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WE GET YOU. WE GOT YOU.
Introducing the 6dimensional Success Model™ – a methodology that empowers customers to unleash their full revenue potential and and achieve extraordinary outcomes.
Chapters
Chapter 1
Introduction
Chapter 2
Understanding the Basics of AI Marketing and Sales Tools
Chapter 3
Common Misconceptions and Overpromises in AI Marketing
Chapter 4
Evaluating AI Solutions: Key Criteria to Consider
Chapter 5
How to Assess the Learning Curve and the Level of Support Offered by Vendors
Chapter 6
Conclusion
Table of Contents
Chapter 1
Introduction
The allure of AI is everywhere, promising to streamline workflows, drive unprecedented growth, and revolutionize how your team operates. But as enticing as these promises are, it’s essential to approach them with a healthy dose of skepticism.
We get why you’re skeptical: The pressure to adopt AI tools can be overwhelming. Vendors often tout their solutions as the next big thing, claiming they can solve all your business challenges with the click of a button. However, the reality is far more complex.
This guide is designed for decision-makers who:
- Are considering investments in AI
- Understand the potential of AI, but also
- Recognize the importance of making informed decisions based on real-world results, not overblown promises and predictions
Let’s dive in and separate the hype from reality, helping you navigate the complex, crowded AI landscape confidently and make the best choices for your team’s success.
Chapter 2
Understanding the Basics of AI Marketing and Sales Tools
Marketing and sales tools described as “AI-powered” can perform tasks that typically require human intelligence. Common capabilities include:
Data Analysis: AI-driven tools can sift through enormous datasets to uncover insights that would take humans much longer to find. This includes identifying high-value accounts, understanding customer behavior, and optimizing marketing campaigns in real time.
Personalization: AI can tailor marketing messages to individual prospects based on their past interactions, preferences, and needs (derived from intent signals). This level of personalization helps build stronger relationships and drives higher conversion rates.
Lead Scoring: AI algorithms can assess the likelihood of an account converting into a customer by analyzing factors such as engagement level, company size, and industry. This helps sales teams prioritize their efforts on accounts that are most likely to close.
Predictive Analytics: By analyzing historical data, AI can forecast future trends, customer behaviors, and market conditions. This enables B2B companies to make more informed decisions about where to allocate resources and how to adjust their strategies.
In other words, AI-powered tools have a lot to offer revenue teams in terms of efficiency and scalability.
However, it would be irresponsible to not address the hype surrounding AI.
Chapter 3
Common Misconceptions and Overpromises in AI Marketing
Understanding the true capabilities of AI and separating the facts from the hype is crucial for making informed buying decisions. By recognizing both the potential and the limitations of AI tools, B2B companies can use these technologies effectively without falling for overblown promises — or their own misconceptions.
While you’re shopping for AI-driven tools, be on the lookout for the following overpromises from vendors, and keep the following commonly held misconceptions in check:
Misconception: AI Can Replace Human Jobs Entirely
The Truth: While AI can automate many tasks, it’s not a replacement for human intelligence and creativity. AI tools are best used to augment human capabilities, taking over repetitive tasks and providing insights that humans can use to make better decisions.
Overpromise: AI Will Instantly Solve All Your Business Problems
The Truth: AI is not a magic bullet. Implementing AI effectively requires careful planning, quality data input, and ongoing management. Businesses that expect immediate results without proper setup and understanding of AI’s limitations will be disappointed.
Misconception: AI Understands Context
The Truth: AI can process and analyze data, but it doesn’t grasp context in the way humans do. For example, an AI tool might identify a trend in customer behavior, but it won’t understand the context behind that behavior, be it cultural, seasonal, economical, etc. AI recognizes patterns, but not meaning. Human oversight is crucial to interpret AI-generated insights correctly and apply them effectively.
Misconception: All AI Tools Are the Same
The Truth: Not all AI tools are created equal. Many are built on similar technologies (we’ll go over this later), but their effectiveness can vary significantly based on the quality of the data they use, the algorithms they employ, and how well they integrate with existing systems. It’s important to evaluate each tool on its specific merits and how well it fits your organization’s needs.
Misconception: AI Can Predict the Future with Certainty
The Truth: While AI can make predictions based on historical data, it is not a crystal ball. Predictive analytics can provide valuable insights, but it’s important to remember that these predictions are based on probabilities and assumptions. External factors, such as market changes or unforeseen events, can impact the accuracy of AI predictions.
Overpromise: AI Implementation Is Quick and Easy
The Truth: Implementing AI solutions the right way takes time, effort, and resources. It requires a solid foundation of clean, structured data, the right expertise to set up and maintain the systems, and thorough onboarding to get started. Businesses should be prepared for a learning curve and ongoing adjustments to fully realize the benefits of AI.
Chapter 4
Evaluating AI Solutions: Key Criteria to Consider
As mentioned above, many AI tools can appear remarkably similar at first glance, and this isn’t just a coincidence: AI solutions are mostly all built on the same foundational technologies, and may even use the same data sets.
However, while these tools share many similarities, the way they are applied can vary greatly. Understanding these differences is key to making an informed choice.
Why Many AI Tools Look the Same
Common Foundations: Many AI tools leverage widely available machine learning or natural language learning models and algorithms, such as those from OpenAI or Anthropic. These models, often open-source or licensed, provide a foundation upon which companies build their solutions. As a result, multiple tools might offer similar functionalities, such as predictive analytics or automated content generation.
Shared Data Sources: AI tools that rely on common data sources, like publicly available datasets or standard industry data, can produce outputs that are very similar. This can make it challenging to distinguish one tool from another based solely on surface-level features.
Digging Deeper: Identifying Truly Unique Solutions
Proprietary Algorithms: Some AI solutions distinguish themselves through proprietary algorithms that have been fine-tuned for specific applications or industries. These can offer more accurate or faster results compared to generic models.
Customizable Logic: Customization options that allow users to tailor algorithms to their specific needs, such as adjusting predictive models based on proprietary business data, can provide a significant competitive advantage.
Advanced Integrations: Tools that offer seamless integration with a wide range of platforms and systems, especially those that are specific to your industry, can enhance the overall effectiveness of the AI tool and provide more comprehensive solutions.
Scalability and Integration
Once you’ve weeded out generic AI solutions and narrowed down your options, you can start shaping your buying decision with more granular factors, like scalability and integration.
As you go check out potential vendors, consider:
Data Compatibility: Can the AI tool easily integrate with your existing data systems? If your company uses a specific CRM or marketing automation platform, it’s important for the AI tool can connect with these systems without requiring extensive reconfiguration.
Workflow Continuity: Will the solution enhance, not disrupt, your current workflows? A tool that integrates well will allow you to automate processes seamlessly, without requiring a complete overhaul of how your team operates.
Real-Time Syncing: Can the AI tool sync with your data sources as frequently as needed to keep insights current and actionable? For AI to be most effective, it should work in real-time or near real-time with your existing systems.
Handling Increased Data Volume: Can the AI solution handle larger datasets without a drop in performance? As your business expands, so will the amount of data you generate. Scalable AI solutions can grow with your company, ensuring that they remain effective even as your operations scale up.
Adapting to New Requirements: Will the solution offer flexibility to adapt to new data sources, additional users, or more complex analyses as your business evolves? Your business needs today might not be the same as they will be in the future.
Cost Considerations: Can you uncover any hidden costs associated with scaling, like additional licensing fees or the need for more advanced hardware? Ensure that the cost of scaling the AI solution is sustainable.
Usability and User Experience
Ease of use is just as important as technical capabilities. A powerful tool that is difficult to use will likely lead to underutilization and frustration among team members.
The more intuitive the AI tool is, the easier it will be for all team members to adopt it. This is particularly important in larger organizations where diverse teams might need to use the tool, each with varying levels of technical expertise.
Chapter 5
How to Assess the Learning Curve and the Level of Support Offered by Vendors
User Interface (UI) and Experience (UX): Consider the design and layout of the tool. Is it intuitive and logically organized? Does it allow users to perform tasks efficiently without needing to dig through menus or seek help frequently?
Training and Onboarding: Evaluate the vendor’s training resources and onboarding support. This includes access to tutorials, documentation, and customer support during the initial setup phase.
Ongoing Support and Community: Post-purchase support is critical. Check if the vendor offers ongoing customer support, including troubleshooting and updates. Also look for tools with active user communities or forums (i.e. 6sense’s RevCity) that can provide valuable insights and tips from other users facing similar challenges.
Chapter 6
Conclusion
AI solutions are constantly evolving, and new tools are emerging almost daily. It’s easy to get swept up in the excitement and rush to adopt the latest technology. However, if you’re a skeptic, you’ve got the right mindset: Taking a thorough approach to selecting AI tools is not just prudent — it’s essential for ensuring that your investments yield real, sustainable benefits.
Evaluating tools based on these key criteria — differentiation, scalability and integration, and usability and user experience — helps navigate the crowded AI marketplace and select a solution that sets your organization up for success in the long run.
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The 6sense Team
