In large organizations, teams often find themselves buried under constant requests, meetings, and "urgent" tasks. Startups facing AI adoption aren’t immune to this issue either. Distractions such as vendor negotiations, pilots, and dashboard overload prevent teams from staying focused on what truly matters. Leaders responsible for AI initiatives must embrace the role of the signal umbrella. This role, inspired by Zach Perret’s concept of the "sh*t umbrella”, is about shielding teams from unnecessary distractions and making sure they focus on delivering measurable business outcomes. Zach Perret’s concept of the "sh*t umbrella” AI adoption requires a clear focus and an understanding of what drives business success. This is a guide for achieving real impact, rather than being distracted by irrelevant noise. Identifying and Understanding the Noise Identifying and Understanding the Noise In AI adoption, noise can come from several directions. Common distractions include: AI Vendor hype: Vendors often promise more than they can deliver or present solutions that don’t align with your business needs.
Redundant AI pilots: Multiple pilot projects that lack clear objectives or fail to make a meaningful impact, demoralizing the business and product/engineering teams.
Half-baked proofs of concept: Initiatives that are not fully developed, lack proper scoping or alignment with KPIs, but consume valuable engineering resources.
Constant status updates: Reporting on “progress” without addressing key results that matter.
Macro “Doom and Gloom” AI Hype: Society writ large has made AI a linchpin topic, with politicians, big tech executives, and “AI Influencers” online complicit in ratcheting up hype on AI, with unchecked hyperbole. In fact, social media rewards it through likes/comments and further hype spiraling. AI Vendor hype: Vendors often promise more than they can deliver or present solutions that don’t align with your business needs. AI Vendor hype: Redundant AI pilots: Multiple pilot projects that lack clear objectives or fail to make a meaningful impact, demoralizing the business and product/engineering teams. Redundant AI pilots Half-baked proofs of concept: Initiatives that are not fully developed, lack proper scoping or alignment with KPIs, but consume valuable engineering resources. alf-baked proofs of concept Constant status updates: Reporting on “progress” without addressing key results that matter. Constant status updates Macro “Doom and Gloom” AI Hype: Society writ large has made AI a linchpin topic, with politicians, big tech executives, and “AI Influencers” online complicit in ratcheting up hype on AI, with unchecked hyperbole. In fact, social media rewards it through likes/comments and further hype spiraling. Macro “Doom and Gloom” AI Hype These distractions prevent the team from focusing on driving measurable outcomes. Whether it’s increasing revenue, reducing costs, or improving operational efficiency, AI initiatives should be evaluated based on their impact on the business. Anything that does not contribute to that outcome is noise. The biggest successes in AI that we’ve worked on frankly addressed key operational inputs for the business, not the most glamorous, but they can significantly affect KPIs, particularly COGS. Focusing on the Signal Focusing on the Signal The focus of any AI project should be on the outcomes that matter and measurable business results. Every initiative must contribute directly to business goals. Rather than getting sidetracked by a constant influx of new AI tools or vendor pitches, focus your team on solving specific business challenges. For instance, automating customer service to improve response times or implementing predictive maintenance to minimize downtime should take priority. Or order fulfillment times, supply-chain delays, and even content generation for marketing. These efforts are directly tied to business performance and can be measured against clear KPIs. This allows AI to become a strategic asset that drives real business outcomes, not just an experiment. Acting as the Signal Umbrella As an AI leader, your primary responsibility is to protect your team from distractions and ensure they stay focused on the signal. You must act as a signal umbrella, shielding your team from the aforementioned distractions that do not contribute to the project’s success. signal umbrella Here’s how to act as a signal umbrella in practice: Minimize vendor distractions: Once a solution is chosen, stop evaluating other options. Give the team the space to execute their tasks effectively. This is very hard for executives, I’ve found, particularly those constantly on X and LinkedIn, taking incremental “AI Product Launches” as gospel.
Cut down on redundant pilots: Pilots are important, but they should not be the default mode. Focus on initiatives that will provide tangible results and align with your business strategy. Pilots should have a clear end goal to either land in production or be sunsetted at a predetermined time.
Prioritize high-impact projects: Evaluate each AI initiative based on its potential to drive business impact. If it doesn’t directly contribute to your goals, it shouldn’t take up your team’s time. This is easier said than done, particularly when product teams have little understanding of how AI metrics should be chosen and instrumented. Minimize vendor distractions: Once a solution is chosen, stop evaluating other options. Give the team the space to execute their tasks effectively. This is very hard for executives, I’ve found, particularly those constantly on X and LinkedIn, taking incremental “AI Product Launches” as gospel. Minimize vendor distractions Cut down on redundant pilots: Pilots are important, but they should not be the default mode. Focus on initiatives that will provide tangible results and align with your business strategy. Pilots should have a clear end goal to either land in production or be sunsetted at a predetermined time. Cut down on redundant pilots Prioritize high-impact projects: Evaluate each AI initiative based on its potential to drive business impact. If it doesn’t directly contribute to your goals, it shouldn’t take up your team’s time. This is easier said than done, particularly when product teams have little understanding of how AI metrics should be chosen and instrumented. Prioritize high-impact projects By acting as a signal umbrella, you’ll help your team stay on track, execute with clarity, and achieve meaningful results. signal umbrella Without the Umbrella, AI Projects Stall Without the Umbrella, AI Projects Stall The focus should always be on driving business impact, which requires some strategic brainstorming and workshopping, as AI metrics are quite different from the metrics of the last decade of software products. For example, instead of attending multiple vendor demos or exploring every new AI tool on the market, establish a clear framework for evaluating AI ROI. Once a decision is made, delegate the execution, define the appropriate metric,  and let the team focus on delivering solutions. Without a strong signal umbrella, AI projects become bogged down by distractions, leading to frustration, wasted resources, and a lack of tangible outcomes. Practical Recommendations for AI Leaders Practical Recommendations for AI Leaders If you're overseeing AI initiatives, there are several practical steps you can take to reduce noise and ensure your team remains focused: Set Clear, Measurable KPIs: Ensure every AI initiative is tied to measurable business objectives. AI projects should be tracked against clear KPIs that reflect their contribution to the business. This is often opaque and needs appropriate education in AI. Set Clear, Measurable KPIs Ruthlessly Prioritize Initiatives: Focus on high-impact AI initiatives. Avoid the temptation to try everything. Prioritize projects that deliver clear, measurable business results. Keep vendor pilots to a minimum–trust me, most are vaporware now in the early innings of AI anyway. Ruthlessly Prioritize Initiatives: Protect Your Team from Distractions: Shield your technical teams from unnecessary tasks that do not contribute to the project’s success. Every moment spent on irrelevant activities is time taken away from achieving meaningful results. Protect Your Team from Distractions Simplify Decision-Making: Streamline your decision-making and reporting processes. Keep the focus on actionable steps that move the project forward and deliver measurable impact. Remember that the complex dashboards you developed over the past decade are largely moot now, so a simple spreadsheet by the Product owner that leads the AI initiative / POC built from scratch may be your single best option to gain traction with your team. Simplify Decision-Making Plan for Scalability and Long-Term Impact: AI systems should be built with scalability and production deployments in mind. Focus on solutions that not only solve today’s problems but are also capable of evolving with the business. Most AI POCs work fine as POCs, rarely do they actually survive the many use cases in production, either back-office or with customers. Plan for Scalability and Long-Term Impact Build with Clarity and Focus Build with Clarity and Focus AI adoption doesn’t need to be a chaotic, complicated process. The key to success lies in focusing on outcomes, protecting your team from distractions, and ensuring that every initiative contributes to measurable business goals. As an AI leader, you must act as the signal umbrella, guiding your team through the noise and ensuring they stay focused on the work that matters. Once the noise is eliminated, AI projects become strategic solutions that deliver real business impact.

This story contains AI-generated text. The author has used AI either for research, to generate outlines, or write the text itself. 

The is an opinion piece based on the author’s POV and does not necessarily reflect the views of HackerNoon.

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