Memo to My Readers: Putting AI to Work Inside the Data Science Function

Dear Readers,

AI hype is everywhere, yet many organizations still struggle to decide where these capabilities should sit and how to turn them into tangible value. After years of building and advising data-driven teams, here’s the structure I’ve found works and the pitfalls to avoid.

1. See the Stack, Not the Buzzwords

AI is just the newest layer on a technology ladder that starts with solid data plumbing. No data infrastructure, no analytics. No analytics, no machine learning. No machine learning, no AI. Keep that hierarchy front-of-mind and the “mystery” disappears.

2. Separate the Three Core Missions

Treat data work as three distinct, complementary capabilities, each with its own culture, deliverables, and success metrics:

Capability	Mission	Typical Home	Examples
Business Intelligence	Answer questions that guide daily decisions	CFO / COO	Revenue diagnostics, unit-cost tracking
Product Data Science	Build algorithms that enrich the product	Engineering / Product	Recommendations, fraud filters, search ranking
R&D / Advanced Analytics	Explore moon-shots that open new markets	Innovation / CTO	Deep-learning prototypes, new data businesses

Assume overlap at your peril: a killer churn model won’t spring from a BI team stuck producing monthly decks.

3. Budget for Experimentation And Failure

Software projects start with a spec and finish with working code. Data science starts with a hypothesis and sometimes ends in a dead end. Don’t shove scientists into the sprint rituals of pure software; give them lab time, guardrails, and a failure budget. The wins will more than pay for the losses.

4. Fix Process Before You Chase Tools

If your experimentation pipeline mirrors traditional agile, you’ll suffocate creativity. Instead, install a research path (rapid exploratory work, disposable code) and a production path (hardened, test-covered services). Moving proofs of concept across that bridge is a deliberate, documented transition and treat it that way.

5. Beware Cultural Trip-Wires

Teams get punished when research fails, so they stop proposing risky ideas. Counter that reflex. Reward well-designed experiments regardless of outcome; penalize only sloppy method. Otherwise the bold work you hired scientists for never surfaces.

6. Challenge the Black-Box Sales Pitch

If a vendor flashes the “It’s AI with keyword 'trust us'” card, keep probing: What data feeds it? What latent patterns might bias outputs? How does feedback loop into retraining? If they can’t or won’t answer plainly, walk away imediately.

Closing Thought

Deploying AI is less about mastering exotic models than about designing an environment where counting, predicting, and innovating coexist without tripping one another. Get the foundations right, carve out space to explore, and insist on transparent methods. Do that, and “AI” becomes just another tool. Powerful? yes, but firmly under your team’s control.

Cheers,

Haluan