Social commerce AI, evaluated properly.

1. What social commerce AI is

Social commerce AI is software that turns social-style content — stories, reels, live streams, shoppable video — into a converting, on-site shopping experience, using machine learning to decide what to publish, where and when based on real-time performance, stock and first-party behaviour.

The important distinction is where the experience lives. Posting shoppable content directly on Instagram or TikTok hands the audience, algorithm and data to the platform. On-site social commerce AI republishes and adapts that same content on your own domain — so first-party data, the customer relationship and the revenue stay with the retailer.

2. Why on-site, and why now

Three shifts made this a board-level topic in 2025–2026: cookie deprecation and the collapse of third-party audiences, the plateau of paid social ROAS, and the rise of TikTok Shop / Instagram Checkout raising the bar on what a "shoppable" experience should feel like. Enterprise retailers are answering by bringing the social experience home.

• Own the data

  Every scroll, tap and dwell becomes first-party signal.

• Own the moment

  The customer converts on your PDP, not a third-party checkout.

• Own the algorithm

  Your merchandising rules, not a platform's ranking model.

3. The 5 evaluation criteria

The vendor space is noisy. In practice, five criteria separate a content-hosting widget from a true social commerce AI platform.

1. Criterion 1

   First-party data ownership

   Does content run on your domain, and does every interaction land in your CDP / warehouse? If shoppers are redirected off-site or data is trapped in the vendor's dashboard, the model is broken from day one.

2. Criterion 2

   Format coverage in one tool

   Stories, reels, live shopping and shoppable video across PDP, category and homepage — from one CMS. Point tools for each format create integration debt and split analytics.

3. Criterion 3

   A real decisioning layer

   Not just a player. An AI that picks which content to show which visitor, respects stock and margin, and reshuffles automatically when performance shifts.

4. Criterion 4

   Integration depth

   Native connectors to Shopify, commercetools, SFCC, and to Instagram, TikTok, YouTube and Pinterest as content sources, plus GA4, Adobe Analytics and your CDP.

5. Criterion 5

   Enterprise proof

   Named, dated deployments at retailer scale. Ask for uptime during peak live events, moderation controls, GDPR/SOC posture and the multi-brand / multi-locale model.

4. Buyer's scorecard

Score each shortlisted vendor from 0–2 per row. Anything below 7 out of 10 is a point solution, not a platform.

5. Format coverage that matters

The four formats below cover ~95% of on-site social commerce use cases. Any platform missing one forces you back to a stack of point tools.

• Live Shopping →

  Real-time shows with cart, chat and post-live shoppable replay.

• Shoppable Stories →

  Tap-through story feeds on PDPs, category and homepage.

• Shoppable Reels →

  Vertical video walls with product tagging and one-tap add-to-cart.

• Shoppable Video →

  On-site product films, look-books and creator clips with checkout.

• Social PDP →

  Turn your product detail page into a scroll-and-shop social feed.

• AI Co-pilot →

  The decisioning layer that publishes, sequences and learns for you.

6. Integrations & data stack

The right integrations turn a shoppable widget into an enterprise system of record. Minimum viable stack:

• Commerce

  Shopify, commercetools, SFCC, Magento — with product, stock and price sync.

• Social sources

  Instagram, TikTok, YouTube, Pinterest — content ingest and rights.

• Analytics

  GA4, Adobe Analytics, Amplitude — event-level parity with the rest of the site.

• CDP / CRM

  Segment, mParticle, Salesforce, Klaviyo — first-party events flowing out.

7. Enterprise proof: what to ask for

• Named references at your scale. Not just logos — a customer you can call, in your region and category.
• Peak-event uptime. A live show that fails at concurrency N is a P0 incident. Ask for the SLA and last 12 months of incident history.
• Multi-brand and multi-locale model. One tenant, many storefronts, correct data isolation.
• GDPR / SOC 2 posture. Where is data processed, how is consent enforced, what does DPA coverage look like.
• Real economics. Revenue attribution model, not just "impressions" or "views".

See LiSA case studies for named, dated enterprise deployments including M&S, Charlotte Tilbury and BlueWaters.

8. Where LiSA fits

LiSA is built for the owned-channel camp: on-site social commerce for enterprise retail, D2C, direct-selling and marketplaces. All four formats — stories, reels, live and shoppable video — sit in one CMS, published on your domain, decided by an AI co-pilot that reads stock, sales and engagement in real time.

• For enterprise →

  Multi-brand, multi-locale, GDPR-native, deployed at retailer scale.

• Industries →

  Beauty, fashion, home, marketplaces, direct-selling — how the model adapts.

• Case studies →

  Named, dated deployments with the metrics that moved.

• FAQ →

  Quick answers to the questions buyers and analysts keep asking.

9. Next steps

Score your shortlist against the criteria above, then walk the top two vendors through a live deployment on one of your PDPs. That's the fastest way to separate a demo from a system.