Scaling Value with AI

As a Fortune 500 leader nearly 120 years old, with a global workforce of 95,000 employees and over $36 billion in annual revenue, Honeywell International is an ocean liner-sized business. Of course, "big" doesn't mean "immovable."

Honeywell has proven its agility since the early 2000s, when it reinvented itself by focusing on operational excellence, high-growth markets, and strategic investment in technology-driven innovation. In 2025, Honeywell again shows its skill at maneuvering its large organization more like a kayak than a freighter. It has not only embraced artificial intelligence (AI) to reshape its offerings and boost operational efficiency but also revamped how marketing and sales grow reputation, relationships, and revenue using generative and agentic AI.

We spoke recently with Ron McMurtrie, Honeywell's Chief Marketing Officer, about how his team has been using AI to improve marketing outcomes, boost sales results, and redefine customer value. Though this is a big-company story, for mid-sized B2B company marketers grappling with AI adoption, Honeywell's AI marketing journey also offers a valuable roadmap.

AI-Driven Marketing Success Stories

Honeywell's increasing dedication to customer centricity and value delivery directly influences the firm's AI-powered marketing approach. "We were a very activity-driven marketing organization, and one of the big pivots we've pursued is shifting from volume to value. That means emphasizing, creating, and measuring what best builds customer and company value for Honeywell," McMurtrie shared. With that in. mind, Honeywell has been experimenting and testing AI across several marketing and sales functions to create a more profound impact:

• Campaign Optimization

"We use AI for campaign research and calibration," McMurtrie explained. "For a global campaign on building management systems, AI provided rapid insights into creative, messaging, and targeting by market. It used to take days for a team to gather similar data—AI did it almost instantly. It also is helping us shift to a closer measurement of funnel velocity and pipeline creation versus more volume-oriented metrics. As a result of better-aligned messaging, our pipeline creation and conversion rates improved fourfold."

• Customer Listening

"We productized an AI tool for competitive listening. The tool helps us better understand how our audiences respond to our messages and guide our campaign voice work," McMurtrie said.

• Content Drafting and Personalization

"Currently, we use AI to generate initial draft content but not for final authoring. We need a subject matter expert to help develop the final version. We've had some success there," said McMurtrie. He also highlighted the role of AI in creating tailored messaging for Honeywell's industrial audience: "In the industrial sector, where messaging often gets lost in a 'sea of sameness,' personalization is key to breaking through."

• Sales Enablement | Prospect Intelligence

Honeywell is also using to AI support its sellers. For example: "We've digitized the old-fashioned briefing book," McMurtrie noted. "Sales teams now have dynamic profiles with insights into prospects' needs and financials. It makes every interaction more informed and impactful."

Reflecting on Honeywell's AI marketing progress to date, McMurtrie said, "We've been using AI to give us greater speed. We've used it to give us insights into what others say about our market and products, allowing us to pinpoint smarter strategic practices to use. I have not seen good use cases for using AI to support high-value differentiation. I would love that, however. If you can crack the code of enriching messaging and enhancing differentiation through AI, that's a money maker."

He added, "We've done a lot, but our use of AI for marketing has been more experimental than production-focused. Based on the AI roadmap we've defined, 2025 is the year we move into production mode."

Overcoming Challenges in Scaling AI Marketing

"There are many potential obstacles to success in adopting AI for marketing, from data to technology to talent," said McMurtrie. Here's how Honeywell has experienced and responded to the challenges.

• Data

McMurtrie highlighted significant issues with Honeywell's data infrastructure. Despite investments in an enterprise data warehouse that consolidates financial and engineering information, Honeywell's marketing data still lags. Honeywell has eight separate Salesforce instances that do not fully communicate with one another, creating "islands" of structured and unstructured data. This lack of integration complicates efforts to unify marketing and sales data, essential for AI-driven marketing initiatives.

• Technology

According to McMurtrie, most of the existing AI tools on the market "aren't ready-made solutions that fit our needs right off the shelf." Instead, Honeywell often adapts and builds customized tools to meet its specific requirements. For most of Honeywell's AI tech, we must make rather than buy what we need." To do that, Honeywell has created a shared services group that supports marketing with AI solutions. Honeywell staffs this AI center of excellence with data science and AI experts who can handle complex modeling and advanced analytics tasks. Marketers provide them with AI use case ideas and business requirements, and the shared team will create it.

• Talent

"Our marketers all have access to AI tools they can use independently. Honeywell also invests in training programs to ensure broader AI literacy among our marketers. For instance, if you're on the marketing creative side where we're an Adobe shop, you're getting trained and certified on Adobe's AI-enhanced tools. We also bring in new AI knowledge from outside the company. We are bringing in new AI-savvy recruits from Georgia Tech and other universities through our early talent program. And each year, we add more to each Honeywell business group.

Lessons Learned for Mid-Market Companies

Honeywell's AI marketing initiatives offer practical lessons for mid-market company marketers seeking to adopt AI effectively:

1. Adopt a Phased Approach:

Start with experimental AI projects, refine them, and transition into scalable, repeatable solutions. Balance innovation with manageable steps to ensure sustainable adoption.

2. Prioritize AI-Driven Outcomes Over Activities:

Shift focus from measuring activity metrics (e.g., leads generated) to delivering measurable business outcomes like ROI, pipeline acceleration, and customer value.

3. Invest in AI for Campaign Optimization:

Use AI tools to streamline research, understand competitor strategies, and adjust campaigns based on real-time feedback - reducing manual effort and accelerating campaign effectiveness.

4. Build Cross-Functional AI Expertise:

While you may not be able to have your own internal "AI Center of Excellence", you can focus on developing cross-functional AI expertise for your organization through a talent ecosystem model: engaging external AI-marketing experts who can bridge the gap between technical capabilities and marketing needs. These non-employee human resources can work alongside your internal marketing team to provide the specialized knowledge and strategic guidance while fostering collaboration and continuous learning to drive innovation.

5. Strengthen Sales Enablement with AI:

Equip sales teams with AI tools for pre-call intelligence and lead prioritization. Empowering sales with real-time insights enhances their ability to close deals effectively.

"B2B marketers who want to succeed with AI marketing must plan for it. They must start with a clear strategy and roadmap for using AI to drive growth, prioritize experimentation and iteration, and reimagine their marketing organization – redefining and realigning team roles with AI's capabilities."

By applying these lessons, mid-market companies can accelerate their AI journeys, enhance customer engagement, and achieve impactful business results.