Artificial intelligence has become the headline act in business technology. Every platform promises automation. Every vendor claims productivity gains. Every go-to-market (GTM) stack now includes some form of generative capability. Yet beneath the noise sits a quieter structural problem that AI alone cannot fix.
Data inefficiency.
In most revenue organizations, data flows through cracked systems. It duplicates. It decays. It fragments across tools. And when companies layer AI on top of that fragile foundation, they often accelerate confusion rather than clarity.
Yoni Tserruya, CEO and co-founder of Lusha, approaches the AI conversation from a systems perspective. “AI delivers meaningful results when the foundation beneath it is strong. When data is accurate, connected, and prioritized, automation drives impact. Without that foundation, activity increases, but outcomes do not.”
The issue is not a lack of information. It is an overabundance of unmanaged, disconnected, and unreliable data. Efficiency is not about having more records. It is about knowing which information matters, when it matters, and how it connects directly to action.
The decay problem
Business data changes constantly. Job titles shift. Decision makers move roles. Companies raise funding or restructure. Technology stacks evolve. Entire buying committees reorganize within months.
Estimates suggest that roughly a third of B2B data decays annually. That means nearly one in three records becomes unreliable each year. The impact is measurable. Routing logic fails. Lead scoring models weaken. Outreach misses timing. Forecasts drift from reality.
Revenue operations teams often spend significant time reconciling records instead of optimizing performance. Sales professionals dedicate hours to validating contacts before even initiating conversations. Marketing teams struggle to align targeting with the current ground truth.
This is not simply a data hygiene problem. It is a structural inefficiency embedded in the revenue engine.
Efficiency is not automation
There is a growing assumption that automation automatically creates efficiency. In practice, efficiency begins with clarity and prioritization. Generative tools can produce thousands of emails in seconds. Growth comes from identifying which conversations are relevant and timely.
Tserruya emphasizes precision over volume. “The breakthrough is understanding who to engage, when to engage them, and why the timing matters. That is where data creates advantage. Automation then becomes focused and intentional rather than broad and reactive.”
Automation handles execution. Efficiency requires prioritization. Prediction must precede production.
Organizations that fail to separate these layers risk building faster pipelines that move in the wrong direction. Volume increases. Relevance declines. Noise compounds.
From raw data to guided action
Data efficiency can be understood as progression. Raw data is unstructured and duplicated. Information explains what has happened. Knowledge interprets patterns. Recommendations guide what should happen next.
Many teams remain trapped at the informational layer. They possess dashboards and exports but lack direction. The missing component is fusion.
Lusha’s approach is built around what it describes as a fused data layer. Public and social data signals, combined with Lusha’s verified global database and each customer’s internal CRM data, create a unified decision framework. Instead of exporting static lists, teams work with continuously updated datasets that reflect real-world change.
When these layers operate independently, inefficiency persists. When they are fused into a single system, prioritization becomes dynamic. Accounts are ranked by intent and fit. Contacts are enriched in context. Signals trigger action without manual intervention.
In practical terms, this means revenue teams move from searching for contacts to receiving continuously refreshed recommendations. Records update automatically. Signals trigger enrichment. Routing occurs in real time.
Data becomes active rather than archival.
Trust as a performance lever
Efficiency is inseparable from trust. In regulated environments, data cannot simply be abundant. It must be compliant, secure, and verifiable.
Lusha has positioned compliance and verification as core infrastructure rather than an afterthought, with alignment with GDPR, CCPA, and ISO certifications embedded into its data operations. That focus reflects a broader principle. Efficiency collapses when teams doubt the accuracy or legitimacy of the information in front of them.
Verified data layers create operational confidence. When teams trust the foundation, they move faster. Decision cycles compress. Execution becomes consistent.
In this context, efficiency is not only about speed. It is about reducing friction caused by uncertainty.
Efficiency across the stack
Data inefficiency rarely lives in one system. It spreads across the entire GTM stack. CRM platforms, enrichment providers, engagement tools, marketing automation systems, and analytics dashboards all depend on consistent records.
Disconnected tools create latency. Manual workflows introduce bottlenecks. Routing delays reduce conversion probability. Each additional reconciliation step slows execution.
Lusha’s platform model reflects this reality. Its browser extension supports frontline sellers, its API enables developers and RevOps teams to embed verified data directly into workflows, and its evolving Workspace environment connects enrichment, alerts, and automation into a single collaborative layer. The objective is not to replace the stack, but to serve as a consistent data backbone across it.
When enrichment, scoring, and routing operate automatically across systems, the impact compounds. Inbound forms convert into complete records instantly. Buying signals trigger contextual alerts. Accounts are prioritized without manual intervention.
The objective is not to eliminate people from the process. It is to eliminate unnecessary friction so that human effort can focus on high-value interactions.
The human impact
AI-driven efficiency reshapes the role of the salesperson and the operator. If machines handle validation, enrichment, prioritization, and workflow triggers, humans can concentrate on empathy, negotiation, and strategic engagement.
Tserruya argues that AI will raise the bar rather than lower it. Automation removes repetitive tasks. It does not replace relationship building. In fact, by reducing noise and surfacing true opportunity, it amplifies the value of human skill.
Efficiency, in this sense, is liberating. It reclaims time. It restores focus.
Real Infrastructure
The future of AI-driven growth will be determined by who builds the most reliable data infrastructure.
Language models are widely accessible. Predictive models are increasingly commoditized. The differentiator becomes the quality and integration of the data layer beneath them.
Organizations that treat data as infrastructure rather than output create compounding advantages. Better predictions lead to better prioritization. Better prioritization leads to higher conversion rates. Higher conversion rates reinforce the data loop.
In an era obsessed with automation, the quiet advantage belongs to those who solve for efficiency first.
AI may be the engine. Data efficiency is the steering wheel.
Without it, acceleration only increases the likelihood of drift. With it, automation becomes focused, predictive, and transformative rather than cosmetic.
The companies that understand this distinction will not simply move faster. They will move smarter.
