I’ve always believed that how you capture and qualify leads is the truest barometer of a digital marketing agency’s readiness to grow. You can build jaw-dropping campaigns, pull off clever brand strategies, and publish data-packed reports, but if your leads are slipping into half-forgotten spreadsheets, your entire operation is sailing with an anchor.

This article explores precisely how an online AI Design Sprint™ workshop can help a fast-growing digital marketing agency, rescue its lead funnel from chaos, unify data in real time, and embed AI at the earliest, most critical juncture of client engagement.

Through the AI Design Sprint™ workshop, they were able to create a cohesive funnel that merges generative chatbots, AI scoring, aggregator forms for phone and DMs, with ethical oversight to ensure no promising lead gets lost.

If your agency or business is grappling with a scattered lead process, where staff guess who to call next, read on, as this article might just offer a transformative template to follow.

UNDERSTANDING THE AGENCY CONTEXT

The Agency That Outgrew Its Lead Funnel

The agency at the heart of this story had a classic problem: rapid growth.

Over the last few years, they’d diversified from being a niche PPC shop into a multi-service digital marketing firm. New roles appeared, like a Data Analyst, a Social Media Coordinator, an expanded Creative team, and a dedicated Paid Media Specialist. Growth is exciting, but success can bring blind spots.

Leads poured in from LinkedIn Ads, Google Ads, random referrals, phone calls, and even social DMs. Yet the funnel for handling them remained a patchwork of spreadsheets, Slack notifications, and “I’ll get to it tomorrow” mental to-do lists.

Symptoms of Overload: The Account Manager might see 10 new leads appear in a sheet over the weekend. Some had partial info (“Name: John, Email: [email protected], Message: I want help.”). Others had zero data about budget or timeline. Meanwhile, the Paid Media Specialist had no clue which campaign those leads came from, making ROI tracking nearly impossible. And the Social Media Coordinator found herself copying and pasting Instagram messages to Slack, hoping someone would notice. Real opportunities died in the confusion.

The Six Staff Members: Why Each Role Matters

Let’s name them:

Account Manager (AM): She deals directly with new inquiries. She’s responsible for deciding who to call, but her guesswork-based approach leaves her anxious about ignoring a diamond in the rough.
Strategist (ST): He frames big-picture campaign goals and wants reliable data on which ad spends yield profitable clients. He’s tired of seeing only partial lead info.
Creative Director (CD): She cares about the brand experience. If a user’s first contact is a half-baked form, that tarnishes the brand. She’s interested in how a generative chatbot might greet visitors elegantly.
Data Analyst (DA): She sees the biggest pains in measuring real ROI. Without consistent data entry, her monthly or quarterly reports remain incomplete.
Paid Media Specialist (PMS): She invests budgets in LinkedIn or Google, but without channel attribution in the funnel, she can’t validate whether a lead is valuable or not.
Social Media Coordinator (SMC): She manages DMs on Instagram, Twitter, or Facebook. Some are genuine leads, yet they fall off the radar if not immediately logged.

All these roles came together online using Miro for a multi-day AI Design Sprint™ workshop led by a certified facilitator.

The workshop’s single-minded focus was on Workflow 1: Lead Generation and Qualification, the earliest step in the client lifecycle. They systematically walked through each pain point, introduced AI Cards, discovered an approach that everyone could endorse, and tested it with pilot data and a prototype.

The result was a dramatic shift from scattered guesswork to a methodical, AI-backed funnel.

The Steps We Followed

Each of these steps is an extra layer of reassurance that your final concept is robust, not just a fancy idea that collapses once you attempt real-world deployment.

Let’s briefly restate these steps we’ll cover in detail:

Check Whether the Process Describes Reality
Mark and Describe Pain Points with Red Post-Its
Mark High-Value Points with Green Dots
Go Through AI Cards Two at a Time, Copy Them If Relevant
Prioritise the Three Most Important AI Cards for Each Step
Select Two Process Steps to Focus On
Move and Reformulate the Two Selected Focus Points
Integrate the Anchor Points into the Existing Process
See How Neighbouring Steps Are Influenced
Rethink the Entire Process
Add How and Where People Could Help AI Perform Well
Mark Sketches Where a Person Interacts with AI
Revisit Anchor Points and Improve Descriptions
Break Each Anchor Point into Four Sub-Steps
Restate the AI Technologies Used
Give the New Process a Catchy Name
Individually Select Relevant AI Ethics Cards
Write Best- and Worst-Case Scenarios
Reflect on Implications for the AI Solution
Share with the Team and Align
Go Back to the Solution and Refine
Pitch the Solution to External Users
Seek Feedback
Discuss Feedback and Improve the Concept

We’ll use these steps through a story: how the funnel was diagnosed, which AI solutions were chosen, how ethics got addressed, and how the final “AI Leads Flow” solution was born and tested.

MAPPING THE CURRENT FUNNEL (STEPS 1–3)

Step 1: Check Whether the Process Describes Reality

We started the workshop online using Zoom and Miro by drawing the funnel, step by step, using the staff’s real experiences. We asked each person to place sticky notes for how leads show up, where they record them, or if they do. Within 45 minutes, we pinned the final layout:

User Notices an Ad or Hears a Referral
User Lands on the Website or Social Profile
User Provides Minimal Info
Data Might Go to a Spreadsheet or Slack
No Unified Channel Attribution
AM Eventually Notices
AM Performs a Gut-Check
High-Potential Leads Get a Follow-Up

If you jump straight to brainstorming AI solutions without seeing the actual funnel, you risk ignoring half the problem. The Social Media Coordinator specifically noted that phone calls or inbound DMs sometimes skip process steps 3 and 4, going straight to process step 6 if staff remember. This revelation showed the agency they had a bigger organisational gap than they’d realised.

Step 2: Mark and Describe Pain Points with Red Post-Its

We used Miro’s red sticky notes to label each step with the worst obstacles:

Process Step 3: “We rarely get budget or timeline details, so we guess.”
Process Step 4: “Data scattered across multiple spreadsheets/Slack threads.”
ProcessStep 5: “No idea which ad or referral source for each lead.”
ProcessStep 7: “AM decides with no consistent criteria.”
ProcessStep 8: “Slow or no follow-up if we’re busy.”

By naming these pain points, each staffer saw how daily tasks get derailed. The Data Analyst pinned “Scattered data kills monthly reporting,” while the Paid Media Specialist pinned “Can’t see which LinkedIn campaign delivered a real client.” Everyone recognised the funnel wasn’t just mildly messy, it was systematically losing opportunities.

Step 3: Mark High-Value Points with Green Dots

Next, each staffer took three green dots to place on whichever red notes they felt most urgent or impactful to fix. The top priorities emerged:

No budget/time details at process step 3.
Scattered data at process step 4.
No channel attribution at process step 5.
Guess-based qualification at process step 7.

This clarified which problems, if solved, would yield the greatest improvement. For instance, if we know the user’s budget is $3k/month, that alone helps the AM skip hunch-based decisions. If staff unify data in one aggregator, the Data Analyst can measure ROI properly. Focusing on these high-value points steers the next steps away from less critical annoyances.

AI CARDS AND PRIORITISATION (STEPS 4–5)

Step 4: Go Through AI Cards Two at a Time, Copy Them If Relevant

We had Miro boards that introduced AI categories in pairs, so staff could read short bullet points describing each card’s typical application:

AI Finds and Organises Information (aggregators, data sorting)
AI Predicts Future Events (lead scoring, churn prediction)
AI Summarises or Improves Text (turning user free-text into structured fields)
AI Chats and Talks (generative chatbot for user queries)
AI Gains Insights from Big Data
… plus the Joker Card for custom ideas like “Generative Chatbot that personalises brand voice.”

They each copied relevant cards and placed them near the funnel steps where they saw a solution.

Instead of just brainstorming “AI might do something,” staff saw concrete examples. The Social Media Coordinator realised “AI Summarises or Improves Text” could parse inbound social DMs, turning them into partial lead entries. The Data Analyst pinned “AI Finds and Organises” for unifying data from multiple channels. The Creative Director created a Joker Card for a brand-aligned chatbot that says, “Hey! Let’s talk marketing goals, give me your budget or timeline!” This step supercharged creativity and prevented staff from being limited by the AI they already knew.

Step 5: Prioritise the Three Most Important AI Cards for Each Step

We used dot-voting again, but this time for each funnel step, we asked staff to pick the top three AI solutions they felt would best address the red-posted pains. After tallying:

Generative Chatbot (Joker Card) rose to the top for step 3 (user data capture).
AI Finds and Organises Information emerged as essential for step 4–5 (unifying scattered data, adding channel attribution).
AI Predicts Future Events (lead scoring) soared near step 7 (replacing the AM’s guess-based approach).

This sets the foundation for the rest of the sprint. While “AI Summarises or Improves Text” or “AI Gains Insights from Big Data” were also interesting, they weren’t as critical for immediate transformation. So we singled out the chatbot, aggregator, and lead-scoring approach as our prime AI solutions.

FOCUSING THE SPRINT (STEPS 6–7)

Step 6: Select Two Process Steps to Focus On

We needed to zero in on where these AI solutions would do the most good. The funnel has eight steps, so we asked: “Which two steps, if overhauled, would fix the biggest chunk of chaos?” The team chose:

Process Step 3 (“User Provides Minimal Info”) to transform with a chatbot or enhanced form.
Process Step 7 (“AM’s Gut-Check”) to transform with lead scoring.

It’s easy to try patching every step, but that often leads to half-baked solutions. By concentrating on process steps 3 and 7, we addressed the high-priority pain points: insufficient user data and guess-based qualification. This ensures a deeper fix rather than a superficial band-aid across many spots.

Step 7: Move and Reformulate the Two Selected Focus Points

We “moved” these two steps to a new, central area of Miro , labeling them as “anchor points.” Then we rewrote them in clearer language:

Anchor A: “Generative Chatbot / Enhanced Form” captures user budget, timeline, brand challenge, plus channel attribution in a single aggregator.
Anchor B: “AI-Based Lead Scoring” uses the aggregator’s data to produce a numeric score. High scorers trigger immediate staff alerts; others go into a standard drip.

By reframing them as anchor points, we aligned the entire sprint around building these solutions. Everyone saw how anchor A addresses “lack of user data,” while anchor B tackles “guess-based qualification.”

INTEGRATING AND EXAMINING NEIGHBOURING STEPS (STEPS 8–9)

Step 8: Integrate the Anchor Points into the Existing Process

We took the old steps 3 (Minimal Info) and 7 (Gut-Check) and replaced them:

At Process Step 3: The visitor now sees a brand-friendly chatbot or advanced form. The aggregator logs their info, including channel source.
At Process Step 7: Instead of the AM guess, an AI model or rule-based logic scores the lead. If the score is ≥ X, Slack or email notifies the AM. If below X, it’s a standard follow-up track.

We also inserted a short aggregator form for phone leads or DMs. So if someone calls in, staff type in key fields like name, approximate budget, and domain. That merges into the aggregator, too.

This integration ensures we don’t create a side system that staff forget. We replaced old steps with new, AI-driven steps. The entire funnel from user arrival to staff response now flows seamlessly.

Step 9: See How Neighbouring Steps Are Influenced

We asked, “How does fixing process steps 3 and 7 change process steps 4, 5, or 8?” The aggregator at process step 4 now merges data instantly. Process step 5 (no channel attribution) is resolved because the form or chatbot includes UTMs or referral IDs. Process step 8 sees a faster follow-up for high-scorers.

By confirming these ripple effects, we prevented new friction. For example, the Social Media Coordinator realised that phone calls bypass steps 2–3 if the user never visits the site, but logging them with the aggregator form handles it. So the entire funnel remains consistent.

BIG-PICTURE REVIEW & PEOPLE-AI INTERACTIONS (STEPS 10–12)

Step 10: Rethink the Entire Process

We stepped back to see if the new funnel, with anchors A (Chatbot) and B (Lead Scoring), truly solves the original issues. The group concluded it does, as long as staff truly adopt the aggregator form for phone leads or DMs. Without that compliance, the solution fails for inbound calls.

This big-picture review is your chance to confirm alignment with the agency’s mission. If capturing budget details and providing immediate scoring speeds up follow-ups, staff become more efficient, and the Data Analyst can measure ROI. All goals check out.

Step 11: Add How and Where People Could Help AI Perform Well

We documented each role’s new responsibilities:

Account Manager (AM): Oversees final calls, can override AI scores if she suspects a lead is more/less viable.
Social Media Coordinator (SMC): Must fill aggregator forms for phone or DM leads, ensuring data completeness.
Paid Media Specialist (PMS): Must attach UTMs to each ad so the aggregator can log the channel.
Creative Director (CD): Must ensure chatbot dialogues remain brand-friendly, not pushy or robotic.

AI is never fully autonomous, “not yet”! This step clarifies exactly where staff oversight or data entry is critical. No aggregator can unify phone calls if staff never log them. No scoring model can function if the user’s budget is never recorded.

Step 12: Mark Sketches Where a Person Interacts with AI

We drew a simple Miro flow diagram:

User interacts with the chatbot or form at step 3.
Aggregator merges data, triggers scoring at step 7.
AM sees Slack alerts if lead is high-scoring.
Override if needed.

We highlighted each point where user or staff input is needed. The Chatbot is the user’s AI interaction, the aggregator-scoring step is staff’s AI interaction, Slack alert is staff again.

By visually marking these interactions, no one confuses the user’s tasks with staff tasks. The Paid Media Specialist doesn’t have to do anything at step 3, but the Social Media Coordinator does if the lead arrives via DM. Each interaction is clearly assigned.

REFINING ANCHORS & TASKS (STEPS 13–14)

Step 13: Revisit Anchor Points and Improve Descriptions

We re-labeled anchor A and anchor B with more precise language:

Anchor A: “Generative Chatbot or advanced form that collects budget, timeline, brand context, channel source, merges data in aggregator.”
Anchor B: “AI lead scoring, rule-based or ML-based logic awarding points, Slack/email alerts above threshold, with a human override.”

Now everyone sees exactly which fields the chatbot asks, which points the scoring model uses. If you stay vague, “The chatbot just collects some info”, you’ll cause confusion. Crisp definitions help the dev or the data team implement effectively.

Step 14: Break Each Anchor Point into Four Sub-Steps

For each anchor, the team detailed four mini-steps or tasks:

Anchor A (Chatbot / Enhanced Form)

Design & Brand: The Creative Director pairs with a dev to finalise chatbot UI and brand voice.
Aggregator Setup: The Data Analyst ensures data from the chatbot merges into the CRM.
Channel Tagging: The Paid Media Specialist includes UTMs so aggregator logs lead source.
Phone/DM Logging: The Social Media Coordinator uses a “New Lead Quick Form” for non-site leads.

Anchor B (AI Lead Scoring)

Historical Data: Gather ~300 old leads labeled as “won” or “lost” to see patterns.
Rule-Based Approach: Award points for budget ≥ $3k, timeline < 3 months, known domain.
Notification System: If score ≥ 70, Slack/email the AM.
Monitor & Adapt: The Data Analyst checks accuracy monthly, adjusting thresholds or rules.

Breaking them into sub-steps transforms a “cool concept” into an actionable plan. Each item has an owner and a clear goal, ensuring the solution is truly implementable.

FINAL DECLARATIONS & ETHICAL CHECKS (STEPS 15–17)

Step 15: Restate the AI Technologies Used

In Miro, we pinned a final summary:

AI Chats and Talks for the generative chatbot at process step 3.
AI Finds and Organises Information for aggregator unification.
AI Predicts Future Events for the lead scoring logic.

By listing them, the team sees which AI capabilities must be set up or purchased. The dev might pick a chatbot framework, the Data Analyst might code a rule-based scoring or a partial ML model if data grows.

Step 16: Give the New Process a Catchy Name

We asked each staffer to propose a name. Options included “AQL Flow,” “AI-Enhanced Lead Pipeline,” or “AI Leads Flow.” They dot-voted and picked “AI Leads Flow,” emphasising speed and intelligence.

A name like “AI Leads Flow” fosters internal buy-in. Staff can say “Let’s push that lead into our AI Leads Flow,” making it part of daily vocabulary.

Step 17: Individually Select Relevant AI Ethics Cards

We showed the participants the AI Ethics Cards, like “Transparency,” “Privacy,” “Fairness,” “Accountability,” “Autonomy.” Each staffer picked the ones they felt most applicable to the anchor points. For example, the Data Analyst worried about fairness if the scoring model undervalued small-business leads. The AM worried about privacy for user-provided budget info.

It ensures that when we roll out the chatbot or aggregator, we handle data responsibly. For instance, the bot can display a short privacy statement: “We use your data to deliver better follow-ups, no spam or third-party sharing.” And the “human override” mitigates the risk of the AI ignoring certain leads.

SCENARIO PLANNING & REALIGNMENT (STEPS 18–21)

Step 18: Write Best- and Worst-Case Scenarios

The staff wrote short paragraphs describing how “AI Leads Flow” might excel or fail:

Best Case: The chatbot receives ~40% usage, collects budget/time data, aggregator unifies 90% of leads, AI scoring is accurate enough that staff respond to top leads within hours, conversion rates rise significantly.
Worst Case: The chatbot annoys visitors, staff forget to log phone leads, the aggregator is incomplete, AI scoring yields false positives, so staff chase the wrong leads, missing real opportunities.

This scenario planning reveals possible pitfalls. If staff sees the “Worst Case,” they become proactive about ensuring compliance. If the “Best Case” requires certain UTMs or a brand-friendly chat design, they commit to those details.

Step 19: Reflect on Implications for the AI Solution

We asked, “Given these best- and worst-case scenarios, what do we need to do to keep the solution stable?” They concluded:

Staff Compliance: They must log phone or DM leads.
Chatbot Tuning: The chatbot can’t harass visitors. If data suggests a user closes it quickly, show a simpler fallback form.
Threshold Adjustments: The scoring system might start with a threshold of 70 and get raised or lowered after a month of data.

AI solutions often fail if the staff can’t feed them consistent data. By clarifying these implications, the agency recognised the need for regular aggregator reviews, ethical overrides, and brand-friendly chatbot design.

Step 20: Share with the Team and Align

We held a short “all-hands” Miro session where each department lead or role got to see the updated funnel, anchors, sub-steps, best/worst cases, and ethical checks. Everyone confirmed the approach solved their specific pain points.

If, say, the Social Media Coordinator never saw a reason to fill aggregator forms, the solution might fail. This alignment ensures no role is left behind, removing friction that could reintroduce scattered data or guess-based decisions.

Step 21: Go Back to the Solution and Refine

After hearing final input, we made small refinements:

We set the scoring threshold to 75 (not 70), so the AM sees slightly fewer “urgent leads.”
We introduced a special brand script for the chatbot: “Hey there! Ready to chat about your marketing goals?” with an easy skip button.
We formalised a 1-minute aggregator form for phone calls or DMs, with name, domain, approximate budget, and the user’s main request.

These final tweaks ensure the solution’s day-to-day usage matches the staff’s bandwidth and brand style. If the threshold was too low, the AM might get bombarded. If the chatbot was too pushy, it might repel leads.

Note: Typically at this point a “Tech Check” is done by either external partners or internal tech teams to see it the proposed solution is viable from a technical point of view. If yes, there would be an agile prototype phase to develop and fine-tune before going into full development and integration.

PILOT & EXTERNAL FEEDBACK (STEPS 22–24)

Step 22: Pitch the Solution to External Users

The dev built a minimal version of the chatbot for a subpage, tested aggregator logs, and set up a Slack integration for scoring. The agency invited about a dozen “friendly testers,” including a couple of existing clients, to try the new funnel. The testers provided immediate commentary:

“I liked that the chatbot introduced itself politely, not spamming me.”
“It asked about my monthly budget, which was a bit direct, but I get it.”
“The skip button was helpful if I wasn’t ready to chat thoroughly.”

Real user impressions highlight hidden friction. If multiple testers said the budget question was too abrupt, the agency might soften the language. This external perspective is your final reality check before a full launch.

Step 23: Seek Feedback

They compiled the testers’ feedback in a Miro table, listing each user’s top praises and complaints. For instance, one user found the “domain-based” question confusing. Another said the skip button was too small. Another praised how quickly the AM followed up after they typed “$5k monthly budget.” This all fed into a simple rating of user satisfaction.

By capturing structured feedback, the team can decide systematically which points to fix now vs. later. They avoid personal biases or ignoring certain testers.

Step 24: Discuss Feedback and Improve the Concept

In the final step of the sprint, the staff met again to weigh the testers’ comments. Key changes included:

Making the budget question slightly friendlier: “What monthly budget are you considering for marketing? No worries if you’re unsure, just pick a range.”
Ensuring the skip button was bigger or more obvious.
Setting the Slack notification to also email the AM in case Slack was missed.

Then they deemed the pilot version ready for broader rollout.

This is your chance to catch lingering friction. If you skip it, you risk launching a solution that frustrates users or fails to unify data. With these final tweaks, “Lightning Leads AI Flow” became genuinely user-centric and staff-friendly.

THE FINAL SOLUTION – “AI LEADS FLOW”

Recap of Pain Points Addressed

By the end of these 24 steps, the agency resolved:

Minimal Data from Users: The generative chatbot or enhanced form asks for budget, timeline, brand needs, so staff skip guesswork.
Scattered Data: The aggregator merges all leads in one CRM record, letting the Data Analyst measure ROI properly.
No Channel Attribution: The aggregator logs UTMs for each ad or referral link, fixing a major gap.
Guess-Based Qualification: The AI lead scoring model ensures high-value leads get immediate attention.
Slow or Missed Follow-Ups: Slack or email alerts prompt same-day calls for top leads.

The Pilot Outcomes & Next Steps

Possible outcome within weeks of launch:

One-third of site visitors used the chatbot, providing budget/timeline info. The aggregator stored about 90% of inbound leads consistently, including phone calls entered by staff. Lead Scoring (threshold ~75) triggered ~20% of leads as “hot,” and the AM called them within a few hours. Early signs suggest a better close rate. The Data Analyst finally correlated “LinkedIn Ad #2” leads with higher budgets. The Paid Media Specialist planned to invest more in that campaign.

Challenges can remain therefor the staff must remain vigilant about logging phone leads, the chatbot is still under refinement to perfect its brand tone. The agency aims to gather enough data to train a modest ML model, replacing the rule-based approach with a more adaptive scoring logic. But the transformation from muddled guesswork to data-centric speed is already visible.

WHY A STEP PROCESS WORKS

Thoroughness vs. Speed

Some AI sprints skip half these steps, but the complexity of lead funnels demands a methodical approach. You can’t just “slap a chatbot in place” and call it done. This framework ensures you:

Identify real friction, not imagined.
Explore AI Cards systematically, not haphazardly.
Involve everyone from the Account Manager to the Creative Director so no crucial role is overlooked.
Incorporate user feedback and worst-case scenario planning for real-world reliability.

Everyone Sees Their Part in the Puzzle

From day one, the Social Media Coordinator recognised how phone and DMs needed to feed the aggregator. The Paid Media Specialist addressed channel attribution. The Creative Director shaped brand-friendly chat dialogues. The AM got an immediate Slack alert system. The Data Analyst overcame reporting gaps. That synergy is the hallmark of a well-run sprint.

AI with Purpose, Not Gimmick

We explicitly matched AI solutions to the red-posted pains. We found generative chat for user data capture, aggregator for data unification, lead scoring for prioritization. Each addresses a distinct problem, rather than layering AI for novelty. The ethics step further ensures we’re transparent with user data and keep a “human override” to handle unusual or edge cases.

KEY TAKEAWAYS

The Resulting “AI Leads Flow”

After following the steps of concept development, the agency’s final solution looked like this:

User Lands on site or sees an ad.
Chatbot or Enhanced Form asks for budget, timeline, brand notes, storing them in an aggregator with UTMs if from an ad.
Aggregator merges leads in a single CRM or database entry, so no more scattered spreadsheets.
AI Scoring quickly rates the lead. If score ≥ 75, Slack notifies the AM. If lower, we keep them on a standard track.
Staff enters phone or DM leads manually in a short aggregator form, ensuring no lead is lost.
Ethics: The user sees a short disclaimer about data usage. Staff keep a human override on the scoring.

The synergy attacks all major pain points:

Minimal user info was resolved by the chatbot or form’s fields.
Scattered data was resolved by the aggregator.
No channel attribution was fixed by UTMs or manual selection in the aggregator.
Guess-based qualification was replaced by AI scoring plus AM override.
Slow or missed response is remedied by Slack/email alerts.

Lessons

To successfully integrate AI into your processes, ensure it is custom-fit to address the most pressing pain points within your funnel rather than applying it indiscriminately.

Foster a cross-functional approach by involving all roles, overlooking a key contributor, such as the Social Media Coordinator, could derail critical elements like the aggregator form. Maintain ethical oversight by implementing human override mechanisms and providing transparent data usage disclaimers to address concerns around privacy and fairness.

Conduct a pilot with real or friendly users to uncover potential oversights and refine elements like chatbot scripts or scoring thresholds.

Finally, assign a distinctive name, such as “AI Leads Flow,” to give the initiative a cohesive identity that staff can embrace and rally behind, ensuring smoother adoption and implementation.

Forward-Looking Upgrades

Once the agency logs enough leads (perhaps 1,000+), the Data Analyst can attempt a small ML model for lead scoring. They might also incorporate “AI Summarises or Improves Text” so the chatbot or aggregator can parse free-text brand briefs. Meanwhile, the Creative Director might explore a voice-based approach for phone leads. The fundamental architecture, chatbot, aggregator, scoring, remains the bedrock for further expansions.

Last Thoughts

In an industry obsessed with quick wins, dedicating time to a AI Design Sprint™ Workshop might seem like overkill. Yet for agencies drowning in lead chaos, it’s exactly this structured thoroughness that fosters real transformation. By systematically walking from verifying reality (Step 1) to refining post-user-feedback (Step 24), you ensure no detail is missed, no role is left behind, and no AI solution is introduced without a clear purpose.

If you’re facing a similar meltdown in your lead funnel, consider adopting a similar process. Rally your staff, open Miro or your favourite collaborative tool, and give each step the attention it deserves. You may well emerge with your own “AI Leads Flow,” ready to convert inbound interest into real, high-value client relationships.

Notes:

The AI Design Sprint™ was developed by Michael Brandt and his team from 33A in Denmark.

Jacobus van Niekerk is a certified AI Design Sprint™ Workshop Facilitator from CATICS based in the Netherlands. If you’d like to connect further on the topic, feel free to reach out or book a quick online meeting.

Part 1 of 11: Lead Generation & Qualification with AI Design Sprint™.