Wolf&Grayson EditorialArtificial intelligence (AI) is rapidly reshaping how commercial organizations operate, but most companies have yet to translate experimentation into measurable growth. For Sadaf Z. Malik, Head of Global Sales for the Biobank and Biomarker Division at Crown Bioscience, the issue is the friction surrounding adoption. “AI should remove friction from revenue generation, not add complexity to the organization,” Sadaf says.
AI must function as core infrastructure, not as an accessory layered on top of existing workflows. In biotech sales, where scientific rigor meets long sales cycles and multi-stakeholder decision-making, friction compounds quickly. Information lives in publications, CRM systems, marketing collateral, SharePoint folders, and slide decks presented months earlier. The effort is immense, but growth slows when decision making lags.
Eliminating Friction in Complex Commercial Ecosystems
Sadaf’s perspective is shaped by leading commercial teams in highly scientific businesses, where business development teams navigate oncology biomarker strategies, biobank assets, and complex translational study designs while building strategic relationships. In that environment, fragmented information becomes the hidden tax on performance.
“The biggest constraint to growth isn’t the amount of effort,” she says. “It’s how long it takes to make decisions.” Reps spend hours assembling data from scientists, marketing teams, CRM notes, and prior meeting records. By the time they enter a client conversation, momentum has already slowed.
Rather than treating AI as a side initiative, Sadaf embedded it into the commercial operating model. Automated meeting preparation now pulls relevant collateral, summarizes capabilities, analyzes Salesforce data, and identifies whitespace opportunities within accounts. Post-meeting tools generate structured follow-ups and clear next steps. Conversations that once felt scattered become focused and intentional, accelerating relationship development.
“AI eliminates research and admin work,” she says. “You’re not automating a relationship. You’re automating the preparation.” The distinction is critical. Strategic dialogue remains human. Preparation becomes intelligent and immediate.
Why Most AI Rollouts Stall
“The technology isn’t the limitation. AI adoption fails because of workflow design and incentives,” Sadaf says. Too often, organizations introduce AI as an optional productivity enhancement. A new platform is announced, training is delivered, and teams are encouraged to explore. High performers experiment quickly, while others revert to habit.
Adoption stalls because the tool sits adjacent to revenue generation instead of inside it. Sadaf advocates embedding AI directly into daily workflows, such as integrating automated insights into meeting platforms or CRM systems so that reviewing AI-generated intelligence becomes part of the call preparation process itself. When the system produces immediate value without additional effort, adoption becomes structural rather than motivational. “If it requires them to do something extra, it’s never going to scale,” she says. Embedding removes choice fatigue. It transforms AI from a novelty into operating infrastructure.
Turning Pilots Into Repeatable Growth Systems
For leaders whose AI pilots never scale, Sadaf outlines three non-negotiables.
- Automate Preparation, Not Relationships: Administrative research consumes a disproportionate share of a representative’s time. Eliminating that burden frees capacity for strategic engagement, trust building, and deeper client insight. Faster preparation leads directly to faster action and more meaningful conversations.
- Embed AI Into Existing Workflows: AI must be embedded within existing commercial systems, not as a separate destination. Switching platforms creates friction and slows adoption. Seamless overlays create momentum and make AI part of how revenue is generated rather than an optional add-on.
- Measure Behavioral Change, Not Just Usage: Login statistics provide comfort but not clarity. Real transformation shows up in faster response rates, shorter time between meetings and next actions, expansion within existing accounts, improved pipeline accuracy, and greater confidence in forecasting.
“Tool usage isn’t enough,” she says. “Are strategic conversations happening earlier because you have greater insights from the get-go?” When insight translates into accelerated decisions, adoption is real.
From Table Stakes to Competitive Moat
Certain AI capabilities will soon become standard across business development teams. Intelligent CRM summaries, automated account research, meeting preparation tools, and lead follow-up generation will stabilize the commercial baseline. The differentiator will be the system built around them. “What matters is the system you build around it,” Sadaf says. “Tools don’t create advantage. The operating system built around them does.” The competitive moat lies in integrating AI with proprietary data assets and customer interaction histories, structuring that data in formats AI can digest, and establishing clear workflows that translate insight into action.
Feedback loops complete the equation. When customer signals are captured, structured, and fed back into the system, organizations accelerate decision cycles. Teams trained to work in partnership with AI at every step create compounded growth that competitors can’t easily replicate.
Advancing Science Through Shared Intelligence
In life sciences, AI’s impact goes far beyond efficiency. Its ability to structure and interpret complex biological data is accelerating how new therapies are developed. Sadaf believes that intelligence must extend beyond internal teams into the broader biotech ecosystem. “As we build AI capabilities, the real opportunity is making that intelligence accessible to the broader biotech ecosystem,” she says. “When large, complex biological data sets can be structured and interpreted faster, it directly accelerates how therapeutics and diagnostics are developed. AI has the potential to compress the timeline of scientific innovation itself.” That shift could ultimately bring new treatments to patients faster.
Concerns about job displacement persist as AI adoption accelerates, but Sadaf sees a different reality emerging. Rather than replacing business development, she believes AI is redefining what high performance looks like. Teams that integrate AI into their daily workflows can move faster, operate with greater insight, and create more value for their partners. “AI isn’t going to replace people,” she says. “But the people who learn to work with AI will replace those who don’t.”
Follow Sadaf Z. Malik on LinkedIn or visit her website for more insights.
