Erin Pearson holds a marketing leadership position at Evalueserve, a global professional services company specializing in tech-enabled managed services. She leads the move from experimental AI initiatives to production-level implementations with measurable returns on investment. Her responsibilities cover competitive intelligence, sales enablement, and campaign optimization, with a focus on developing AI agents and automated workflows that improve marketing efficiency and revenue impact. Erin brings a practical, implementation-focused approach to AI adoption, balancing automation with the human expertise that sets professional services organizations apart.
Evalueserve
Evalueserve is a global professional services company specializing in tech-enabled managed services across diverse industries. Delivering tailored, consultative solutions rather than off-the-shelf approaches, Evalueserve provides research, analytics, and strategic insights that help organizations make informed decisions. The company uses advanced technologies, including AI and automation, to deliver high-quality outputs and speed up time to value for clients.
“This interview is part of the New Skills, New Mindset Market View Report, produced by TechPros and sponsored by 6sense.”
How is your organization deploying AI agents within marketing workflows, and which use cases have delivered measurable value?
We have moved beyond experimentation and are now running focused use cases in production, backed by clear KPIs that show real ROI. Our setup uses n8n for workflow orchestration, which lets us build automation sequences that connect with our existing systems.
One agent that works particularly well focuses on meeting preparation. It identifies upcoming attendees, then researches both the individual and their organization—pulling recent company news, earnings reports, executive commentary on priorities, and relevant LinkedIn activity. The agent turns this into tailored talking points that link our offerings to the prospect’s business situation, helping us have more meaningful first conversations that show we have done our homework.
We have also built an agent for event intelligence that reviews potential industry events based on our target audience, then automatically fills our project management system with the key details. This turns what used to be occasional manual research into ongoing intelligence-gathering. We are now extending this to look at announced speakers at target events, so we can send personalized outreach before we attend.
As buyers increasingly use AI tools during research, how are you adapting your strategy to be found by large language models?
Much of the buyer journey now happens before any direct sales contact. More and more, prospective clients go to ChatGPT or similar tools with specific problems, asking for vendor recommendations. This means marketers need to rethink how we explain what we do, so AI systems can find us and recommend us accurately.
The basics still matter: original research, solid case studies, and clear explanations of what makes us different. But an LLM needs enough detail to understand and sort your offerings. If visitors to your website struggle to grasp your value, AI systems will have the same problem, and you will not make it into their recommendations.
The shift from keyword optimization to conversational search is a big change. We now need to think about the natural questions prospects ask AI assistants, moving past generic messaging toward specific content that addresses real challenges. Companies that spell out their unique point of view clearly will stand out from competitors saying much the same thing.
“Real AI adoption starts with identifying the bottleneck. Find the pain point, and you’ll get people to experiment.”
What is your view on AI’s impact on marketing productivity, and how do you balance automation with professional expertise?
There is a common belief that AI speeds up all marketing work dramatically. The truth is more mixed. While AI is great at early research and drafting, the checking and refining stage has grown as a result. Before, putting a lot of effort into upfront research meant first drafts were fairly polished. Now, quick drafts need careful quality checks to make sure they meet professional standards.
Marketing materials are often the first sign of what working with a company will be like. Prospects naturally assume that if marketing quality is poor, service delivery will be too.
The ease of creating AI-generated content has flooded the market with more output, but it has also opened up chances for organizations committed to real thought leadership. Content that shows original thinking and first-hand research now stands out more clearly from generic material. Companies putting effort into fewer, better assets are winning more attention as audiences get pickier about what they read.
“If a buyer can’t understand what you do from your website, neither will an LLM. Generic messaging fails both human and AI audiences.”
When turning signals into action, what separates organizations that use data well from those that struggle?
Today’s marketing environment offers plenty of intelligence. The challenge is not getting the data but putting it to work. Signal delays are a basic constraint—by the time platforms spot and share signals, several organizations usually get similar information at once.
For companies serving large enterprises, signal detail adds another layer of difficulty. Account-level buying signals are hard to act on when target organizations have tens of thousands of staff across many business units. The key skill becomes linking broad signals to specific people and matching outreach with existing relationships.
The best model I can see brings together signal detection with automated research and content preparation. When signals come in, AI agents research stakeholders, find the right contacts, and prepare personalized messages. People then focus on checking and approving rather than creating from scratch. Many organizations talk about this approach; how well they actually do it is what sets them apart.
“Marketing sets expectations for everything that follows. If your marketing is mediocre, why would prospects expect your deliverables to be any better?”
What will set apart marketing leaders who handle this transformation well?
Good AI integration lifts team capability rather than replacing it. The opportunity is using automation to extend what skilled professionals can do—helping specialists work at greater scale while spending their time on high-value tasks that need human judgment.
Smart automation should target repetitive work that pulls attention away from meaningful projects. When people can put more energy into creative problem-solving, both quality and job satisfaction go up, creating a positive cycle of engagement and new ideas.
Getting it right also takes careful change management. New tools need to show early wins to build confidence across the organization. Initial successes, shared well internally, create momentum for wider change. Getting genuine buy-in matters greatly, especially when bringing in AI-driven processes that may feel unfamiliar at first.
