How to Pitch Autonomous Testing to Your Executive Team (and get nods)
Secure your budget for AI testing – turn technical capabilities into hard economic metrics
The promise of autonomous testing is alluring: breaking past the traditional automation plateau and letting AI eliminate maintenance bottlenecks. But as the Forrester Buyer’s Guide: Autonomous Testing Platforms, Q1 2026 highlights, moving from a successful proof-of-concept to adoption requires more than technical validation—it requires a bulletproof business case.
Grab your coffee and join Applitools for a fast-paced, 20-minute strategy session. We’ll skip the marketing hype and dive straight into the economic pointers needed to translate autonomous capabilities into hard business value.
We will map out how to build an autonomous testing business case, including:
- Quantifying the “Automation Ceiling”: How to use Forrester’s data to expose the hidden cost of the manual maintenance tax on your product velocity.
- Calculating Multi-Dimensional ROI: Why focusing solely on authoring speed misses the mark, and how to quantify the value of visual validation and cross-environment flexibility.
- De-risking the AI Investment: How to leverage Forrester’s criteria on vendor trust and AI maturity to ease security and procurement anxieties.
Walk away with practical tips to align your leadership team and secure the budget for your next AI testing investment.
The promise of autonomous testing is alluring: breaking past the traditional automation plateau and letting AI eliminate maintenance bottlenecks. But as the Forrester Buyer’s Guide: Autonomous Testing Platforms, Q1 2026 highlights, moving from a successful proof-of-concept to adoption requires more than technical validation—it requires a bulletproof business case.
Grab your coffee and join Applitools for a fast-paced, 20-minute strategy session. We skipped the marketing hype and dove straight into the economic pointers needed to translate autonomous capabilities into hard business value.
We mapped out how to build an autonomous testing business case, including:
- Quantifying the “Automation Ceiling”: How to use Forrester’s data to expose the hidden cost of the manual maintenance tax on your product velocity.
- Calculating Multi-Dimensional ROI: Why focusing solely on authoring speed misses the mark, and how to quantify the value of visual validation and cross-environment flexibility.
- De-risking the AI Investment: How to leverage Forrester’s criteria on vendor trust and AI maturity to ease security and procurement anxieties.
Walk away with practical tips to align your leadership team and secure the budget for your next AI testing investment.
Applitools is the world’s most intelligent test automation platform. See it in action with a personalized demo.
Expert Speakers
CMO, Applitools
With over 20 years of experience, Joanna Schloss is a CMO whose engineering background and deep expertise in AI and machine learning inform her impactful marketing strategies. She uniquely bridges the gap between data, technology, and market insight, leveraging AI to fuel innovation, extract valuable customer intelligence, and drive data-driven optimization for maximum ROI.
Sr. Product Marketing Manager, Applitools
Heather Vercillo is a senior member of the Product Team at Applitools, an AI-driven end-to-end testing platform. With a career spanning testing across industries, she blends strategic marketing with technical storytelling to communicate product value. A frequent speaker at industry events—including Gartner Application Innovation Summit, Ministry of Testing, and Automation Guild—Heather also serves on the board of the Contemporary Youth Orchestra and supports her daughter’s passion for writing.
Frequently Asked Questions
Focus on economic value instead of technical features. Expose the hidden cost of the manual “maintenance tax.” Prove how this investment turns a variable bottleneck into a fixed, negligible cost.
Look beyond script-authoring speed. Measure multi-dimensional value like cross-environment flexibility and the cost of bugs escaping to production.
To secure buy-in, proactively address executive anxieties regarding data security and “black box” AI hallucinations. Guide leadership toward a trusted, enterprise-grade AI engine.