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How to do keyword research for AEO (+ Tools)

William Gu
William Gu
Marketing AI Team @ MKTGAGENT.AI

Mastering Keyword Research for Answer Engine Optimization (AEO): A Modern Guide with Top Tools

Introduction

The landscape of keyword research is evolving fast with the rise of Answer Engine Optimization (AEO), a strategic approach that goes beyond traditional SEO. Unlike standard search engine optimization, which focuses on ranking web pages for keywords, AEO revolves around how answer engines like ChatGPT understand and respond to user queries. This shift means that content must be optimized not only to rank but also to be cited as a trustworthy source in conversational AI responses. This article provides a clear guide to understanding AEO keyword research and highlights the essential tools and techniques for success.

Understanding the Difference Between SEO and AEO Keyword Research

Traditional SEO keyword research emphasizes identifying popular search terms to drive traffic and improve rankings. AEO, however, requires insight into conversation-driven queries and the intent behind them, as answer engines curate responses contextually rather than just listing links. This demands a nuanced approach where the focus is on questions users ask and the kind of comprehensive, authoritative answers that AI systems prefer to cite.

Tools Tailored for AEO Keyword Discovery

Achieving effective AEO keyword research involves using a mix of platforms designed to capture different facets of user intent:

  • Traditional Keyword Tools: Platforms like Semrush and Ahrefs still play a role by providing foundational keyword data and competitive analysis.
  • Question Mapping Tools: AlsoAsked and AnswerThePublic help uncover the conversational questions users ask, revealing the natural language queries AI answer engines are likely to encounter.
  • Fanout Query Tools: Otterly.ai and similar technologies expand the range of queries by generating conversation branches, helping to identify diverse user intents and related questions.

A Systematic Approach to AEO Keyword Research

To transition smoothly from traditional keyword research to AEO, practitioners should:

  1. Identify core seed topics relevant to their niche.
  2. Expand these topics into detailed question lists using question mapping tools.
  3. Validate and refine these questions through AI systems to align with how answer engines interpret queries.
  4. Develop structured content briefs that cater specifically to providing clear, concise, and authoritative answers.

Key Insights

  • What makes AEO different from traditional SEO? AEO prioritizes being cited as an authoritative answer by AI systems rather than only ranking for keywords.
  • Which tools are most effective for AEO? Semrush and Ahrefs offer foundational keyword data, while AlsoAsked, AnswerThePublic, and Otterly.ai specialize in conversation-focused query discovery.
  • How can content creators adapt? By focusing on question-based content and validating queries with AI tools, content becomes more relevant for answer engines.

Conclusion

Answer Engine Optimization represents a significant shift in keyword research strategy, emphasizing trustworthiness and conversational relevance. By leveraging specialized tools and adopting a question-driven approach, marketers and content creators can enhance their visibility within AI-powered answer engines. As AI continues to evolve, embracing AEO techniques will be crucial for maintaining competitive online presence.

Source: https://blog.hubspot.com/marketing/keyword-research-tools-for-aeo

How we Build with AI

William Gu
William Gu
Marketing AI Team @ MKTGAGENT.AI

How HubSpot Builds with AI: A Three-Phase Journey to Innovation and Efficiency

In recent years, artificial intelligence (AI) has transformed how companies drive engineering productivity and enhance customer experiences. HubSpot’s approach to AI adoption represents a carefully planned evolution, showcasing how emerging technologies can accelerate product development and create seamless user experiences. This article explores HubSpot’s AI journey across three distinct phases and what it means for the future of business innovation.

Phase 1: Empowering Engineers with Coding Copilots (2023-2024)

HubSpot’s initial AI integration focused on augmenting engineers’ capabilities through coding copilots. These AI assistants helped developers write code more efficiently by providing suggestions and automating routine tasks. The adoption rate among engineers reached an impressive 80%, resulting in a noticeable boost in engineering velocity. This phase demonstrated that AI can effectively supplement human skills to speed up development without sacrificing quality.

Phase 2: Enhancing Efficiency with Autonomous Coding Agents (2024-mid 2025)

Building on the success of coding copilots, HubSpot moved towards deploying autonomous coding agents in the second phase. These agents operated with greater independence, managing more complex coding tasks and reducing manual intervention. With 96% engineer adoption during this period, coding efficiency saw a further leap forward. Introducing autonomy in AI tools enabled HubSpot to streamline workflows and reduce bottlenecks in development cycles.

Phase 3: Creating a Unified AI Platform for Innovation (Mid 2025-Present)

The current phase centers on cohesion and scalability. HubSpot unified all AI agents under a single, shared infrastructure, enabling agents to work interoperably across various products and teams. This shared platform increases flexibility and encourages rapid innovation, because new AI capabilities can be deployed quickly and seamlessly integrated. The result is faster product delivery and a more consistent customer experience.

Key Insights

  • Why was phased AI adoption important? Phased deployment allowed HubSpot to progressively integrate AI, increasing adoption and minimizing disruption while maximizing productivity gains.
  • What benefits came from autonomous coding agents? They significantly improved coding efficiency by automating more complex tasks and alleviating engineers’ workloads.
  • How does a unified platform impact AI innovation? It fosters interoperability, scalability, and faster rollout of AI capabilities across multiple products.

Conclusion

HubSpot’s AI strategy demonstrates the power of structured innovation in technology adoption. By evolving from assistive coding copilots to autonomous agents on a unified platform, HubSpot has enhanced engineering productivity and customer satisfaction. Companies looking to leverage AI for business growth can learn from this approach, emphasizing phased integration, adoption focus, and infrastructure that supports continuous innovation.

Source: https://blog.hubspot.com/marketing/how-we-build-with-ai

How we Grow with Agent-first GTM

William Gu
William Gu
Marketing AI Team @ MKTGAGENT.AI

Growing Smarter with HubSpot’s Agent-first Go-to-Market Strategy

In today’s competitive business landscape, companies are seeking innovative ways to enhance customer acquisition, engagement, and retention. HubSpot has taken a groundbreaking approach by adopting an Agent-first go-to-market (GTM) strategy powered by artificial intelligence (AI), reshaping how they operate and connect with customers.

What is the Agent-first GTM Strategy?

HubSpot’s Agent-first GTM strategy centers around AI-driven agents designed to optimize each stage of the customer journey. Over three years, HubSpot developed several specialized agents that automate and enhance critical sales, marketing, and support functions. These AI agents are not replacements but powerful assistants that improve efficiency and personalization.

Key AI Agents Driving Growth

  • Demand Agent: Identifies potential Ideal Customer Profiles (ICPs) to focus marketing and outreach efforts efficiently.
  • Inbound Agent: Automates responses to incoming customer inquiries, accelerating engagement.
  • AEO Agent: Enhances visibility by optimizing content for AI-generated search results, increasing lead quality.
  • Prospecting Agent & Guided Sales Assistant: Streamline sales workflows and improve win rates by providing timely, context-specific support.
  • Customer Agent: Resolves around 60% of customer support queries autonomously, freeing human agents for more complex issues.
  • Customer Success Assistant: Facilitates personalized outreach, resulting in higher customer retention and satisfaction.

Impact on HubSpot’s Business

These AI-powered agents have led to significant improvements in key business metrics, including a notable increase in qualified leads and booked meetings. The streamlined sales process and enhanced support capabilities have further elevated customer experience and loyalty.

Key Insights

  • How does AI enhance customer acquisition at HubSpot? The Demand Agent leverages data to identify ideal customers, making targeting more effective and efficient.

  • In what ways has automation improved sales and support? Agents like the Inbound Agent and Customer Agent handle routine inquiries, reducing response times and increasing scalability.

  • What are the broader benefits of this strategy? The Agent-first approach enables HubSpot to deliver personalized, timely interactions that boost conversion rates and customer retention.

Conclusion

HubSpot’s Agent-first GTM strategy exemplifies how AI can transform B2B sales and support by delivering smarter, faster, and more personalized customer interactions. Businesses looking to grow sustainably should consider integrating AI-driven agents into their go-to-market strategies to innovate and remain competitive in an increasingly digital world.

Source: https://blog.hubspot.com/marketing/how-we-grow-with-agent-first-gtm

How we Operate as an AI-first Company

William Gu
William Gu
Marketing AI Team @ MKTGAGENT.AI

How HubSpot Operates as an AI-First Company: Transforming Culture and Productivity Through AI

HubSpot’s commitment to becoming an AI-first company is more than just adopting new technology—it embodies a strategic transformation at every level of the organization. Their journey highlights how embedding AI fluency, enabling team productivity, and redesigning institutional processes can create a dynamic environment that leverages artificial intelligence effectively.

Building AI Fluency Across the Workforce

The first phase of HubSpot’s transformation aimed to increase AI fluency among employees. The company focused on equipping everyone with access to necessary AI tools and cultivating a culture that encourages experimentation with these technologies. The results are impressive: 94% of employees now use AI weekly, and more than 3,900 AI agents have been created internally. This widespread AI adoption sets a foundation for future innovations and operational efficiency.

Driving Productivity Through Team-Level Transformation

Recognizing that teams vary in their AI readiness, HubSpot introduced a framework to categorize teams based on their maturity with AI tools. This approach allowed the company to tailor strategies that enhance productivity where it’s most impactful. Marketing and recruitment teams, for example, have experienced considerable efficiency gains. This targeted adoption not only improves output but also demonstrates AI’s tangible value in day-to-day business functions.

Institutional Transformation: Redesigning Processes for AI Integration

The final, and perhaps most ambitious, stage focuses on embedding AI deeply into HubSpot’s institutional processes. This means redesigning workflows to fully leverage new AI capabilities, ensuring every employee has easy access to the right tools and information precisely when needed. The goal is to create a self-sustaining AI ecosystem that continuously enhances productivity at an organizational level.

Key Insights

  • Why is building AI fluency important? It lays the groundwork for broader adoption and innovation by ensuring employees are comfortable and capable with AI.
  • How does team-level AI maturity drive productivity? Tailored strategies allow teams to maximize AI benefits based on their specific needs and readiness.
  • What does institutional AI transformation involve? It requires rethinking company-wide processes to integrate AI tools seamlessly and sustainably.

Conclusion

HubSpot’s AI-first journey illustrates the multi-layered approach necessary to make AI a core part of business operations. From empowering individuals to transforming teams and institutional frameworks, the company is setting a precedent for leveraging AI not just as a tool but as an integral element of its corporate culture and workflow. As other organizations pursue similar paths, HubSpot’s model offers valuable lessons on embracing AI for sustainable productivity gains.

Source: https://blog.hubspot.com/marketing/how-we-operate-as-an-ai-first-company

In The Agentic Era of AI, Appian Emerges as a Vital Partner to Help Businesses Harness its Capabilities

William Gu
William Gu
Marketing AI Team @ MKTGAGENT.AI

The landscape of artificial intelligence (AI) is evolving rapidly, ushering in what experts call the “agentic era.” This new phase of AI development is marked by systems capable of higher degrees of autonomy, requiring businesses to rethink how they integrate AI to generate meaningful and measurable outcomes. Appian has positioned itself at the forefront of this transformation, emerging as an essential partner for enterprises striving to unlock the full potential of AI within their operations.

Understanding the Agentic Era

The agentic era of AI refers to a shift where AI technologies operate with a greater degree of independence and decision-making ability. Rather than AI functioning as a mere tool or supplement, it becomes an active agent embedded directly into business workflows. This transition demands robust integration that connects AI actions transparently to core business processes, ensuring results can be measured and verified.

The Challenge of AI Adoption in Business

Many organizations invest heavily in AI pilots and projects, yet a significant number report failing to achieve tangible financial returns. Industry analyses show that nearly 75% of AI initiatives do not deliver the expected economic benefits, leaving businesses skeptical about scaling these technologies. This gap often stems from AI solutions being developed in silos or disconnected from the practical realities of enterprise processes.

How Appian Addresses These Challenges

Appian’s approach centers on embedding AI capabilities within secure, end-to-end workflows that maintain transparency and traceability. By integrating AI directly into operational processes, Appian enables businesses to not only deploy AI at scale but also to ensure those deployments drive measurable success. A landmark case study with TELUS highlights the practical benefits of this method—Appian’s solutions helped save thousands of employee hours and significantly boosted operational efficiency.

Using advanced process intelligence, Appian connects AI-driven insights to meaningful work, enhancing decision-making and workflow automation. This seamless integration ensures AI is not just a theoretical advantage but a tangible asset that leads to genuine improvements in productivity.

Key Insights

  • Why is the agentic era pivotal for business AI adoption? Because autonomous AI demands integration within processes to realize measurable impact.
  • What makes Appian’s approach successful? The blend of secure, transparent workflows with embedded AI ensures real business value.
  • How does process intelligence improve AI effectiveness? It connects AI insights directly to everyday work, enhancing efficiency and outcomes.
  • What lessons does the TELUS collaboration teach? Practical deployment and integration of AI can save substantial resources and improve operations.

Conclusion

As AI advances into the agentic era, the pressure on businesses to adapt and optimize their AI strategies intensifies. Appian’s role as a vital partner highlights the necessity of integrating AI within comprehensive workflows to realize the technology’s true potential. Organizations aiming to thrive must prioritize transparent, process-connected AI solutions that deliver measurable benefits, enhancing both operational efficiency and strategic decision-making in a competitive marketplace.

Source: https://martechseries.com/predictive-ai/ai-platforms-machine-learning/in-the-agentic-era-of-ai-appian-emerges-as-a-vital-partner-to-help-businesses-harness-its-capabilities/