Unlocking Growth Part 2: AI Empowers Organizational Functions

From personalized customer engagement to product innovation and go-to-market strategies, AI is reshaping how we operate and compete. This article is the second installment in a three-part series capturing the key insights from the ‘Future of Automation and AI’ fireside chat at the GTM Alliance. Read on to learn about how AI empowers sales, marketing, and product teams, offering a glimpse into the future of AI-driven growth and efficiency.

AI has disrupted every discipline and organizational function that exists. The journey of organizations from app development combined with the storage and transformation of data on the cloud was a decade in the making. As we move towards GenAI, data intelligence platforms are enabling AI-led products in a much shorter period of months or a year.

Teams are faced with a two-pronged problem:

1. Engaging customers at scale in a way that feels personalized and humanized means having a well-integrated data platform with clean data.
2. Engaging customers so they can efficiently use complex B2B SaaS platforms means simplifying messaging and experiences outside the product while also simplifying and consumerizing the product experience.

AI-first solutions come into play to solve these problems and as a result, have transformed organizational functions.

1. Sales & Field Teams:

• Personalization at scale needs to combine both people-based and digital-led strategies. It means disrupting traditional frontline operating models with predictive growth-score-based or propensity-based lead-gen models.
• This approach lowers the cost to serve, targets the right customer at the right time, and creates an effective customer engagement model that is humanized and yet takes advantage of the myriad of data points about the customer.
• Salesforce’s Sales Cloud Einstein and HubSpot’s CRM tools are great examples of how AI streamlines sales processes, enhances customer relationship management, and improves sales forecasting accuracy.

Leverage for the frontline is created by:

• Actively listening to customer experiences, transactions, and operational feedback.
• Processing these inputs to generate customer insights, forecasts, and intelligence signals.
• Combining these insights with support/help/enablement content to then proactively respond via Conversational AI, enable the frontline, and potentially improve the product experience.

The same is true for digital-led strategies. Hyper-targeted advertising campaigns and optimized content creation are now AI-led. Coca-Cola’s AI-powered vending machines and The Washington Post’s AI-driven article suggestions aim to improve marketing impact.

2. Product & Marketing:

• AI also informs the building of intuitive user experiences in products to achieve product-market fit.
• From the voice of customer insights to developing product-led growth loops, AI-led product development utilizes solutions such as propensity models to deliver optimal customer experiences.
• Features for the same persona may need to look different if they’re an SMB developer with a small IT team vs. an Enterprise developer with diverse infrastructure resources.
• Google’s AutoML platform and GitHub’s CodeQL tool showcase how AI drives product innovation and enhances software development processes, from idea generation to bug detection.

For marketing and learning organizations, content creation and curation are now completed by large language models that are responsive. Identifying customer segments, understanding the categories of content that resonate with personas, and dynamically building ideal customer profiles based on intent and usage are a few applications of GenAI solutions.

The basics are still applicable in that customers want to learn how to build better products, sell better, and operate their organizations effectively. For example, a newsletter titled ‘What’s new with <Product or Company Name>’ is less interesting than something along the lines of ‘What’s new in Data Intelligence’ or ‘What’s new in GenAI’. Even subject lines can be further optimized with ChatGPT. Adobe’s Marketing Cloud platform illustrates how AI improves sales processes, optimizes marketing campaigns, and enhances customer segmentation.

3. GTM Leaders Developing Strategy:

• GTM leaders developing strategy and operational models, product leaders building the next-gen design, and sales leaders reimagining enablement of their frontline teams all have one thing in common: every organization is reimagining how work gets done, not just what output is delivered.
• Microsoft CEO, Satya Nadella, has invested heavily in AI to enhance not just its product offerings but also create operational efficiencies. Azure AI is a critical part of the Microsoft cloud go-to-market strategy combined with helping businesses co-create and co-sell AI applications.
• HubSpot’s use of AI in CRM tools, predictive lead scoring, and automated marketing campaigns are other examples of how leaders are reimagining product-led AI.

There’s a GenAI solution for all, from coding editors to image generators. Netflix’s data-driven content recommendations and Walmart’s real-time data analysis for inventory management are a few use cases. Applications revolve around a better understanding of customer behaviors and product usage patterns and converting customer experience data to faster response times and delivery. Automation of repetitive tasks, co-creation, and personalization are three key areas that we’re leveraging the unlock of AI.

It’s no longer just sufficient to augment the product itself but people and processes are significantly impacted day to day. However, most organizations face the same challenge — clean, hygienic, and integrated data. Above all, the challenge of secure, safe, and authentic data.

Data integration is another challenge with disparate data sets across commercial, sales, product usage, and several third-party tools used by organizations today. Creating clean data sets that enable the development of 360 performance views for customer revenue growth is a key task at hand here. DBRX is an open, general-purpose Large Language Model (LLM) developed by Databricks. It has set a new state-of-the-art for established open LLMs. To use DBRX, though, practitioners need clean data and as such can use Apache Spark, Delta Lake, or Notebooks for large-scale data cleaning tasks, ACID transactions, and mixing in code respectively.

We are undeniably transforming every facet of how we work every day and, as a result, reshaping organizational functions, from sales and marketing to product development. As this landscape evolves, the emphasis on clean, secure, integrated data is paramount. AI has truly reimagined how today’s leaders drive growth and innovation at an unprecedented pace.