AI in Practice: From Earnings Strategy to Technical Execution | AlphaSense

Earnings season demands significant bandwidth: monitoring a large group of companies, tracking KPIs against specific metrics, assessing analyst reactions, and pulling all of it into a defensible narrative for leadership. When the tools are fragmented and the work is manual, the inefficiency compounds fast. Edric Lin (Lead, Corporate Finance APAC at AlphaSense) and Yu Chen Han (Corporate Finance, APAC at AlphaSense) run a live working session for CFO, IR, FP&A, and corporate strategy teams across APAC and EMEA on how to move from earnings strategy to technical execution using AlphaSense. The demo is structured around 3 workflows: - Post-earnings strategy read-through: a live NVIDIA example showing how to synthesize what changed in strategy, guidance, and macro context across broker research, filings, and earnings calls, with sentence-level citations - Peer benchmarking with Generative Grid: comparing dozens of earnings calls in a single pane of glass, with customizable columns for growth outlook, R&D focus, geographic expansion, AI strategy, and even avoidance language - Continuous monitoring with workflow agents: pre-built agents for Analyst Q&A Thematics, Analyst Q&A Deep Dive, Earnings Narrative Planner, Anticipatory Q&A Generator, Bull vs Bear Debates, and Faint Signal Detection, plus a walkthrough of building custom scheduled agents that push updates to your inbox 00:00 Welcome and Introductions 00:44 Meet the Panel: Edric Lin and Yu Chen Han 02:50 Quick AlphaSense Overview 04:49 Setting the Stage: Closing the How-To Gap in Earnings Season 05:27 The 3 Workflows for Earnings Season 06:36 Workflow 1: Post-Earnings Strategy Read-Through on NVIDIA 09:32 The Best-of-Breed Multi-Model Approach 10:28 Reading the Output and Understanding Citation Colors 12:59 Verify Button and the 2% Hallucination Rate 13:24 Turning the Output Into a PowerPoint Deliverable 13:56 Q&A: Downloading Reports and Comparing Companies 14:55 Workflow 2: Generative Grid for Peer Benchmarking 17:31 Customizing Columns: Avoidance Language as an Example 20:16 Q&A: Building a Watchlist Calendar for Earnings Season 21:47 Q&A: Downloading Broker Research 22:31 Q&A: Manually Adding Companies to the Grid 23:53 Q&A: Can You Pre-Populate the Grid Before Earnings? 25:15 Q&A: Building Custom Columns Like AI Strategy 28:09 Q&A: Integrating Internal Emails, Memos, and Projections 29:20 Q&A: Working With Proprietary Data 30:01 Q&A: Summarizing the Entire Grid 31:03 Q&A: Broker Report Embargo Periods 32:20 Workflow 3: Pre-Earnings Prep and Workflow Agents 33:16 Analyst Q&A Thematics and Earnings Narrative Planner 36:31 Bull vs Bear Debates and Faint Signal Detection 37:38 Q&A: Multiple Documents per Row and Workspaces 39:47 Q&A: Multi-Language Coverage Including Chinese 42:15 Building Custom Scheduled Agents 44:43 Reactive vs Proactive Monitoring 47:27 Roadmap: Super Analyst 49:34 Closing: Decision-Grade Intelligence Closing with a preview of Super Analyst, AlphaSense's always-on AI capability with persistent memory, and Edric's take on why the future of AI in enterprise belongs to decision-grade intelligence, not just more output. For CFOs, IR leaders, FP&A directors, and corporate strategy teams looking to compress earnings season without giving up defensibility.