In an exclusive conversation with Martechai.com, Arppit Sharrma — Assistant Vice President at Industrial Motion — outlines how Martech is transforming one of the most legacy-bound sectors: industrial manufacturing. From leveraging AI to fuse real-time machine data with marketing strategy, to deploying generative AI for technical storytelling, Sharrma shares how his team is redefining growth and customer engagement. He also breaks down why Martech should be treated like an engineering discipline in B2B, how predictive systems are reshaping buyer interactions, and what’s coming next—from generative engine optimization (GEO) to AR field enablement.

Anupama Mitra (AM):  Industrial Motion operates in a legacy-heavy sector. What unique challenges have you faced in embedding Martech solutions in an industrial manufacturing context?

Arppit Sharrma (AS): Embedding Martech in industrial manufacturing isn’t about chasing fleeting trends—it’s about translating cutting-edge technology into tangible industrial logic. Our most significant hurdle? Unearthing critical data often buried deep within decades-old Manufacturing Execution Systems (MES) and siloed engineering specifications. We have literally found vital customer usage data residing solely in Programmable Logic Controllers (PLCs) on factory floors! Bridging this required crafting custom APIs and, more importantly, bridging the mindset gap. It’s about demonstrating to engineers how real-time vibration data from connected sensors can directly inform next quarter’s content strategy around predictive maintenance. It’s about respecting our rich legacy while meticulously constructing intelligent bridges to the future.

(AM):  How do Martech investments align with ROI in an environment where the sales cycles are long, and decisions are highly technical or engineering-driven?

(AS): In our world of manufacturing and distribution, ROI isn’t measured in clicks—it’s measured in the velocity of our commercial pipeline. We strategically leverage Martech to de-risk technical decisions for our clients. Consider our Content Hub, which features interactive 3D model viewers and scenario simulators. Prospects who deeply engaged with these assets moved 30% faster from the initial consideration phase to technical validation. We meticulously track metrics like cost-per-technical-lead and sales cycle compression, unequivocally proving that Martech accelerates revenue, not just mere awareness. It’s about tangible impact on the bottom line.

(AM): How are AI and machine learning shaping the way you approach not just customer engagement, but internal decision-making across sales and marketing functions?

(AS): AI’s impact extends far beyond chatbots. We feed sophisticated machine learning models with a rich tapestry of CRM, ERP data, service logs, and even anonymized product telemetry. This allows us to predict not just who might buy, but what specific component might be under stress in their current setup. Imagine sales reps entering technical reviews armed with AI-driven ‘reliability risk assessments’ tailored to that client’s specific machinery. Internally, this is fundamentally shifting marketing’s focus from discrete ‘campaigns’ to ‘predictive solution triggers’—proactive interventions that address client needs before they even fully emerge.

(AM): In what specific areas has generative AI added unexpected value to your marketing or product communication strategy?

(AS): Beyond its obvious utility in drafting social posts, Generative AI has become our indispensable ‘technical translator.’ We’ve meticulously trained a custom Large Language Model (LLM) on 30 years of engineering manuals, IEC standards, and sales transcripts. Now, it can instantly convert highly complex mechanical specifications into clear, compelling value propositions for diverse buyer roles. For example, it effortlessly translates ‘harmonic drive backlash <1 arc-min’ into ‘precision that reduces your assembly line recalibration downtime by 15%.’ This is about scaling a depth of expertise we simply couldn’t hire fast enough.

(AM): Are you integrating AI beyond marketing—for example, into supply chain communication, technical documentation, or internal knowledge-sharing systems?

(AS): Absolutely. AI is permeating every surface of our operations. We’re seeing its transformative power in three key areas:

1. Supply Chain: AI analyzes global logistics data in conjunction with supplier communications to predict potential delays, even auto-generating customer notifications with revised ETAs.

2. Technical Documentation: Generative AI drafts preliminary service bulletins by ingesting field failure reports, which engineers then review—cutting drafting time by a remarkable 50%.

3. Knowledge Sharing: Our internal ‘Copilot’ indexes all project files, empowering engineers to ask precise questions like, ‘Show me torque calibration procedures used for Client X in humid environments.’ It’s essentially our institutional memory, on demand.

(AM): For B2B industrial buyers, relevance is everything. How are you using first-party data or intent signals to personalize interactions at an account level?

(AS): We ruthlessly combine first-party data to achieve unparalleled relevance. Here is a prime example: when a key account’s engineering team downloads a white paper on thermal management, and our IoT platform simultaneously shows elevated operating temperatures in their connected machine, it triggers a hyper-personalized sequence. The sales rep receives an immediate alert, and the client receives a tailored case study on thermal solutions for their exact machine model, coupled with an offer for a complimentary thermal scan. It’s the fusion of intent data and operational data that creates truly irresistible relevance.

(AM): Could you walk us through your current Martech stack? What types of tools or platforms are you prioritizing in 2025—CDPs, ABM tools, no-code workflows, etc.?

(AS): Our Martech stack is architected for integration resilience:

• Core: Internal CRM, Microsoft BC (ERP), self-built CDP, Marketo (Orchestration)
• Intelligence: Buyer intent signals, deeper account-level insights, custom AI models tailored to industrial data—from predicting machinery failures to optimizing content relevance. We also heavily explore prototyping and research, especially for complex deep learning applications.
• Content: Sitecore (CMS), Salsify (PIM), 3D Interactive Models

For 2025, our focus is squarely on scaling our no-code workflow hub to empower product engineers to build campaign triggers autonomously, without relying on IT. We’re also aggressively prioritizing Generative Engine Optimization (GEO) to dominate technical query searches, recognizing a fundamental shift in how industrial buyers seek information.

(AM):  How do you ensure alignment between marketing, sales, and engineering when deploying AI-led initiatives in customer-facing strategies?

(AS): We foster deep alignment through joint ‘AI Proof-of-Value Sprints.’ Before any customer-facing AI rollout, we lock key stakeholders in a room for 72 hours. Marketing defines the use case, Sales defines the conversation impact, and Engineering defines data feasibility. For our predictive lead scoring model, Sales insisted on a simple ‘Trust Score’ overlay that engineers could easily validate. Shared KPIs—like ‘Technical Validation Rate’—are instrumental in keeping us united and focused on shared success.

(AM):  Have you seen AI tools change the nature of conversations between sales reps and potential clients—for instance, through predictive scoring, email generation, or conversational intelligence?

(AS): Conversational intelligence tools revealed a stark reality: our reps were spending 70% of discovery calls simply explaining basics. In response, we developed an AI Coach that listens in real time, prompting reps with critical insights, like ‘Client mentioned vibration issues—share Case Study #23 on damping solutions.’ More crucially, predictive scoring flags which technical specification will most resonate with a specific client. Our reps now lead with invaluable insight, not rote interrogation. This shift has directly contributed to a 12% increase in deal sizes.

(AM): Do you see a role for AI in future product co-creation or industrial innovation—beyond its marketing use cases?

(AS): Absolutely, 100%. We strategically use AI to analyze vast amounts of unstructured data—global support forums, competitor patents, even academic papers—to pinpoint unmet needs. Recently, Natural Language Processing (NLP) uncovered recurring complaints about a specific gearbox mounting process within maritime forums. This insight directly informed our R&D, leading to the development of a new quick-mount system in collaboration with manufacturers. AI is rapidly becoming our ‘voice of the machine listener,’ providing invaluable feedback for innovation.

(AM): How are you benchmarking Martech adoption against competitors in your sector? Are industrial firms catching up or still lagging in digital maturity?

(AS): While many industrial firms still lag, leaders are making significant leaps. We track a ‘Digital Maturity Index’ through anonymized data consortiums like MAPI. While 60% of our peers still rely on basic CRM, innovators like Rockwell Automation and Siemens are rapidly closing the gap. Our competitive edge? We treat Martech as an engineering discipline—with rigorous change control and stringent uptime SLAs. Industrial buyers, by their very nature, demand that same level of reliability from their technology partners.

(AM): How do you distinguish between trend and transformation when evaluating emerging technologies like GEO (Generative Engine Optimization), AR, or digital twins?

(AS): Our filter is refreshingly simple: Does it solve a multi-million-dollar problem? For instance:

• AR for remote technician assistance? Transformative—it cuts $500K/year in travel costs.
• Digital twins for predictive maintenance? Absolutely core to our strategy.
• GEO for technical content? Critical, especially as engineers bypass traditional search engines for specialized databases.
• Pure-play metaverse showrooms? That’s a trend.

Our unwavering filter is tangible impact on downtime, cost-per-install, or design cycle speed. We invest in what truly moves the needle.

(AM): Can you share an example of a recent campaign or customer-facing initiative where marketing technology made a measurable impact on business outcomes?

(AS): We executed a highly targeted campaign aimed at plants with aging vibration sensors. Leveraging intent data combined with installed base analytics, we identified 500 high-fit accounts. We then shipped AI-powered diagnostic dongles, each with a QR code that triggered a personalized portal revealing their equipment’s specific risk profile. The results were astounding: a 42% engagement rate, a 28% pipeline contribution, and a substantial $8.7 million in new sensor sales. Martech made it possible to deliver scalably personal and impactful experiences.

(AM): What trends or technologies are you closely watching that could significantly influence industrial marketing in the near future?

(AS): I’m intently watching several key trends:

1. Generative Engine Optimization (GEO): As engineers increasingly query specialized databases rather than traditional search engines, optimizing for platforms like Engineering360 will be paramount.

2. AI-Powered ‘Product Stories’: The dynamic generation of technical narratives directly from live product data will revolutionize how we communicate product value.

3. AR Field Enablement: Overlaying real-time IoT data onto physical machinery via tools like HoloLens for enhanced maintenance and troubleshooting will become standard.

4. Ethical AI Audits: As industrial AI scales, transparent model governance and explainable AI will become non-negotiable brand differentiators. Trust is paramount.