At SaaStr Annual + AI Summit 2025, Asana CMO Shannon Duffy shared how her team achieved 93% efficiency gains using AI daily – and the hard lessons learned from building “janky Britney Spears” content along the way.
Top 5 Learnings from Asana’s AI Marketing Transformation
- Daily AI usage drives 93% efficiency gains – Asana’s research with Anthropic shows marketers using AI monthly see 41% effectiveness increase, weekly users see 75%, but daily users achieve 93% improvement
- Start with manual, repetitive tasks in your existing workflow – Focus on intake, planning, execution, and reporting phases rather than trying to revolutionize everything at once
- Inspire, don’t mandate AI adoption – 38% of marketers are skeptical about AI, 19% worry about trust, and 27% fear being seen as lazy – leadership must inspire, not force
- Find your “super connectors” – These cross-functional employees who solve real business problems with AI create workflows that are 95% more likely to be adopted organization-wide
- Transform promotion criteria to include AI scaling – Add “how have you scaled yourself, your team, or organization with AI” to performance reviews and career development conversations
The Holy Grail Marketing Has Been Chasing for Decades
For years, marketers have pursued the same elusive goal: hyper-personalization at scale. We’ve had technologies that promised this capability, and we could achieve some level of personalization, but never at the massive scale needed to truly differentiate.
Shannon Duffy, CMO of Asana, believes AI finally gives marketing teams the power to achieve this holy grail – though we’re not quite there yet.
“By implementing AI into our teams and programs, we’re going to get that much closer to driving personalization at massive scale,” Duffy explained at SaaStr Annual. “But I want to be clear – we can’t necessarily do it just yet.”
The Cultural Hurdle: Why 38% of Marketers Are Skeptical About AI
Asana’s research into how knowledge workers interact with AI revealed concerning statistics about marketing teams:
- 38% of marketers are skeptical about using AI (often due to fear of producing low-quality output)
- 19% worry colleagues will trust them less if they use AI
- 27% fear being perceived as lazy by teammates and managers
This skepticism creates a dangerous cycle. Marketers already worry AI might eliminate their jobs, and now they’re concerned that using AI tools might signal they’re not fully committed to their work.
“This is really important because if people are worried about using AI, they’re already worried that AI might take their job away,” Duffy noted. “And now they’re worried that the perception that AI means they’re not fully committed to what they’re doing.”
The Three-Pillar Framework for AI Marketing Transformation
1. Start Small (Don’t Try to Revolutionize Everything at Once)
“If you go to your team and say, ‘I want you to run product launches end-to-end with AI,’ you will fail,” Duffy warned.
Instead, focus on the four key components of any marketing workflow:
- Intake: How requests are received and sorted
- Planning: Assigning deadlines, resources, and deliverables
- Execution: Creating content, briefs, and creative assets
- Reporting: Analyzing and communicating results
Each stage contains manual, repetitive tasks perfect for AI automation. Start by identifying these tasks rather than attempting wholesale transformation.
2. Inspire, Don’t Mandate
“It’s really, really hard to drive cultural transformations through fear,” Duffy emphasized. “Think of your own career. Think of times where you’ve been motivated by fear. You probably can’t think of a lot.”
Asana implemented two key strategies to inspire AI adoption:
Transformed Promotion Guidelines: Added “how have you scaled yourself, your team, or the organization with AI” to performance review criteria. This approach is inspirational rather than mandatory, giving employees authority to innovate.
Career Development Conversations: During weekly career discussions, Duffy asks, “How are you leaning into AI? Because this will make you more marketable.” She frames AI adoption as career advancement: “In two years, three years, five years, when you’re sitting across from a hiring manager, how are you telling your story of how you transformed the way you work, your department works, your company works with AI?”
3. Find and Elevate Your Super Connectors
These are employees deep in your organization who use AI to solve real business challenges specific to your company. Asana’s research identified common characteristics:
- Work cross-functionally and touch multiple teams
- Aren’t necessarily early adopters but are problem-solvers who create workflows others use
- Generate workflows 95% more likely to be adopted organization-wide
Two Super Connector Success Stories from Asana
Steph: Content Team Powerhouse
Steph works on Asana’s five-person content team, which needed to create content for three ICPs across six verticals. She created an AI editorial assistant, starting small with planning and content generation, then expanding to scheduling and delegation.
Result: Tasks that previously took weeks now take hours, enabling her to scale across multiple profiles and verticals.
Ethan: From SDR to AI Evangelist
Ethan started as an SDR handling inbound marketing leads. Without being asked, he began using AI to customize and personalize email communications to prospects. His results were so exceptional that leadership investigated what made him different.
Result: Ethan was promoted from SDR to chief AI evangelist and now works with the CRO on forecasting – revolutionizing how Asana predicts deal cycles using historical data and touch analysis.
Five Practical AI Use Cases for Marketing Teams
1. Campaign and Project Management
The Problem: Hours spent in meetings discussing campaigns, manual task tracking, constant realignment, and pressure for immediate results (“How many leads came in?”).
The AI Solution:
- Use historical campaign data (successful and failed) to generate new campaign plans
- Draft multiple content variations for personalization
- Automate timeline and deliverable management
- Provide real-time lead updates when campaigns launch
Real Example: Accor, the global hotel conglomerate with 44 brands and 7,000 employees, uses AI for multilingual campaign launches across multiple countries. Results: 50% fewer meetings and 96% increase in team efficiency.
2. Creative Intake and Production
The Problem: Time-intensive brief processes, version control challenges, and the dreaded “Was it in the brief?” conversations that creative teams know all too well.
The AI Solution:
- AI reviews briefs against historical successful/unsuccessful examples
- Flags incomplete or unclear briefs before they reach creative teams
- Automates brand guideline and tone compliance checking
- Provides real-time creative asset quality control
Asana created an AI teammate loaded with brand guidelines and tone requirements. When their CEO asked about quality control, Duffy’s previous answer was “someone looks at it and says it’s good.” Now AI automatically checks against brand standards, saving time and ensuring consistency.
Real Example: Clear Channel manages global billboard creative requests through AI triage, identifying reusable creative assets and routing requests to appropriate teams in real-time. Result: 15 hours saved per creative request.
3. Customer Insights and UX Research
The Problem: Time-consuming customer research processes – finding customers, conducting interviews, extracting insights, and hoping someone in the organization acts on the findings.
The AI Solution: Asana fed their AI teammate all customer data, calls, and notes (unstructured) to create a queryable knowledge base.
Benefits:
- Generate research plans faster
- Real-time insight queries for product teams
- No waiting for report compilation
- 83% less time analyzing interviews
This enables product teams iterating rapidly to access customer insights immediately rather than waiting for formal research cycles.
4. Product Launches
Having spent 12 years in product marketing at Salesforce, Duffy admits she “still has trauma” from product launches. The challenge: rapid changes, cross-functional dependencies, and ensuring everyone stays aligned on pricing, GA dates, and customer references.
The AI Solution:
- Build cross-functional plans using historical data
- Automatically identify blockers and dependencies
- Flag when marketing activities depend on unshipped features
Real Example: Toast revolutionized their product launches by combining past successful data with current goals. Result: 80% improvement in teamwork efficiency.
5. Marketing Operations
Marketing ops teams face the challenge of proving marketing impact using data scattered across multiple tools, especially difficult for HIPAA-compliant or regulated environments.
The AI Solution:
- Create single source of truth for all marketing data
- Enable real-time querying for insights
- Optimize A/B testing with granular, data-driven suggestions
- Predict emerging trends by analyzing past context, current information, and future goals
Real Example: Children’s Health, a Southeast-based healthcare organization requiring HIPAA compliance, created a single source of truth for marketing and patient data. Result: Days saved in duplicative work, happier teams, and literally life-or-death efficiency improvements for patient care.
The Research That Changes Everything
Asana’s Work Innovation Lab research with Anthropic revealed the exponential impact of AI frequency:
- Monthly AI users: 41% more effective
- Weekly AI users: 75% more effective
- Daily AI users: 93% more effective
“Imagine if you could inspire your teams to use AI daily, and your team is almost double as effective and impactful as they are today,” Duffy emphasized.
Looking Forward: The Transformation Ahead
AI will fundamentally disrupt marketing work. It will transform jobs and likely eliminate certain positions. However, the opportunities for professionals who lean into this transformation are exponentially greater.
“AI is going to make people more productive. It will transform small teams, individual contributors, small organizations into powerhouses. It will completely level the playing field,” Duffy concluded.
The key is starting now and driving both technological and cultural transformation within marketing teams.
4 Mistakes Shannon Duffy Made Along the Way
1. The “Janky Britney Spears” Mistake: Trying AI Without Context
Duffy’s attempt to create a Britney Spears image thanking her marketing team resulted in a bizarre, distorted output. She tried to use AI for creative content without providing sufficient context or constraints, leading to unusable results that highlighted AI’s current limitations.
2. Not Setting AI Expectations Early in Cultural Transformation
While Duffy eventually learned to “inspire, not mandate,” she initially didn’t address the cultural fears around AI adoption quickly enough. The statistics showing 38% skepticism and concerns about being perceived as lazy suggest the cultural transformation messaging should have started earlier and more proactively.
3. Assuming Super Connectors Would Be Early Adopters
Duffy initially looked for AI champions among obvious early adopters rather than problem-solvers. She learned that super connectors “aren’t necessarily early adopters” but are people who “have a problem and create workflows that other people use.” This misidentification likely delayed finding the right internal evangelists.
4. Not Transforming Performance Reviews Fast Enough
The promotion guidelines change to include “how have you scaled yourself, your team, or organization with AI” should have been implemented at the start of the AI transformation, not partway through. By waiting, Asana missed opportunities to signal AI adoption as a career advancement strategy from day one, potentially slowing organization-wide adoption.
Shannon Duffy’s presentation demonstrates that while AI transformation in marketing isn’t without challenges and mistakes, the organizations that start now – even imperfectly – will have significant competitive advantages as the technology matures.
