Peter Steube
Community FounderThe Human Voice in the Agentic Era: Connecting the Dots Between Community and AI
“A mindset shift I would encourage for companies to be successful in deploying a bespoke AI agent is that they need to be overly pragmatic and have an eye towards iterative evolution, rather than biting off more than they can chew.”
Peter Steube has been reshaping how organisations approach growth — shifting the focus from traditional sales motions to building ecosystems rooted in trust, dialogue, and shared value. Working across AI, automation, and executive communities, he has created networks that go beyond engagement to actively inform decisions and drive meaningful business outcomes.
Rather than relying on conventional go-to-market playbooks, Peter advocates for a more connected approach. As AI transitions from buzzword to business tool, he views it as a mirror — highlighting gaps in processes, clarity, and organisational readiness.
In this Expert Hub conversation, Peter breaks down the rise of community-driven growth, the realities of applying AI in day-to-day operations, and why long-term success increasingly depends on credibility, alignment, and strong professional networks.
Q & A
You’ve hosted over 1,000 networking events for the finance elite and now advocate for a "human voice" that stands out in the age of AI. How do you ensure that as we automate more, we don't lose the "authenticity bonus" that makes a brand or a community actually feel real?
As we automate more, there should always somehow be a layer that isn’t automated. I believe that certain interactions shouldn’t be replaced, even with advancements in the quality of AI and personalization. Brands and personal creators alike should seriously weigh the pros and cons of how they use AI to generate content – consumers are now well accustomed to sniffing out copy that is purely AI generated – the more personal the copy gets (i.e. commenting on posts), the higher the risk of backlash. In doing so you’re also leaving open the door to creativity and context that AI cannot replicate, that’s where a community actually feels real for members of your network.
SearchUnify Lens:
As enterprises accelerate toward Agentic AI, the real differentiator will not be how much is automated – but how intelligently humans remain part of the loop. At SearchUnify, we believe Human-in-the-Loop (HITL) capabilities are essential for building AI systems that are scalable, trustworthy, and operationally responsible, especially in high-stakes industries like BFSI. Our industry-specific AI agents are designed to streamline workflows while still enabling human validation wherever context, compliance, or customer sensitivity requires it. This human-first approach is explored further in our perspective on AI for BFSI; our discussion on human-in-the-loop support models in Season 3, Episode 5 of The Customer Service Show, and our blog on Agentic AI in customer support.
This philosophy also shapes how we view the future of enterprise search and AI orchestration. While retrieval-only approaches prioritize control, modern enterprises increasingly need systems that can unify fragmented knowledge, generate contextual intelligence, and still maintain governance through human oversight. As explored in our analysis of Agentic AI Enterprise Search, the future of AI-driven experiences will depend on balancing autonomy with accountability – not replacing human judgment altogether.
You describe your career arc as "non-traditional" and "constantly evolving," starting from Wall Street trading desks to Head of Community at Capto. For leaders navigating the AI landscape from an "outsider" background, what is the most critical "dot" they should be connecting right now?
The most critical point they should be connecting right now is “how is my work impactful and attributable”, especially with AI, there’s a lot of noise and claims. Those who stand out are the ones who make their mark by building something that actually works and has clear value to their audience.
SearchUnify Lens:
Peter’s perspective on “impactful and attributable” AI reflects a growing shift in enterprise AI strategy: moving beyond experimentation toward systems that create measurable value for the end user. In community ecosystems especially, that impact depends on how effectively organizations can unify fragmented knowledge into meaningful, actionable experiences. Through SearchUnifyFRAG™ and Cognitive Search, SearchUnify helps enterprises federate knowledge across platforms to power stronger, AI-driven communities, as explored in our infographic on strengthening online communities with Agentic AI.
This approach has translated into measurable outcomes for customers like Varonis, where SearchUnify Cognitive Search helped increase case deflection by surfacing more contextual answers directly within the community experience. At the same time, our federated search framework preserves source-level, role-based access controls—ensuring AI-driven knowledge discovery remains both impactful and secure.
You view community and advocacy as "conducting a symphony of content". In massive enterprises where data is siloed across Slack, Jira, and Salesforce, how can Agentic AI act as the conductor that finally brings all those disparate notes into a single point of truth for the customer?
If community and advocacy is in fact a “symphony” then Agentic AI can act as a conductor by placing all the disparate instruments (i.e. points of data) into a single auditorium. It can then also be a more precise listener to what sounds are pleasing the audience, need to be turned up or down accordingly.
SearchUnify Lens:
Peter’s “AI as a conductor” analogy captures a fundamental challenge in enterprise environments: harmonizing fragmented data into something coherent, contextual, and responsive to the audience. At SearchUnify, this orchestration comes to life through our Agentic RAG approach, where deep connectors across systems like Slack, Jira, and Salesforce bring siloed knowledge into a unified layer of intelligence. Instead of simply aggregating content, this model enables AI to interpret context, surface the most relevant signals, and continuously refine what information should be amplified for the customer—much like a conductor shaping the performance in real time.
This orchestration becomes even more powerful when combined with intelligent clustering. By grouping patterns across cases, conversations, and knowledge signals, enterprises can uncover what truly matters and act with greater precision. As explored in our perspective on clustering for support teams, this approach enables organizations to not only centralize knowledge, but actively “listen” to it—identifying recurring issues, prioritizing what needs attention, and fine-tuning the overall experience. In this sense, Agentic AI doesn’t just create a single source of truth—it evolves it dynamically, ensuring every interaction is more contextual, relevant, and aligned with customer needs.
On your podcast, you aim to give a voice to finance and support leaders who don't usually get the limelight. Why are these "back-office" departments actually the most fertile ground for the first wave of successful Agentic AI implementation?
I believe the people that we should be highlighting are the ones actually doing the work, not only the leaders who are tasked with evangelizing the results. It’s no more complicated then playing the game of “telephone”, when you talk to the person closer to the beginning of the chain, you’re more likely to get the accurate truth.
SearchUnify Lens:
Peter’s insight about “people closest to the work” underscores where the most accurate, real-time intelligence in an organization resides—within frontline support teams. This is where Agentic AI delivers immediate value. At SearchUnify, our approach focuses on equipping these teams with unified, context-driven knowledge, enabling more consistent and accurate decisions, as explored in our perspective on fixing the limitations of customer support outsourcing.
With solutions like AI SupportPlus Agent, support professionals are guided with relevant insights and workflows directly within their environment—enhancing their judgment rather than replacing it. The result is faster resolutions and more reliable outcomes, driven by the teams closest to the source of truth.
You’ve noted that many executives "talk the talk" on LinkedIn but haven't actually "walked the walk" with AI reality. Based on your advisory work, what is the one internal mindset shift that must happen before a company can successfully deploy a bespoke AI agent?
A mindset shift I would encourage for companies to be successful in deploying a bespoke AI agent is that they need to be overly pragmatic and have an eye towards iterative evolution, rather than biting off more than they can chew. We see influencers and thought leaders telling us that it is possible to automate entire departments and FTEs in 15 minutes, when in reality it isn’t that simple.
SearchUnify Lens:
This perspective on pragmatism and iterative evolution reflects a reality many enterprises are now confronting—successful AI adoption isn’t about sweeping transformation, but about starting with focused, high-impact use cases and scaling from there. At SearchUnify, this philosophy is central to how we help organizations operationalize AI. A strong example is the Celonis journey toward AI-ready self-service, where the foundation was built step by step—unifying knowledge, improving discoverability, and gradually enabling more intelligent, scalable support experiences rather than attempting a one-shot transformation.
This grounded, real-world approach to AI adoption is also echoed in conversations on The Customer Service Show, where industry leaders share how AI strategies actually unfold beyond the hype. The common thread is clear: enterprises that succeed with Agentic AI are the ones that treat it as an evolving capability—continuously learning, refining, and expanding—rather than a one-time deployment.
Looking Ahead
The future of the Agentic Era belongs to the pragmatic, not the loud. As the hype cycle cools, the focus is shifting from sweeping automation to a "symphony" of human-led, AI-orchestrated experiences. Success will be defined by those who can connect the dots between fragmented data and authentic human touchpoints, ensuring that technology serves as a conductor that amplifies—rather than replaces—the human voice.This evolution requires a fundamental shift toward iterative realism. Instead of chasing overnight transformations, organizations must empower the "back-office heroes"—the people closest to the work—to ground AI agents in real-world truth. By balancing agentic autonomy with human accountability, enterprises will move beyond the noise of AI-generated content to build communities and brands that are not only efficient but deeply, irreducibly real.
