7 Reasons You Need A Generative AI Advisory Board for Marketing in 2024

Hey there! If you want to succeed with generative AI for marketing, I’ve got some valuable insights for you. The advent of Generative AI tools has genuinely revolutionized the marketing landscape, offering incredible opportunities for innovation and efficiency. But to make the most of this game-changing technology, taking a comprehensive and structured approach to its integration within your organization is essential.

One key recommendation is establishing a cross-functional Generative AI advisory board within your marketing department. This diverse group brings together experts from various domains, such as marketing strategists and data scientists, to have meaningful discussions about these tools’ feasibility and potential applications. By thoroughly scrutinizing the implications of adopting AI, you can mitigate risks and make informed decisions.

What is a Generative AI Advisory Board

A GenAI advisory board helps you keep pace with the dynamic AI landscape and evolving capabilities of generative AI technologies. It also fosters a culture of continuous learning and adaptation. This proactive approach ensures your organization stays at the forefront of technological advancements, giving you a competitive edge and driving enhanced marketing outcomes and overall business performance.

These insights will help you succeed in harnessing the power of generative AI for your marketing goals!

Reason 1: Defining and prioritizing generative AI use cases for marketing

As you embark on your journey with generative AI tools in your marketing department, prioritizing the best use cases for evaluation is crucial. Start by identifying the key challenges and objectives your marketing team currently faces. Whether it’s content generation, personalized customer engagement, campaign optimization, or loyalty programs, these pain points will guide where generative AI can add the most value.

Next, consider the feasibility of these use cases. Do they align with your organization’s technical capabilities and resources? Engage your GenAI advisory board and key stakeholders to assess each use case’s complexity and potential return on investment.

Lastly, always keep the customer at the forefront. The primary goal of your marketing efforts is to enhance customer experience and engagement. Prioritize use cases that allow you to personalize your marketing efforts, delivering content and messages that resonate with your target audience, thereby driving conversions and customer loyalty. 

Remember, the journey to deploying generative AI is iterative and dynamic. As your organization grows and evolves, so too should your use cases and objectives. Regularly revisiting and reassessing your priorities ensures that your AI efforts are constantly aligned with your marketing goals.

Reason 2: Documenting Marketing Use Cases for Generative AI

When it comes to documenting marketing use cases for generative AI, it’s essential to consider the following components:

1. Problem statement: Identify and articulate the marketing challenge the generative AI tool intends to solve. This could be anything from creating personalized email campaigns and generating engaging social media content to predicting consumer behavior.
2. Marketing objectives: Detail the marketing objectives that align with the use of generative AI. These might include increasing conversion rates, boosting customer engagement, or enhancing brand awareness.
3. Input data: Describe the type and source of data required as input for the AI. This might include customer profile data, purchase history, web browsing behavior, or social media engagement metrics.
4. AI function: Explain what specific tasks the AI will perform. This could include segmenting customers, generating content, or predicting trends.
5. Desired output and delivery: Specify the desired output from the AI and how these results should be presented. This might involve numerical predictions, visualizations, or written reports. 
6. Performance measurement: Outline how the success of the AI’s performance will be measured. This could include key performance indicators (KPIs) such as conversion rate, click-through rate, or customer retention rate.

Remember, the main aim is to answer the marketing person’s fundamental questions – ‘What problem can AI solve for me?’, ‘What data do I need?’, and ‘How will I know if it’s working?’. This detailed documentation will ensure a clear understanding of how generative AI can enhance marketing strategies.

With a comprehensive understanding of your marketing objectives and clear definitions of success, you are ready to delve into the world of software vendors. Various generative AI platforms and software are available, each with unique capabilities and features. Your advisory board, equipped with the knowledge of your specific use cases, will need to critically evaluate these offerings. You will consider factors such as ease of integration, customer support, scalability, and, most importantly, the AI’s ability to meet your specific marketing objectives. Let’s explore this exciting phase of your Generative AI journey.

Reason 3: Analyzing available technologies and vendor software

GenAI advisory boards should undertake generative AI software evaluations, pilots, and deployments systematically and robustly. The process should start with a comprehensive software evaluation, assessing its ethical implications, accuracy, potential biases, and overall alignment with the organization’s values and goals. This evaluation should be performed by a diverse team representing multiple perspectives to minimize blindspots and biases.

For pilot projects, the advisory board should establish clear goals and metrics for success. It should also ensure rigorous monitoring and data collection during the pilot phase to assess the software’s performance and potential impacts on stakeholders. Any identified issues or challenges should be addressed promptly and transparently, with learning incorporated into future projects.

Regarding deployments, the GenAI Advisory Board should conduct a final review of the software, checking its compliance with all legal and ethical standards. It should also ensure the establishment of a robust feedback loop to continuously monitor the software’s impact post-deployment. Furthermore, the advisory board should commit to transparency, communicating openly with stakeholders about the deployment process, any associated risks, and the measures to mitigate those risks. By following these steps, a GenAI Advisory Board can ensure that generative AI software evaluations, pilots, and deployments are performed ethically and responsibly. 

In evaluating generative AI software solutions, the GenAI Advisory Board should engage in two key procurement processes: Request for Information (RFI) and Request for Quotation (RFQ). An RFI is often the initial step, providing the advisory board with a broad understanding of the available solutions in the market. It is a formal, structured process for gathering information about the capabilities of various suppliers and understanding their product offerings and solutions.

On the other hand, an RFQ is a subsequent step wherein the advisory board seeks detailed and competitive pricing information from selected vendors. An RFQ requires suppliers to provide specific and detailed cost breakdowns for the AI software solutions under consideration. It helps the board compare costs across vendors, ensuring an accurate and fair assessment. It also sets the stage for negotiations, paving the way for cost-effective and value-adding contractual agreements. Thus, while an RFI is about gathering information, an RFQ is about seeking the best financial terms. These steps are crucial to the GenAI Advisory Board’s procurement strategy, ensuring they make informed and cost-effective decisions.

Request for Information (RFI) Template for Generative AI Software Technology Vendors

An RFI is a preliminary document to gather vendor capabilities and understand the landscape of available offerings. Here is a template you can follow:

1. Introduction: Briefly introduce your organization and your need for generative AI software.
2. Purpose of the RFI: State the purpose of the document and what you hope to achieve.
3. Vendor Information: Ask for the vendor’s basic information, including company name, contact details, and years in business.
4. Product Information: Request details about the product, including features, underlying technology, and system requirements.
5. Customer References: Ask for references from clients who have used their generative AI software.
6. Ethical and Privacy Considerations: Understand the vendor’s stance and protocols for ensuring ethical use and data privacy.
7. Support and Maintenance: Get information about the post-purchase support and maintenance offered.

Request for Quotation (RFQ) Template for Generative AI Software Technology Vendors

An RFQ is a business process in which companies invite vendors to bid on specific products or services. Here’s a template for the same:

1. Introduction: Reiterate your organization’s identity and purpose of the RFQ.
2. Scope of Work: Clearly define the scope of work, including the technical requirements of the generative AI software you’re interested in.
3. Pricing Structure: Ask for a detailed breakdown of the pricing structure, including licensing fees, setup fees, and any ongoing costs.
4. Implementation Timeline: Request a proposed timeline for software implementation.
5. Terms and Conditions: Outline specific terms and conditions relating to payment, delivery, service level agreements, etc.
6. Response Due Date: Provide a precise due date for when the quotation should be submitted. 

Remember, responses to both RFI and RFQ should be used to inform the final decision-making process, considering factors such as the ethical implications, software capabilities, vendor reputation, and cost-effectiveness of the solution.

Reason 4: Determining best practices and methodologies of usage of GenAI tools

Determining best practices and methodologies for using generative AI tools in marketing is crucial for several reasons:

1. It ensures the effective and efficient use of technology, maximizing marketing ROI. It can help identify key metrics to gauge AI performance and inform future marketing strategies.
2. It ensures compliance and ethical use of generative AI, protecting the company from legal and reputational risks.
3. It helps educate and empower your marketing team, fostering a culture of innovation and continuous learning.
4. By defining best practices, your organization can better manage customer data, uphold privacy standards, and maintain public trust.

Thus, understanding and implementing best practices in generative AI is not only a matter of operational efficiency but also of ethical responsibility and strategic advantage.

Developing best practices for GenAI usage in marketing involves careful planning, research, and collaboration. Follow these steps:

Step	Description
1	Identify your marketing goals and understand how AI can assist in achieving these. Research industry standards, consult AI experts, and benchmark against competitors to inform your practice.
2	Develop a framework that outlines how AI will be used within your marketing strategy. This should include guidelines on data management, AI tool selection, and performance measurement. Clear roles and responsibilities should also be defined, ensuring all team members understand how to use AI effectively and ethically.
3	Test and refine your AI practices. Implement AI tools on a small scale initially, monitor results, and adjust your strategies based on performance data. This allows for continuous improvement and adaptation to changes in technology or market conditions.
4	Prioritize education and training. Regular training sessions and workshops can ensure your marketing team stays up-to-date with AI advancements and understands how to use AI tools effectively and ethically. By empowering your team with the necessary knowledge and skills, you can foster a culture of innovation and make the most of GenAI in your marketing strategy.

Remember, developing best practices is an ongoing process. Continuously review and update your practices to ensure they remain effective and compliant with ethical and legal standards.

GenAI Methodology Template

To streamline your GenAI marketing efforts, consider utilizing the following template to establish clear methodologies:

1. Objective Identification: Clearly list your marketing objectives. Detail how GenAI can contribute towards achieving these goals. 
2. Research and Benchmarking: Document your findings from industry research, expert consultations, and competitor benchmarking. This will serve as the foundation for developing your GenAI marketing strategies.
3. GenAI Marketing Framework: Outline your strategic framework for GenAI usage in marketing. This should encompass data management procedures, AI tool selection, performance measurement metrics, and clear roles and responsibilities. 
4. Testing and Refinement: Design a strategic plan for testing your GenAI tools and methodologies. Include guidelines for monitoring results and adjusting strategies based on performance data.
5. Education and Training: Describe the plan for regular training sessions and workshops to keep your team updated with GenAI advancements. Ensuring ethical usage of AI tools is vital to the curriculum.
6. Review and Update: Create a timetable for reviewing and updating your methodologies to stay in line with technological advancements and changes in market conditions.

Remember, the template is only as good as the information and commitment you put into it. Dedication to continuous learning and improvement will ensure the success of your GenAI-driven marketing strategy.

Reason 5: Implementing content governance elements of the content center of excellence

For several reasons, content governance is critical for generative AI (GenAI) in marketing. It ensures consistent and high-quality content by setting standards and guidelines. These rules help maintain the brand’s voice, style, and tone, which are integral to building brand identity and trust among consumers. Secondly, content governance aids in compliance with legal and ethical standards, which is crucial in the age of data privacy and AI ethics. It ensures that the use of GenAI adheres to regulatory requirements and ethical guidelines, thereby protecting the brand from potential legal issues. Lastly, content governance facilitates efficient content management. Providing a clear structure for content creation, distribution, and maintenance makes the process more streamlined and efficient, saving valuable resources and enabling a proactive approach to content strategy. Therefore, implementing content governance is a vital element in leveraging GenAI for marketing.

Reason 6: Evaluating software improvements, tracking market disruptors, and measuring outcomes

The GenAI Advisory Board’s role in performing these activities is pivotal for several reasons. First, evaluating software improvements aids in understanding the evolving capabilities of GenAI tools, ensuring the organization’s technology remains at the forefront of innovation. The board monitors potential threats and opportunities by tracking market disruptors, allowing the company to adapt and stay competitive in the fast-paced digital marketing landscape. Finally, measuring outcomes is crucial to assess the effectiveness of the GenAI-driven marketing strategy. It provides valuable insights into what works and what doesn’t, paving the way for data-driven decisions and continuous improvement. Hence, these activities are instrumental in steering the direction of the GenAI initiative and ensuring its success.

Evaluating Improvements

There are various strategies to evaluate software improvements in GenAI effectively:

1. One could conduct comprehensive testing sessions after each software update, using a set of pre-defined criteria that align with the business requirements and objectives.
2. User feedback is invaluable. Organizing user experience surveys and feedback loops would provide direct insights into the practicality and efficiency of new features or changes.
3. Comparing the new software’s performance metrics, such as processing speed, error rates, and resource usage, to previous versions would quantify the extent of the improvements.
4. Security audits can help identify any potential vulnerabilities introduced with the new updates, ensuring that the software maintains the highest data privacy and protection standards.
5. Tracking the return on investment (ROI) post-update, including factors like increased productivity or reduced operational costs, offers a clear picture of the financial impact of software improvements.

Tracking Market Disruptions

To effectively track market disruptions in GenAI for marketing, it’s crucial to have a comprehensive, well-rounded strategy in place:

1. Embrace advanced analytics tools that can provide real-time insights into market trends and shifts. These tools can detect patterns or changes in consumer behavior, purchase trends, and other key market indicators.
2. Staying abreast of industry news and updates can provide early warnings of potential disruptions. This could involve subscribing to essential AI and marketing journals, newsletters, and podcasts, and regularly attending industry conferences or webinars.
3. Consider establishing a regular dialogue with customers and other industry stakeholders. Feedback sessions, surveys, and social media engagement can offer valuable insights into the market’s pulse.
4. Competitive analysis should be an ongoing practice.

Observing competitors’ moves can provide indicators of upcoming disruption and allow your company to preemptively adapt. Investing in AI-driven prediction models is also advisable, which can forecast potential disruption based on historical data and current market conditions.

Measuring Outcomes

Measuring outcomes and ROI related to deploying GenAI for marketing use cases is essential for several reasons. It helps gauge the effectiveness of AI implementations, validates the costs and time invested, and provides valuable insights that can guide future decisions and strategies. Metrics to track include lead generation and conversion rates, customer engagement levels, click-through rates, and customer lifetime value. These measurements can demonstrate GenAI’s success in reaching and engaging the target audience and converting potential leads into customers. Additionally, metrics such as cost per acquisition and overall marketing costs can clearly show the financial savings realized through AI. It’s also worth tracking customer satisfaction and retention rates, as these can offer insights into the long-term benefits of deploying GenAI in marketing.

Suggested KPIs for Tracking GenAI in Marketing Use Cases

To effectively track the impact of GenAI in your marketing strategies, consider the following Key Performance Indicators (KPIs).

1. AI Engagement Rate: This measures the level of interaction customers have with AI-driven marketing initiatives. High engagement rates typically reflect a successful AI implementation.
2. AI Conversion Rate: This KPI reflects the number of conversions achieved directly through AI-enabled marketing strategies. 
3. Cost Savings: Measure the financial impact of AI in terms of reduced operational costs, more efficient resource utilization, and return on investment for AI-driven initiatives.
4. Improved Customer Retention: A significant KPI is the change in customer retention rates post-AI implementation.
5. Increase in Customer Lifetime Value (CLTV): Assess if AI implementation has led to an increase in CLTV, an indication of long-term customer loyalty and increased profits.
6. AI-driven Lead Generation: The number of quality leads generated through AI-enabled strategies.
7. AI-enhanced Customer Satisfaction: Track changes in customer satisfaction levels, which can be tied directly to AI enhancements in customer service or product recommendations.

When measured accurately and consistently, these KPIs can provide a comprehensive understanding of the value and effectiveness of AI deployment in marketing strategies.

Reason 7: Strategies for ongoing success with generative AI tools

The Generative AI Advisory Board can implement strategies for ongoing success with GenAI tools for marketing in a multitude of ways. First and foremost, investing in continuous learning and upskilling is crucial. As AI is a rapidly evolving field, staying abreast of new developments and technological advancements is vital. Regular training sessions, workshops, and seminars can help the team stay informed and ready to adapt to new AI tools and strategies.

Second, promoting a culture of innovation and experimentation is critical. Encourage team members to think outside the box, experiment with new ideas, and learn from failures. This fosters an environment where constant improvement is the norm, and stagnation is a foreign concept.

Third, the Advisory Board should consider structured brainstorming sessions where diverse teams can provide different perspectives on possible AI applications. This can lead to unexpected and innovative uses of GenAI tools, driving further success in marketing strategies.

Why is this so important? In the dynamic digital marketing landscape, staying competitive means staying ahead. Continuous innovation in applying GenAI tools can lead to more personalized and effective marketing strategies, ultimately resulting in higher conversion rates, improved customer retention, and increased revenue.

As for team structure, consider creating a cross-functional team comprising members from the marketing, data science, IT, and product teams. This diverse team can bring varied skill sets and perspectives to the table, contributing to a fuller understanding of GenAI capabilities, the marketing landscape, and how the two can best interplay.

In conclusion, the continuous innovation of GenAI tools, driven by a committed and well-educated team and supportive culture, is the backbone of future marketing success.

Final Thoughts

In summary, deploying a Generative AI advisory board is a strategic imperative for contemporary marketing teams. Instigating an environment that encourages innovation, experimentation, and learning from failures will ensure constant progress and prevent stagnation. A cross-functional team of individuals from diverse areas of expertise will enable a comprehensive understanding of GenAI and its potential applications in marketing. Through structured brainstorming sessions and continuous education on AI tools, marketing strategies can become more personalized and effective, leading to higher conversion rates, better customer retention, and increased revenue. Therefore, embracing Generative AI is not just about staying competitive—it’s about paving the way for future success.