AI Partnerships: What Singapore Businesses Should Copy

Pharma companies don’t adopt AI the way most businesses do.

They don’t start with a “trial tool” and hope for the best. They integrate AI into the systems their teams already use, then measure whether decisions get faster and outcomes get better.

That’s why the Jan 2026 news about Schrodinger integrating Eli Lilly’s AI drug discovery platform (TuneLab) into Schrodinger’s LiveDesign matters—even if you’re running a Singapore SME, a regional marketing team, or an ops-heavy services business. It’s a clean example of AI integration done the practical way: put a specialised model where the work already happens, reduce friction, improve the feedback loop, and scale usage through a partnership.

This post is part of our AI Business Tools Singapore series, focused on how Singapore companies can use AI for marketing, operations, and customer engagement without turning it into a science project.

The real story: integration beats “more AI tools”

The headline is about drug discovery, but the lesson is about adoption. Schrodinger will offer Lilly’s TuneLab inside LiveDesign, a platform chemists already use to design compounds and predict properties (like absorption and distribution). The value isn’t “AI exists.” The value is AI appears inside the workflow.

Here’s what the Reuters report (via CNA) makes clear:

• TuneLab is Lilly’s AI/ML platform trained on years of its R&D data.
• LiveDesign is Schrodinger’s cloud platform used by chemists to design and evaluate molecules.
• The integration gives biotech teams direct access to TuneLab inside LiveDesign.
• Availability is staged: existing clients can use it in Q1 2026, new users in Q2 2026.

This staged rollout is a tell. In regulated, high-cost environments, teams don’t tolerate chaos. They want a path from controlled access → wider deployment.

Singapore business translation: if your team has to switch tabs, copy-paste data, or “log into another tool” to use AI, adoption will stall. Integrate AI into your CRM, helpdesk, ERP, marketing automation, or even Google Workspace/Microsoft 365 flows.

A simple definition worth stealing

AI integration is when AI outputs show up at the moment a decision is made—inside the software people already open every day.

If it’s not there, it’s not used.

Why pharma is obsessed with speed (and why you should be too)

Drug development is famously expensive and slow. The article highlights a driver: drug developers are increasing AI use in discovery and safety testing to get faster and cheaper results, aligned with an FDA push to reduce animal testing.

You may not be dealing with clinical trials, but your business still has “discovery” stages:

• Finding which leads are worth calling
• Identifying why customers churn
• Predicting demand and staffing
• Detecting fraud or invoice anomalies
• Drafting compliant customer replies

In every case, time-to-decision is the compounding advantage. If your sales team gets a strong lead score today instead of next week, you win more deals. If your ops team spots exceptions in hours instead of days, you reduce cost and customer pain.

Here’s the stance I take after watching many AI rollouts: speed is only valuable when it’s paired with trust. Pharma gets this because the cost of a wrong prediction is massive.

Singapore business translation: don’t chase “full automation” first. Start with decision support (recommendations, summaries, prioritisation) and build trust through measurable accuracy.

Partnership models: the fastest route to serious AI

Lilly launched TuneLab to provide biotech companies access to models trained on years of internal data. Then it partnered—because more usage means more varied training data, which improves models over time.

That same partnership logic is showing up in business AI tools:

• Tool vendors embed models into existing platforms
• Platforms partner to share data safely (or share capabilities without sharing raw data)
• Customers benefit because the integration reduces implementation time

For Singapore companies, the most practical approach is often buy + integrate + govern, not “build from scratch.” Building can make sense for a narrow, defensible advantage, but most SMEs and mid-market firms will move faster by partnering with vendors.

What to copy: the “platform inside a platform” play

Schrodinger didn’t ask customers to adopt TuneLab as a separate workflow. It’s offered within LiveDesign.

You can copy this in two ways:

1. Embed AI into your core system (e.g., CRM or helpdesk)
   • Lead scoring appears in Salesforce/HubSpot
   • Reply suggestions appear in Zendesk/Freshdesk
   • Call summaries appear in your dialer/VoIP notes

1. Use an integration layer when your stack is messy
   • A lightweight automation layer (workflows, webhooks, APIs)
   • A governed internal “AI assistant” connected to approved data sources

If your current AI plan is “we’ll give everyone a chatbot,” you’re likely to get a burst of curiosity—and then silence.

Operational lessons Singapore teams can apply this quarter

The CNA/Reuters piece includes the most important operational detail: timelines and rollout phases. Existing users get access first (Q1), then new users (Q2). That’s a playbook.

Step 1: Pick one workflow where AI removes a bottleneck

A good first workflow has three traits:

• High volume (daily or weekly)
• Clear “good vs bad” outcomes
• Data already exists in your systems

Examples I’ve seen work well for Singapore businesses:

• Sales: prioritise inbound leads using firmographics + behaviour
• Customer support: auto-triage tickets and draft first responses
• Finance ops: flag unusual invoices/claims for review
• Marketing: generate variant ad copy and landing page sections (with brand rules)

Step 2: Put AI where people already click

This is the Schrodinger-Lilly lesson in plain terms. If your team must leave their primary tool, adoption dies.

Practical implementations:

• AI button inside the ticket view (“Summarise”, “Suggest reply”, “Extract customer intent”)
• AI inside CRM opportunity view (“Risk factors”, “Next best action”, “Competitors mentioned”)
• AI inside shared drive docs (“Summarise policy”, “Draft SOP from bullets”)

Step 3: Measure with a scoreboard, not vibes

If you want leads (and results), measurement can’t be optional. Track:

• Cycle time: time from request → decision
• Quality: error rate, CSAT impact, rework rate
• Adoption: % of users using AI weekly
• Cost: minutes saved translated into dollars, or tickets per agent

A simple, extractable metric that works across functions is:

If AI doesn’t reduce cycle time by 20–30% in a defined workflow, it’s not integrated well enough.

Step 4: Create “guardrails” that don’t slow people down

Pharma is regulated, so it’s forced to build guardrails. Singapore businesses should do the same, especially with customer-facing AI.

Minimum viable guardrails:

• Approved data sources only (no copy-pasting sensitive data into random tools)
• Clear human approval steps for outbound messages
• Logging of prompts/outputs for audits and improvements
• Brand and compliance rules baked into templates

This is where many teams get stuck. They either over-control (nobody uses it) or under-control (someone pastes a customer NRIC into an AI tool). The right answer is light governance plus good defaults.

People also ask: “Does AI replace expertise?”

No—and the Lilly quote in the article makes the direction clear: it’s about moving molecules faster for patients.

In business terms: AI doesn’t replace your best sales rep, ops lead, or customer success manager. It replaces the dead time:

• searching,
• reformatting,
• summarising,
• and repeating.

The expert still decides. AI just makes the decision cheaper and faster.

People also ask: “Should we build our own model?”

Most Singapore businesses shouldn’t start there.

Build when you have:

• proprietary data that competitors can’t access,
• a narrow use case worth defending,
• and internal capability to maintain models and governance.

Otherwise, partner and integrate. Lilly is essentially productising years of internal knowledge. You can benefit from that pattern by choosing vendors whose tools improve as they see more real-world usage (while still meeting your data and security requirements).

What to do next if you’re serious about AI business tools in Singapore

The Schrodinger–Eli Lilly integration is a reminder that AI adoption is mostly an integration problem, not an “AI capability” problem. When the model is placed inside a workflow, teams use it. When it’s bolted on, they don’t.

If you’re planning your 2026 roadmap, start with one workflow and one integration path. Get a measurable win within 6–8 weeks, then expand to the next workflow using the same governance and measurement approach.

The question I’d leave you with is simple: where in your business do decisions get stuck because information is scattered—and what would change if the answer appeared inside the tool your team already uses?

Source article: https://www.channelnewsasia.com/business/schrodinger-offer-eli-lillys-ai-drug-discovery-platform-its-software-5848826