How to Implement Generative AI to Transform Your GTM Strategy in 2024

By ICR Growth Marketing Team

With the advent of 2024 planning season, companies of all shapes and sizes are grappling with how to increase revenue and streamline costs. While generative AI provides a compelling lever, the C-suite continues to wrestle with how to implement this transformative technology in a strategic way to architect the next phase of growth.

ICR hosted its fourth session from its Generative AI series, featuring Paul Roetzer, the founder and CEO of the Marketing AI Institute and Asma Stewart, Managing Director of ICR’s Growth Marketing practice, to discuss how organizations must harness the power of generative AI to transform their go-to-market strategy. Both speakers have worked in the generative AI software space for years prior to the introduction of ChatGPT, and shared key insights for investors and operators alike who are evaluating how AI can drive GTM efficiency.

Scope the Benefits

As organizations begin to scope out how AI can transform the GTM, efficiency is an obvious benefit and the one that gets the most attention. Fundamental benefits to the GTM strategy where the organization can gain immediate efficiency include:

• Faster content creation
• Automated data entry and ingestion
• More personalized messaging
• Complex and effective lead nurture
• Accurate documentation aggregation
• Customer support resource center
• Predictive lead scoring and forecasting
• Optimal upsell and cross-sell recommendations

Since AI can streamline operations, surface insights and automate manual tasks, an overlooked benefit is that it frees up capacity to innovate. Yes, efficiency is initial benefit, but companies will miss the transformative strategic benefits if leadership ignores its capabilities to drive innovation, experimentation, and learning. With increased adoption of these technologies, efficiency will evolve into table stakes, and companies that prioritize innovation will break out, accelerate growth, and claim market leadership.

For example, generative AI provides in-depth insights into prospects and customers alike. By analyzing their behaviors and preferences, marketing and sales will be able to align their efforts  to speak more directly to pain points and cut through the noise. The result? Improved targeting, better conversion rates, increased revenue, and a stronger connection with the market.

Organize for Success

Creating an organization-wide AI strategy is a significant undertaking. Many departments within the company may have already adopted ChatGPT and other tools. To realize the transformative potential of Gen AI, companies must prioritize evolving to an AI-powered organization.

• Recruit a cross-functional generative AI council to ensure organization-wide buy-in
• Assess the organization’s AI maturity; identify adoption gaps within people, process and systems
• Develop an AI point of view and communicate policy to internal and external stakeholders
• Identify initial use cases and quick wins to prove value. Run pilots to test and validate.
• Circulate pilot results, feedback and develop roadmaps for scaling
• Earmark requisite budget, KPIs, training and enablement
• Communicate road map and regularly update progress to the board, customers and investors

Executive teams must themselves adopt the technology to right-size their understanding of capabilities and limitations. This also ensures executive buy-in, which will fuel the broader adoption across the organization.

There is already a glut of SaaS providers that claim to be AI powered with many overlapping features. Start by evaluating your current sales and marketing tech stack to see what generative AI capabilities currently exist within your existing tool set. From there, you can add on additional tools that map to the high-value use cases and quick wins identified for first-run pilots.

Assess Risk and Manage Change

There are key risks that your company needs to strongly consider in its implementation of generative AI, including data security, privacy, trademark and copyright, and understanding how these AI models are trained. Using publicly available large learning models (LLMs), can carry the following risks:

• Hallucinations – Content that is completely fabricated or untrue but is presented authoritatively as fact. Human validation and curation is critical to validate.
• Data privacy and retention – Use of material financial information, private customer data, personal contact info, conversations, product details, etc. It should be assumed that any public LLMs retain and train on any data your company inputs.
• Bias – The fundamental data set can contain bias, stereotype and/or prejudices that fuel biased interpretations and responses. It is critical for the team to recognize and mitigate these biases.
• Auditability – There is limited visibility into LLM algorithms, how they are trained, and how they can reinforce biases. This is especially important for regulated verticals/industries.
• Brand compliance – Public models may produce inappropriate or non-compliant content if not trained/aligned to an organization’s voice.
• Security flaws – Integrating non-vetted LLMs may expose internal systems hackers and malware.

Many organizations, especially those in industries that are regulated, will require private LLMs trained only on vetted internal data. It will be critical for the generative AI council to weigh the tradeoffs to adoption and ensure the proper policies are in place.

Additionally, the introduction of generative AI will lead to major shifts in roles and responsibilities within the organization. Proper change management processes are key and should include ongoing communication, training, and support to help teams adapt and adopt new ways of working. As roles evolve, employees need to understand how AI impacts their work and what new skills they may need to develop. Open and transparent communication about these changes, along with the necessary training and education, can ease this transition and should create a more agile and empowered workforce.

Measure Progress

Companies should absolutely develop specific KPIs tied to the fundamental business problem to which they are applying generative AI. Fundamentally, adopting generative AI should contribute to either revenue acceleration or cost reduction.

Revenue acceleration

• Better ad targeting improves return on ad spend and higher campaign conversion rates
• Automated outbound improves sales-led pipeline revenue
• Personalized product recommendations increase cross-sell and upsell bookings $
• Higher quality and SEO optimized content boost web traffic growth
• Customer data reveals higher pricing tolerance and packaging for better value

Cost reduction

• Automation throughout the GTM increases team capacity for innovation
• Natural language generation automates content to multiply blog output
• Real-time intent data and lead scoring triangulate rep’s time to improve sales cycles
• AI chat and self-serve customer service reduce time to support, improve Net Promoter Scores, and increase retention
• Centralized data provides metrics visibility and reduces time to create accurate reporting

While organizations should absolutely map how each incremental generative AI initiative will drive efficiency, investment should be evaluated holistically in terms of bottom-line performance. Ignore qualitative data points at your own risk: make sure to constantly talk to your employees to ensure that they are seeing progress, especially in the early days.

Communicate to the Market

Generative AI can be a transformative lever for rearchitecting the GTM strategy to compete in the next phase of the modern economy. Once your company has its ducks in a row, create a publicly available AI road map with key milestones and expected results. Similar to ESG roadmaps, the markets will be holding businesses accountable to showing measured progress against generative AI in the next 5 years.

Like any company-wide initiative, AI must be approached with a crawl, walk, run mentality. Larger organizations should aim to have initial pilots completed by second quarter of 2024 at the latest, with a clear roadmap and expected investment earmarked by the end of the year. To help your executive team, the Marketing AI Institute provides great roadmaps, resources, events, and a growing community for companies to get started.

We have passed a tectonic shift for the modern economy: companies must prioritize the transition to an AI emergent company. By aligning, prioritizing, investing, and implementing in the next 12-18 months, your business will be set up to compete for the next 10+ years.

To thrive in this new market environment, companies will need support. Whether it’s for the next fundraise, acquisition, IPO, or the next earnings call, ICR’s Growth Marketing Practice can help your company develop an AI roadmap, right-size investment, achieve efficient growth, and communicate that story to the markets.

Our Growth Marketing practice partners with investors and the C-suite to build an efficient revenue engine powered by AI. Kick off a free GrowthCast Snapshot to surface prioritized opportunities for 2024 planning.

Want to hear the full conversation? Download the generative AI webinar recording today.