How AI in Sales and Marketing Cuts GTM Research Time by 60%

Go-to-market teams are feeling new competitive pressure. They're turning to AI platforms that help them work smarter and finish tasks like competitive analysis and customer segmentation faster. These platforms can cut work time by up to 60%. But it's not just about speed – it's about staying ahead of hungry competitors. With nearly 93% of GTM teams already using AI, knowing how to use these tools has become essential for boosting engagement, closing deals faster, and growing in today's B2B world.

How does AI actually reduce research time?

Moving to an AI-powered GTM research process is quite a journey. Teams might feel uncomfortable leaving familiar spreadsheets and static documents. But switching to a shared, constantly updated research hub is freeing – like cleaning out a cluttered garage so you can finally see all your tools.

This time-saving comes from smart automation, AI assistants working in the background, and flexible workflows that help everyone collaborate better. Here's what makes the difference:

• Centralized visual workflows: AI platforms give you interactive maps connecting competitor details, customer feedback, and market reports. This visual approach makes it easier to spot patterns and opportunities.
• Automated data synthesis: Instead of drowning in raw data, AI helps sort it, summarize it, and write up short reports. That intimidating pile of research turns into a well-organized summary or draft value proposition.
• Accelerated persona development: AI can quickly piece together what buyers care about, where they struggle, and how they make decisions so your teams have a clear picture of real people rather than vague ideas.

Teams benefit from AI assistants highlighting important insights and shaping strategies when needed. When it's time to share findings, AI can package everything into easy-to-read reports, speeding up decisions. This means GTM leaders can focus on strategic thinking instead of feeling stuck in spreadsheets and repetitive tasks.

What results are companies seeing in the real world?

The real-world results are impressive. Some companies have dramatically cut their research time. For example, when LinkedIn's team started using Globist AI, they cut their GTM research time by more than 60%, allowing them to beat competitors to important strategic moves.

The benefits go beyond speed. Companies have noticed that AI helps different teams like marketing, product, and sales work together more smoothly. Industry reports show that companies are seeing:

• Much higher engagement: AI-powered video messaging sometimes increases click-through rates by 8x and boosts responses four times more than standard outreach.
• Deals closing faster: About 76% of teams report closing deals more quickly after adding AI-powered tools.
• Better conversion rates: Around 44% of teams have seen improved conversion rates at key points in their sales process.

Tech giants like ZoomInfo and Seismic are also using AI to improve their research and sales approaches. The growing demand for AI skills shows how valuable and widely accepted this technology has become. AI has gone from being a novelty to a transformative tool that lets GTM teams work smarter, faster, and more collaboratively.

How can my B2B SaaS team implement AI for GTM?

For founders and GTM leaders, the pressure to "add AI" can feel overwhelming. But following trends is pointless if you're not solving real business problems. Success requires planning, focus, and clear priorities.

Start with a clear strategy

Before testing any AI tool, map out your current go-to-market process. Look honestly at problem areas: where is your process slow, repetitive, or frustrating? Using frameworks like SPICED can help you identify where AI would make the biggest difference. Focus on fixing what's broken rather than just collecting fancy tech.

Key steps for successful implementation

1. Evaluate tools on integration and features. Choose platforms with solid APIs that connect easily with your current CRM. Look for tools with lead scoring, content automation, and complete customer journey management.
2. Launch focused pilot projects. Run an experiment with one team or process. These small wins help prove value and motivate the team.
3. Prioritize data quality. AI is only as good as the data you feed it. Before scaling up, clean and standardize your customer and lead data.
4. Develop and document AI playbooks. Create step-by-step guides so everyone knows how to use AI consistently. These playbooks also make great training tools.
5. Emphasize security and ethical use. Make sure any system you use complies with regulations like CCPA and GDPR. Strong security policies build trust.
6. Foster organizational readiness. Make a clear case to leadership, highlight quick wins, and provide training so everyone feels confident with new workflows.
7. Measure, iterate, and optimize. Track important KPIs like sales cycle time and lead coverage. Adjust quickly if things aren't working.

With this practical approach, your team has a better chance of moving from scattered workflows to a streamlined, AI-powered go-to-market process.

What is the measurable ROI of using AI in GTM?

The value of AI-enabled go-to-market research shows up in clear, measurable ways: productivity increases, costs decrease, and revenues grow. Any business that tracks the right numbers can quickly see how their AI investments are paying off.

Productivity and cost savings

Generative AI tools, by automating everyday jobs like writing emails, can give back up to six hours per salesperson per week. That frees up time for more deals or complex problems. Using AI to route leads or schedule meetings means response times drop – by about 67% in some cases – and in sales, responding faster makes all the difference.

Revenue growth

Companies using AI in sales and marketing have seen real improvements: more qualified leads, higher conversion rates, and larger deals. AI-powered personalization alone has led to conversion increases of up to 78% in the best cases.

Here's a simple comparison table showing the standout ROI numbers reported across several industry surveys:

Metric Category	Key Finding	Documented Impact
Productivity	AI automates routine sales tasks	Reclaims up to 6 hours/rep/week
Win rates	AI recommends next-best actions	26% increase in win rates
Cost savings	AI-enabled operations	12% average reduction in CAC
Lead quality	AI-powered lead scoring	25% improvement in lead quality
Conversion rates	AI-driven personalization	15% average uplift in conversions
Revenue growth	AI in marketing and sales	3-15% growth in topline revenue

Create a dashboard that compares metrics before and after AI adoption. Track things like customer acquisition cost (CAC), deal size, close rates, and annual recurring revenue to see what's working.

What common challenges should we prepare for?

Even though AI helps teams make big improvements, the transition isn't always smooth. Common problems often relate to difficult integrations, varying levels of team expertise, or poor data quality. Preparing for these challenges early helps avoid disappointment later.

Integration with existing systems

Connecting new AI platforms with older systems can create "data silos," which means your insights end up scattered and less reliable.

How can we solve integration issues?

• Start with phased implementations. Add AI tools step by step to keep things manageable.
• Choose unified platforms. Look for providers with good integration track records so your company data flows easily between systems.
• Dedicate resources. Create a small team to handle integration and solve problems as they arise.

Gaps in team expertise

It's common for frontline team members to quickly learn AI basics, but the wider team might struggle with more advanced uses. This knowledge gap can become a roadblock when leaders want proof of business value while staff are still learning the basics.

How can we build team expertise?

• Focus on continuous upskilling. Build AI-powered recommendations directly into everyday tools so everyone learns as they work.
• Provide specialized training. Offer ongoing sessions covering topics like data preparation, result verification, and understanding ethics and risks with AI.

Poor data quality

Duplicate records, outdated information, and inconsistencies between databases can seriously weaken results. Some studies suggest that about 70% of AI projects stall mainly because of bad data.

How can we improve data quality?

• Establish robust data governance. Store important data in a single, reliable system and set up regular checks.
• Use automated data enrichment. Try tools that regularly clean and update your customer records.

AI-driven GTM isn't a future trend – it's already established in today's business world. The first benefit, reducing research time, is just the beginning. What's more important is how GTM teams use their newly found time: to listen more carefully to customers, make more confident decisions, and outmaneuver competitors.

Moving forward requires smart technology choices, investments in team learning, and better data. When you bring these elements together, your go-to-market approach becomes a lasting competitive advantage that drives impressive growth.

References

1. The 2024 State of AI in GTM Report - Vidyard. https://www.vidyard.com
2. Future of Revenue - Vidyard. https://www.vidyard.com
3. Vidyard Report: AI-Powered Video Now Table Stakes for GTM Teams—But Measurement Lags Behind. https://martechedge.com
4. tneogi – Medium. https://tneogi.medium.com
5. My latest startup - Globist.AI - is a one person company. | Titash Neogi. https://www.linkedin.com
6. Two months ago I started work on Globist AI - with the idea of creating a platform which makes people more curious about the world around them. | Titash Neogi. https://www.linkedin.com
7. LLM News, Updates and Articles. https://llm.extractum.io
8. LinkedIn: Log In or Sign Up. https://linkedin.com
9. LinkedIn Privacy Policy. https://linkedin.com
10. User Agreement | LinkedIn. https://linkedin.com
11. Cookie Policy | LinkedIn. https://linkedin.com
12. European Regional Hub. https://linkedin.com
13. How using Globist AI speeds up GTM strategy research by over 60% and increased stakeholder and team member alignment. | Globist AI. https://www.linkedin.com
14. Using (Off-The-Shelf) AI to Build a Scalable GTM Playbook: Lessons from MedScout. https://www.dearstage2.com
15. Top 10 AI-Powered Tools to Supercharge Your GTM Strategy: A Comparison of Top Players and Emerging Innovators - SuperAGI. https://superagi.com
16. The best enterprise AI tools to make GTM teams more efficient in 2025 | Bigtincan. https://www.bigtincan.com
17. BlazeSQL GTM Case Study | 3x Brand Visibility in 90 Days. https://stratigia.com
18. The AI-Powered GTM Stack: A Deep Dive into Real-World Case Studies of Companies that Succeeded (and Failed) with AI-Driven Sales and Marketing - SuperAGI. https://superagi.com
19. How OpenAI is scaling their GTM motion with Clay | Clay.com. https://www.clay.com
20. How Startups are Optimizing GTM Strategy With AI. https://www.hubspot.com
21. GTM's AI Toolbox: The FETC Framework - Clay University. https://www.clay.com
22. Case Studies in Secure AI GTM: Success Stories and Lessons Learned from Industry Leaders in 2025 - SuperAGI. https://superagi.com
23. United States Artificial Intelligence Institute| AI Certification| USAII®. https://www.usaii.org
24. OWASP AI Maturity Assessment | OWASP Foundation. https://owasp.org
25. How do we get to a possible future with AI? Meet the AI Adoption Maturity Curve. https://www.zdnet.com
26. AI Has Killed the Traditional SaaS GTM playbook | Felicis. https://www.felicis.com
27. The AI for Go-to-Market Playbook. https://www.gotomarketalliance.com
28. GTM Playbook: How to Become AI-Native as a Founder or Revenue leader in 2025. https://koenstam.substack.com
29. Unlock SaaS AI Growth: The Ultimate GTM Playbook. https://stewarttownsend.com
30. Building Your AI GTM Stack. https://knowyourgrowth.substack.com
31. The no BS blog. https://www.purplepath.io
32. Roi Calculator | Momentum. https://www.momentum.io
33. The AI ROI Calculator. https://www.sectionai.com
34. What if you could calculate the *exact* ROI of your next AI project—before you even launch it? | Jonathan M K.. https://www.linkedin.com
35. The ROI of AI in Sales: Real Impact Data and Analysis | Gong Labs. https://www.gong.io
36. From Theory to Practice: Real-World Case Studies of AI-Driven Go-to-Market Strategy Success - SuperAGI. https://superagi.com
37. Case Studies: How Leading Companies Are Leveraging AI to Enhance Their GTM Strategies in 2025 - SuperAGI. https://superagi.com
38. A16Z: The Median Enterprise AI Startup now Hits $2.1M ARR by Month 12 | SaaStr. https://www.saastr.com
39. AI Survey: Marketers are Leading the Way in GTM AI Adoption. https://pipeline.zoominfo.com
40. AI in GTM: Opportunities, challenges, and best practices. https://www.gotomarketalliance.com
41. Is Your GTM Team Ready For AI? | Leadspace. https://www.leadspace.com
42. AI vs. Traditional GTM Tools: A Comparative Analysis of Efficiency and ROI in 2025 - SuperAGI. https://superagi.com
43. Measuring the ROI of AI in Marketing: Key Metrics and Strategies for Marketers. https://blog.hurree.co
44. AI's Impact on GTM Strategies by 2025 | Copy.ai. https://www.copy.ai
45. Customer Case Studies | Gong. https://www.gong.io
46. Focusing on quality over quantity helps Databricks execute at scale. https://www.outreach.io
47. Outreach AI Review: Everything You Need to Know. https://www.spekit.com
48. How Outreach Achieved Best-in-Class Training Data Quality and ML Performance | HumanSignal. https://humansignal.com