Hey there! This article is part of a series exploring how a digital marketing agency used AI to transform their operations. If you’re just joining us, catch up on Part 1, Part 2 and Part 3 to see how they revolutionised their processes.

Make Audits and Competitor Research More Data-Focused and User-Centred with AI Design Sprint™

You know how crucial it is to really get your market and nail those strategies, right? It’s the backbone of any successful agency. But let’s be honest, diving deep into audits and competitor research can feel like you’re lost in a time-consuming maze, and it’s easy to miss things.

A lot of agencies struggle with this – making those key processes both data-strong and user-focused. The result? You end up with insights that are a bit…well, incomplete, and strategies that don’t quite hit the mark.

But here’s the good news: I’m excited to share how the AI Design Sprint™ can totally change your discovery and strategy game. By bringing AI into the mix, agencies like yours can seriously boost your audits and competitor research, making them way more thorough and user-centered.

What does that mean for you? Data-driven strategies that actually work, deeper insights into your market, and a team that’s using the latest tech to stay miles ahead.

If you’re finding your agency is battling with research that takes forever, data analysis that feels like a puzzle, or teams that aren’t quite on the same page during discovery, then this article is definitely for you.

Ready to explore how you can bring precision and innovation to your discovery and strategy? Let’s dive in.

Understanding the Agency

The Agency That Struggled with Discovery and Strategy

With growth came a bunch of new roles: Strategy Manager, Research Analyst, Creative Director, Data Analyst, Account Executives, and Project Managers. Exciting, right?

But growth also brought some pretty big challenges. Their discovery and strategy processes? Fragmented. Audits? Often just scratching the surface. Competitor research? Lacking in depth. And trying to combine data-led insights with what users actually want? A total nightmare.

Plus, in the rush to get strategies out the door, they sometimes missed critical data, which meant campaigns that didn’t deliver and clients who weren’t happy.

Sound familiar at all?

Symptoms of Inefficiency

Audits often felt incomplete, making it hard to really understand the market.
Team members were doing research, but without a clear process, leading to data that didn’t match up and insights that clashed.
In the race to develop strategies, they sometimes missed what competitors were doing, putting their market position at risk.
Manual data analysis and research that was all over the place meant it took ages to go from discovery to strategy, making them less competitive.

The Six Staff Members: Why Each Role Matters

Let’s meet the team:

The Strategy Manager (SM) is in charge of developing strategies but struggles to bring together different research inputs.
The Research Analyst (RA) does those in-depth audits but finds it hard to link those findings with user insights.
The Creative Director (CD) makes sure strategies fit with creative ideas but often gets held up by incomplete data.
The Data Analyst (DA) delivers crucial data insights but is held back by data that’s scattered everywhere.
The Account Executives (AE) gather what clients need and their feedback but struggle to connect those insights with the overall strategy.
And the Project Manager (PM) coordinates the whole strategy process, trying to manage disjointed workflows and communication gaps to keep things on track.

Bringing these roles together in an online AI Design Sprint™ workshop? That’s where the magic happened. Led by a certified expert, their goal was clear: revamp their discovery and strategy process, unify what everyone was contributing, and build in AI-driven efficiencies from the ground up.

The Steps

Big changes need a solid plan. They used a structured AI Design Sprint™ process to make sure they covered every detail and everyone had a chance to speak up.

Mapping the Current Discovery and Strategy Process (Steps 1–3)

Step 1: Check Whether the Process Describes Reality

They started with Zoom and Miro, mapping out how they were currently handling discovery and strategy. Everyone shared how they did audits, competitor research, and developed and implemented strategies.

In just an hour, they had a picture of their current state, which looked a bit like this:

Client Briefing: The first meeting to get clear on what the client needs and wants.
Market Audit: A full analysis of the market.
Competitor Research: Looking at key competitors and what they’re doing.
Data Collection: Gathering all the relevant data.
Strategy Development: Creating strategies based on the data and insights.
Internal Review: Checking the strategies internally for accuracy and consistency.
Client Presentation: Showing the proposed strategies to the client.
Feedback and Adjustment: Getting feedback from the client and making any necessary changes.
Strategy Finalisation: Finalizing and implementing the approved strategies.
Performance Tracking: Monitoring how well the strategies are working.

During this step, the team talked about how each stage was carried out and found differences between what was written down and what was actually happening. This discussion revealed areas where informal practices had taken over from the planned workflow, giving them a true picture of their discovery and strategy process.

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

Next, they used red sticky notes to highlight the main problems.

They realized that the lack of standard audit templates was causing inconsistency in their market analysis. Manual competitor research was slowing them down when it came to gathering complete insights. Inconsistencies in messaging during strategy presentations sometimes led to overpromising, and tracking strategy performance became a mess during ongoing monitoring.

The team shared their frustrations, discussing how these pain points affected their daily work and the overall efficiency of the discovery and strategy process. This open conversation made sure everyone was on the same page about the challenges they were facing.

Step 3: Mark High-Value Points with Green Dots

Then, they prioritized the most important issues with green dots. Standardized audit templates, a more streamlined competitor research process, consistent strategy messaging, and automated performance tracking became their main focus for integrating AI.

In a lively discussion, team members voted on which pain points would make the biggest difference if they were fixed. This helped them concentrate on the areas that would have the greatest impact.

AI Cards and Prioritization (Steps 4–5)

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

They explored different AI solution categories.

AI Content Generation tools can help draft thorough audit reports with consistent quality.
AI Workflow Automation systems can streamline competitor research and data consolidation.
AI Data Integration solutions can unify data from various sources, making insights deeper and more accurate.
AI Compliance Checks ensure that all audits and research meet ethical and industry standards.

The team looked at each AI category, discussing how these solutions could solve the problems they had identified. By understanding what each AI tool could do, they were able to better match them to their specific needs.

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

Through dot-voting, they chose AI Content Generation to standardize audit reports, AI Workflow Automation to streamline competitor research, and AI Data Integration to unify data from multiple sources.

After reviewing the options, the team agreed that these three AI solutions would be the most effective in addressing their main pain points. This consensus ensured they were all on the same page and focusing on the solutions with the greatest potential impact.

Focusing the Sprint (Steps 6–7)

Step 6: Select Two Process Steps to Focus On

They decided to concentrate on the market audit process and the competitor research phase.

They identified ensuring comprehensive and consistent audits and streamlining competitor research as the areas where improvements would have the most significant impact. The team discussed which stages of the discovery and strategy process would gain the most from AI integration. By focusing on these two critical areas, they aimed to build a strong foundation for enhancing overall efficiency.

Step 7: Move and Reformulate the Two Selected Focus Points

These became their core focus: the AI-Powered Audit Generator, which creates thorough and standardized market audits, and the Automated Competitor Research Workflow, which streamlines the process of gathering and analyzing competitor data with minimal manual effort.

During this step, they redefined their focus areas to clearly outline how AI would improve each process. This clarity helped the team understand the specific roles AI would play in their discovery and strategy workflow.

Integrating and Examining Neighboring Steps (Steps 8–9)

Step 8: Integrate the Anchor Points into the Existing Process

They introduced the AI-Powered Audit Generator to create initial market audits based on standardized templates and client inputs.

At the same time, they implemented the Automated Competitor Research Workflow to manage competitor data collection, analysis, and reporting. They mapped out how these AI tools would fit into their current workflow, ensuring a smooth transition. The team discussed the integration points and how each role would interact with the new systems.

Step 9: See How Neighboring Steps Are Influenced

Now, AI can suggest key market trends and insights based on data during the market audit stage. Consistent strategy messaging improves the clarity and professionalism of their strategy presentations. Additionally, automated tracking ensures timely and organized performance monitoring, reducing missed data points and delays.

The team evaluated the effects of integrating AI into their discovery and strategy process. ¹ This helped them anticipate and address potential changes in related stages, ensuring a comprehensive improvement.

Big-Picture Review & People-AI Interactions (Steps 10–12)

Step 10: Rethink the Entire Process

They took a step back to make sure their new workflow addressed the initial pain points without creating new problems. It was all about ensuring AI would boost their efficiency and consistency while keeping their strategic and user-centered approach front and center.

The team conducted a thorough review, discussing whether the new integrations aligned with their overall goals and maintained their agency’s unique value. This step was crucial for ensuring that the AI tools complemented rather than compromised their discovery and strategy processes.

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

The Strategy Manager (SM) oversees the AI-Powered Audit Generator, providing strategic input and refining the outputs.
The Research Analyst (RA) works with AI tools to conduct in-depth audits and personalize research findings.
The Creative Director (CD) ensures AI-generated strategies align with the brand’s creative standards.
Account Executives (AE) input client requirements and feedback into the AI system.
The Data Analyst (DA) provides data-driven insights to inform audit and research content.
And the Project Manager (PM) coordinates the Automated Competitor Research Workflow, ensuring smooth data collection and timely analysis.

They clearly defined each team member’s role in the AI-enhanced process, making sure everyone knew how to use the new tools effectively. This clarity encouraged ownership and accountability within the team.

Step 12: Mark Sketches Where a Person Interacts with AI

They created a flow diagram to visualize these interactions.

The Strategy Manager inputs client requirements into the AI-Powered Audit Generator.
The Research Analyst reviews and personalizes AI-generated audit reports.
The Creative Director ensures alignment with brand standards.
The Project Manager uses the Automated Competitor Research Workflow to manage data collection and analysis.

By mapping out these interactions, the team gained a clear understanding of their touchpoints with the AI tools. This visual aid made collaboration smoother and highlighted areas where human input was essential.

Refining Anchors & Tasks (Steps 13–14)

Step 13: Revisit Anchor Points and Improve Descriptions

They refined their anchor points to ensure they were clear and precise.

The AI-Powered Audit Generator now creates comprehensive market audits based on standardized templates, client inputs, and data-driven insights.
The Automated Competitor Research Workflow manages competitor data collection, analysis, and reporting through AI-driven task assignments and notifications.

This step helped the team fully understand the functionalities and benefits of each AI tool, making implementation easier.

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

For the AI-Powered Audit Generator:

Template Configuration: Define standardized audit templates with consistent categories and structure.
Client Data Integration: Input client-specific information and market requirements into the AI system.
Audit Generation: AI generates the initial market audit based on templates and client data.
Human Refinement: The Strategy Manager reviews, edits, and personalizes the AI-generated audit.

For the Automated Competitor Research Workflow:

Submission for Research: Initiate competitor research based on client requirements.
Data Collection: AI gathers data from various sources on key competitors.
Analysis and Reporting: AI analyzes the collected data and generates competitor reports.
Final Review and Approval: The Strategy Manager oversees the final review process, ensuring reports meet all standards before presentation.

Breaking down each anchor point into actionable sub-steps provided a clear roadmap for implementation. The team could now follow these detailed steps to ensure every aspect of the process was addressed effectively.

Final Declarations & Ethical Checks (Steps 15–17)

Step 15: Restate the AI Technologies Used

They used AI Content Generation through natural language processing models for drafting comprehensive audit reports.

AI Workflow Automation was implemented using machine learning algorithms to streamline competitor research processes.

AI Data Integration consolidated data from Sales, Research, and Strategy teams into a unified system.

They reiterated the technologies involved to ensure everyone was on the same page about the tools they were implementing. This step reinforced the technical foundation of their new process.

Step 16: Give the New Process a Catchy Name

They named their new process “StrategySync,” highlighting the seamless synchronization and intelligent automation it brings to their discovery and strategy development.

Choosing a memorable name helped the team embrace the new process. It fostered a sense of identity and ownership, making it easier to communicate and refer to the system internally.

Step 17: Individually Select Relevant AI Ethics Cards

They selected Transparency, Privacy, Fairness, and Accountability.

Transparency ensures they clearly communicate AI involvement in audits and competitor research.
Privacy safeguards client data within the AI systems.
Fairness avoids biased data analysis and ensures equitable treatment of all client data.
Accountability maintains human oversight over AI-generated audits and research reports to ensure accuracy and integrity.

Addressing ethical considerations was paramount. The team discussed how to uphold these principles, ensuring their AI integrations were responsible and trustworthy.

Scenario Planning & Realignment (Steps 18–21)

Step 18: Write Best- and Worst-Case Scenarios

In the best-case scenario, StrategySync streamlines audits and competitor research, enhances data accuracy, and reduces strategy development time by 50% within three months, leading to a 20% increase in client satisfaction and retention.
In the worst-case scenario, AI-generated audits lack depth, leading to ineffective strategies and a decline in client trust.

The team brainstormed potential outcomes, both positive and negative. This helped them prepare strategies to maximize benefits and mitigate risks.

Step 19: Reflect on Implications for the AI Solution

To mitigate risks, they implemented rigorous Quality Assurance measures to ensure depth and accuracy in AI-generated audits.

Continuous Training of AI models with new data improved relevance and authenticity.

Additionally, Feedback Loops encouraged team feedback to refine AI tools continuously.

They discussed practical steps to address the worst-case scenarios, ensuring that their AI tools remained effective and trustworthy.

Step 20: Share with the Team and Align

They held an all-hands virtual meeting to present StrategySync.

Each role understood their responsibilities and the importance of collaboration in the AI-enhanced workflow. The team was excited and committed to the new process.

Sharing the plan with the entire team fostered alignment and enthusiasm. Everyone felt involved and motivated to contribute to the successful implementation of the new system.

Step 21: Go Back to the Solution and Refine

Based on team feedback, they integrated advanced analytics features for Research Analysts to easily modify AI-generated audits, simplified the Automated Competitor Research Workflow for easier navigation and management, and scheduled comprehensive training sessions to ensure all team members were proficient with the new tools.

Refining the solution based on feedback ensured that the tools were user-friendly and met the team’s needs effectively. This iterative improvement was key to smooth adoption.

Note: Typically at this point a “Tech Check” is done by either external partners or internal tech teams to see if 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

They rolled out a pilot version of StrategySync with select clients.

Clients appreciated the data-driven and comprehensive audit reports, but some wanted more personalized insights, prompting further refinement. Engaging external users provided valuable insights into how their discovery and strategy processes were perceived, highlighting both strengths and areas for improvement.

Step 23: Seek Feedback

They compiled feedback into a Miro board, highlighting strengths such as improved audit quality and reduced research time, alongside areas for improvement like enhanced personalization and flexibility in strategy recommendations.

Organizing feedback systematically allowed them to identify common themes and prioritize adjustments effectively.

Step 24: Discuss Feedback and Improve the Concept

Key adjustments included configuring AI tools to incorporate client-specific nuances and preferences, enabling the AI-Powered Audit Generator to learn from past audit successes and client feedback for better future outputs, and improving the workflow interface’s usability based on client and team suggestions.

Implementing these refinements ensured that StrategySync was more responsive to client needs and user-friendly for their team.

With these refinements, StrategySync was ready for full-scale implementation.

The Final Solution – “StrategySync”

Recap of Pain Points Addressed

By the end of their 24-step journey, they had successfully tackled several key issues.

Inconsistent Asset Collection was resolved through standardized templates and AI-generated audit reports, ensuring uniformity across all discovery processes.
Disjointed Handoff Processes were unified with AI Workflow Automation, bringing together client information from Sales and Strategy teams seamlessly.
Overpromising Capabilities were mitigated by Automated Review Workflows that incorporated checks to prevent incomplete audits and research.
And time-consuming processes were significantly reduced through AI Workflow Automation, streamlining the discovery and strategy formulation timelines.

The Pilot & Next Steps

Next Steps

Looking ahead, they plan to integrate machine learning models to enhance audit generation with more nuanced and personalized insights.

They aim to incorporate deeper personalization features to cater to diverse client needs and implement AI-driven analytics to predict strategy success rates and optimize their strategies accordingly.

Why a Step Process Works

Thoroughness vs. Speed

While it’s tempting to deploy AI solutions quickly, the complexity of discovery and strategy demands a thoughtful approach.

Their structured AI Design Sprint™ ensured they identified problems thoroughly, tackling the root causes instead of just the symptoms.

Systematic AI exploration allowed them to methodically evaluate AI solutions to find the perfect fit.

Inclusive team involvement engaged everyone, fostering ownership and collaboration.

And robust testing and feedback integration validated solutions through pilot testing and iterative refinements.

Everyone Sees Their Part in the Puzzle

From the Strategy Manager overseeing AI-powered audit generation to the Analytics Specialist leveraging unified data for strategic decisions, every role understood their contribution.

This holistic involvement ensured StrategySync was seamlessly integrated and embraced across the agency.

AI with Purpose, Not Gimmick

Each AI solution directly addressed specific pain points.

AI Content Generation enhanced consistency and efficiency in audit reports.
AI Workflow Automation streamlined competitor research processes, reducing delays and bottlenecks.
AI Data Integration unified team inputs for coherent and comprehensive discovery and strategy.

Ethical considerations, such as transparency and accountability, were integral, ensuring AI served as a tool for empowerment rather than replacement.

Key Takeaways

The Resulting “StrategySync”

Their final StrategySync includes standardized audit templates, ensuring consistent quality and clarity across all discovery processes.

The AI-Powered Audit Generator drafts comprehensive market audits based on standardized templates and client inputs.

Human Refinement allows Strategy Managers to personalize and enhance AI-generated audits.
The Automated Competitor Research Workflow manages competitor data collection, tracks feedback, and ensures timely analysis.
Unified Data Integration consolidates inputs from Sales, Research, and Strategy teams into a single discovery system.
And their ethics framework maintains transparency about AI involvement and ensures client data privacy.

Forward-Looking Upgrades

With StrategySync in place, the agency plans to develop a machine learning-based audit generator to enhance personalization and contextual relevance.

They aim to expand to multimedia strategy elements by incorporating AI tools for generating and integrating multimedia insights.

Additionally, integrating predictive analytics will help forecast strategy success rates and proactively adjust their strategies.

Last Thoughts

In the fast-paced world of digital marketing, it’s key to blend data-driven insights with user-centered approaches.

Integrating AI into your discovery and strategy phase isn’t just about keeping up with trends—it’s about strategically implementing tools that solve real challenges and drive meaningful results.

For agencies struggling with inefficient discovery and strategy processes, embracing a structured AI Design Sprint™ can lead to a unified, efficient, and data-driven strategy ecosystem.

So, gather your team, dive into a collaborative and watch your discovery and strategy efforts transform into a synchronized and impactful force.

Part 4 of 11: Discovery and Strategy