMs can ensure that business stakeholders, data scientists, and machine learning engineers are speaking the same "language." It enables the technical experts to concentrate on what they do best — building robust machine learning models — rather than getting caught up in translation duties.
Objectives and Metrics Are Mismatched
Strategy can be great, but if the objectives and the metrics measuring success are missing? Tactical execution of the work suffers. I’ve found that objectives and metrics are often mismatched in this case.
Standardized objectives and KPIs are key for aligning business, data, and tech teams, but an impenetrable operations wall often exists between them. This wall isn't dismantled by mere metrics—it requires constant strategic alignment and continuous adaptation with three groups
Product Managers (PMs): PMs are the frontline tacticians aiming to tear down the operations wall. They don't just set KPIs; they ensure these KPIs mesh seamlessly with both business goals and what's technically doable. They translate high-level business objectives into technical requirements and actionable tasks. In the absence of this critical alignment role, the wall persists, and projects veer off course.
Lead Data Scientists: Think of them as the architects of the demolition project. Their technical prowess validates the feasibility of objectives and provides a reality check on what can be achieved with the available data. They ensure the tactical moves are not just well-intentioned but also executable, serving as the technical conscience of the team.
Machine Learning Platform Engineers: These are the people who bring the heavy machinery to break down the wall. Their role is vital in ensuring that the machine learning models are not just theoretically sound but also operationally robust—scalable, reliable, and aligned with business-critical metrics like latency and throughput.
The task of demolishing the operations wall is ongoing. Business goals, technological capabilities, and data insights are ever-changing variables that require adaptive strategies. The key players like PMs and Lead Data Scientists serve as change agents, constantly communicating and adapting the approach to ensure all teams are in sync.
Effective wall demolition isn’t just a top-down process; it’s mutual. Real-time, metrics-based feedback from key technical roles ensures that the strategy and tactics remain aligned. This dynamic information exchange is the adhesive that keeps the team cohesive, aiding in the strategic dismantling of operational barriers.
Strategic Alignment is Nonexistent
Imagine sinking resources into a machine learning model designed to optimize supply chain, only to see your organization pivot its focus to customer retention. Now, the once-promising model is misaligned with the AI strategy, and the development, scoping, and need to be reassessed. Painful, right?
Navigating the maze of alignment in AI and machine learning falls to multiple key roles: Product Managers (PMs), Lead Data Scientists, and Managers. Unlike more traditional projects, machine learning is an ever-changing landscape that demands real-time adjustments.
PMs act as the strategic translators, turning evolving business objectives into data-driven goals and technical adjustments. Lead Data Scientists fill the essential role of technical realists, laying out what can and can't be achieved with existing data and technology.
Strategic Alignment aligns closely with product strategy. Learn the difference between a product mindset and model mindset here:
However, don't overlook Managers. They're the stewards of alignment, the ones who pull the strings on where resources go, making sure everyone's work serves the bigger picture. PMs might decide the fate of individual projects, but Managers ensure these decisions fit into broader business strategies.
Alignment is a two-way street. While Managers steer the ship, they rely on PMs and Lead Data Scientists for a constant stream of metrics and feedback. This is the feedback loop that keeps the organization nimble, aligned, and focused on what really matters: hitting those business goals while navigating a sea of change.
Maintaining alignment isn't a one-and-done deal. It's a dynamic equilibrium where Product Managers, Lead Data Scientists, and Managers all play critical roles. They are the pivot points that balance the constant shifts in strategy, technology, and data landscapes.
Final Thoughts
Navigating the complexities of AI and machine learning can feel like steering a ship through turbulent waters. But with the right alignment, you're not just surviving these challenges; you're mastering them.
The operations wall may be daunting, but it's not insurmountable. It takes a dedicated effort from your PMs, Lead Data Scientists, and Managers to turn that wall into a gateway for seamless operation.
This isn't a one-off task; it's an ongoing process that future-proofs your strategies. As you adapt and evolve, your alignment mechanisms should too. Investing in this now sets you up for success in the long term, making your business agile and resilient against inevitable changes.
So as you sift through the daily grind, don't lose sight of this alignment. It's the keystone that holds your operational arch together. With it, you're not just weathering the storm; you're setting a course for clearer skies ahead.
Whenever you ready, there are 3 ways I can help you:
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