Daniel Saks: Advocating for Digital Trust in AI-Driven Go-to-Market Platforms

Artificial intelligence (AI) has flooded B2B Go-To-Market (GTM) channels with automation, personalization, and scale. For Daniel Saks, CEO and co-founder of Landbase, none of it matters without trust. “Businesses want to buy from people they trust,” he says. “And in the age of AI, it matters even more because there’s so much noise.”

After helping build AppDirect into a global subscription commerce platform serving millions of businesses, Saks has spent the past several years focused in the belief that digital trust is now the primary currency of B2B growth. As AI accelerates outbound campaigns and agent-driven workflows, the companies that win will be those that can measure, build, and protect that trust at scale.

Trust as the First Gate to Growth

Saks’ perspective is rooted in experience working with Fortune 500 executives and overseeing billions in gross merchandise value. Technology may evolve, but buying behavior remains remarkably consistent. “Business adoption or procurement is only as good as people trusting the brand,” he says.

Consumer users might experiment freely with new tools but that is not the case for enterprise buyers. They vet vendors, review reputations, and look for third-party validation before agreeing to a meeting. In crowded digital markets, differentiation increasingly hinges on credibility signals that exist long before a salesperson makes contact.

At Landbase, an early-stage company building an agentic AI platform for GTM, Saks and his team have focused on quantifying those signals. He describes them as “digital cookie crumbs” scattered across the internet: sentiment in media coverage, analyst commentary, Glassdoor and G2 reviews, LinkedIn conversations, published research, and peer endorsements. “To earn a meeting, you need to make sure you have digital trust,” he says. “And then once you have that meeting, you need to showcase that you have relationship trust as well.”

The End of Spray and Pray

After analyzing millions of campaigns, Saks found that the digital trust of the seller and the relevance of the offer matter more than clever copy or channel experimentation. “If you’re an unknown person at an unknown company, you practically have no chance of succeeding,” he says.

Mass outreach, once tolerated, has become counterproductive. AI-generated spam has overwhelmed inboxes, phone lines, and LinkedIn feeds. Generic messages sent to massive lists trigger spam flags and permanently damage sender reputation. “The spray and pray method of outbound is dead,” says Saks.

What replaces it is precision. The right offer must be delivered to the right buyer at the right moment, supported by a credible personal and company brand. Targeting, Saks argues, now outweighs messaging. Hyper-specific lists, built around meaningful buying signals, outperform large databases filtered only by company size or geography.

Building Signal-Driven Micro Campaigns

Saks outlines three concrete practices for trustworthy AI-driven outbound. First, sharpen the offer. “Really focus on the why,” he says. Buyers must immediately understand why the solution matters to them.

Second, identify intent signals that indicate a prospect is actively in market. A recent funding round, public commentary about a related product, or a strategic shift can signal readiness. These contextual cues outperform broad demographic filters.

Third, launch micro campaigns instead of mass blasts. Rather than sending 10,000 emails, start with a highly curated group of 50 or 100 contacts aligned around a specific signal. Measure reply rates from that small cohort before scaling. “Instead of it being about did I send 10,000 emails in a week, it should be about the small campaigns that I sent, which ones had the best success, and pick that one and scale it,” Saks says.

This shift requires more sophisticated workflows, traditionally reserved for data scientists or specialized GTM engineers. Saks believes AI should simplify, not complicate, that process. By enabling sales and RevOps teams to build signal-driven lists through natural language interfaces, complex segmentation becomes accessible without deep technical expertise.

Preparing For Agentic Commerce

Looking ahead 12 to 24 months, Saks expects outbound to become highly effective again, but only for organizations that adopt this disciplined approach. Strong brands, relevant offers, and precise targeting will define success. Legacy tactics will continue to fail.

As AI agents increasingly negotiate, provision services, optimize usage, and even dispute invoices, new governance models will be required. Saks argues that leaders must establish clear agent operating procedures and human-in-the-loop controls. Agents, like employees, need defined boundaries. “You need the right escalation or human in the loop to make sure that it’s being done in an accurate and fair way,” he says.

The broader message is not about automation for its own sake. AI should amplify credibility, not erode it. Companies that treat digital trust as a measurable asset and embed it into targeting, pricing, and workflow design will create durable competitive advantage. Those that chase scale without relevance risk becoming part of the noise they are trying to overcome. For Saks, the path forward is clear. Trust must be engineered into the system from the first touchpoint to the final contract.

Follow Daniel Saks on LinkedIn or visit his website for more insights.