# SBE Council Technology Use Survey — March 2026

**Status:** provisional research note (session 2026-09-15)  
**Source (opened this session):** https://sbecouncil.org/wp-content/uploads/2026/03/SBE-Technology-Use-Survey-March-2026-Final.pdf  
**Fielded:** February 17–23, 2026 · **n=517** small business owners (2–99 employees) · TechnoMetrica for SBE Council · credibility interval ±4.4 pp

## Why this note exists

Fresh (2026) category-level signal on how US small businesses use AI, marketing, and sales channels — used to pressure-test GeoLocal wedge, entry paths, and later free/silver/gold packaging. **Not** a ranked brand list of local SaaS apps (that remains unavailable from this source).

## Caveat (scope)

Sample is general SMB 2–99 (professional services, construction, tech, retail, etc.) — **not** local-only / brick-and-mortar filtered. Figures may skew differently for salons, bakeries, and similar local genres. Report does **not** mention Google Business Profile, Apple Business Connect, Bing Places, or local SEO by name.

## Key findings (sourced)

### AI adoption

| Finding | Figure (from source) |
| :--- | :--- |
| Use ≥1 AI tool | 82% |
| Median AI tools in use | 5 |
| Confident adopting AI/digital tools | 90% (52% very) |
| AI important to competitiveness/growth (among users) | 81% (41% very, 40% somewhat) |
| Expect major or transformative industry impact | 53% |
| Median annual AI spend | $2,200 |
| Plan to increase AI spend next year | 62% |
| AI in >25% of operations | 32% (8% in >half) |
| Top AI uses | Research 37%, content 30%, marketing automation / project mgmt / social each 25% |
| Primary AI guidance sources | Peers 45%, internal tech staff 38%, associations 37% |
| Top barriers to further AI | Knowledge/training 23%, privacy 17%, cybersecurity 13%, cost 12% |
| Non-users: “see no need” | 62% of non-users |
| Median owner hours saved / week via AI | 5 |
| Median employee hours saved / week via AI | 11.5 |
| Revenue gains attributed to AI | 66% (22% >10% gains) |

### Marketing & sales channels

| Finding | Figure (from source) |
| :--- | :--- |
| Use social media marketing | 64% |
| Rely on word-of-mouth | 58% |
| Email marketing | 42% |
| Paid online advertising | 35% |
| Highest reported ROI channel | Social 34%, then WOM 24% |
| Platform importance (income/branding) | Facebook 62%, Instagram 43%, YouTube 36%, LinkedIn 29%, TikTok 26%, Amazon 24% |
| Median share of sales from digital/e-commerce | 20% |
| Digital platforms make competing with larger firms easier | 74% |
| Sales channel mix | Website 53%, social 51%, brick-and-mortar 37%; median **3** channels |
| Largest revenue source | Own website 30%, social selling 23%, storefront 14%, online marketplaces 13% |
| Majority of revenue from offline (≥51%) | 31% |
| Plan to add sales channels next year | 47% |
| Median annual digital ad spend | $3,290 |
| Plan to increase digital ad spend (12 mo) | 43% |

### Firmographics (context)

| Finding | Figure (from source) |
| :--- | :--- |
| Median employees | 12.2 |
| Median 2025 revenue | $684K |
| Median business age | 8.5 years |
| Location type | Commercial office 38%, home office 27%, retail 7% |

## Implications for GeoLocal (session synthesis — provisional)

1. **Owner-AI entry path is timely.** High AI tool count + confidence supports “owner (or their AI) asks how we get discovered” as a real GTM surface alongside web chat.
2. **Discovery gap.** AI use clusters on research/content/social — not Core 3 listings. GeoLocal’s free checkup fills an unexamined foundation layer.
3. **Peer/WOM GTM.** Peers are the top AI guidance source; WOM is a top marketing channel. Shareable audit proof + partner referral fits the trust path better than vendor-first cold ads.
4. **Spend context for later bundles.** Median AI (~$2.2k/yr) and digital ads (~$3.3k/yr) set a ceiling/context for silver/gold — still pin bundles after value-prop angles are captured.
5. **Hybrid local reality.** Website + offline + multi-channel remain central; Discovery foundation (NAP + Core 3 + hours) sits under that, not instead of it.
6. **Knowledge barrier = free wedge.** Top barrier is training/knowledge; free audit + ranked punch list attacks “I don’t know what to fix” and “AI doesn’t apply to me.”

## Explicit non-findings

- No ranked brand list of local SMB SaaS (QuickBooks, Square, Toast, etc.).
- No Core 3 / Maps / GBP schema or usage metrics.
- Do not treat category % as local-genre gospel without a local-filtered source.

## Related POR

- `docs/BUSINESS_PLAN.md` — north star, wedge, owner workflow, tiers
- Packaging free/silver/gold: **pinned open** until value-prop angles are fully captured