- Clear executive sponsorship and vision
- Active builder community engagement
- Regular feedback collection and iteration
- Measurable success metrics and use cases ROI tracking
- Continuous learning and improvement
Success planning with Dust
Crafting your Dust vision
Leadership plays a pivotal role in shaping your organization’s AI journey. Here’s how to build a vision that resonates:Align with business strategy
Connect Dust’s capabilities and agents/use cases to your organization’s strategic priorities: Questions to consider:- Which workflows and teams would benefit most from AI augmentation?
- How can we use our existing tools and data more effectively?
- How can Dust help us achieve our business goals?
Help teams build their own solutions
Position Dust as a platform that enables teams to create their own AI solutions: Questions to consider:- How can we identify and support potential builders across departments?
- What resources do builders need to succeed?
- How can we facilitate knowledge sharing and best practices?
- How rewarding people investing time to drive efficiency with Dust?
Explore new opportunities through custom integrations
Emphasize Dust’s ability to integrate deeply with existing tools and workflows: Questions to consider:- Which integrations will deliver the most immediate value?
- How can we use the API and Dust Apps for custom solutions?
- What automation opportunities exist across our tech stack?
Ensure educated AI use
Establish clear company objectives and growth expectations for Dust use while maintaining security and compliance: Questions to consider:- How much time do we want to save on this workflow? How to drive efficiency on identified workflows?
- What data needs to be shared, restricted? What is our Dust Data Access Policy.
Build your Dust team
Measuring value
We recommend evaluating the impact of Dust throughout its deployment:Best practices
- Measure impact and team satisfaction: Assess Dust value at both the organizational, use case levels and individual level (would you be sad if you couldn’t use Dust anymore).
- Align with company’s goals: Ensure every use case tested is linked to business objectives and uses metrics that stakeholders care about.
- Use 80/20 estimates: Start with rough estimates focused on key impacts to build momentum quickly.
- Communicate regularly: Share learnings with leadership, set clear assumptions and share progress as more data becomes available.
- Iterate and improve: Treat ROI estimation as a continuous process, making adjustments over time.
Example
Scenario- Giving your Tier 2 & 3 customer support team members agents connected to your company’s knowledge base and historical support data, with direct context of the ticket at hand.
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Support tickets per month: 2000
- Current average resolution time: 30 min
- Support agent cost: €40/hour
- Support resolution costs: 2000 tickets x €40 x .5 hours = €40,000 per month
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Customer churn rate due to support dissatisfaction: 5% of 1000 customers/year
- Average customer lifetime value: $50,000
- Annual churn cost: 5% x 1000 customers x $50,000= €2.5 million per year
- Decreased resolution time: Able to respond to tickets twice as fast (15min)
- Reduced support cost: 2000 tickets x €40 x .25 hours = €20,000 per month
- Annual Savings = €20,000 * 12 = €240,000
- Increased customer satisfaction: Better answers reduces churn rate to 3%
- Reduced churn cost: 3% x 1000 customers x $50,000= €1.5 million per year
- Increased savings: Potentially save €1.7 million per through better support
- Employee satisfaction: Support team members deal with happier customers so are less likely to churn
- Quicker onboarding: Dust agents help decrease ramp-up time through access to all existing knowledge and policies