Havas’ Ava shows how startups should use LLMs in 2026

Havas just made a very loud point: AI in marketing is no longer a set of side experiments—it’s becoming the operating system. Their new global LLM tool, Ava, is positioned as the “heart of Havas” and is set to roll out this spring.

For UK startups, that matters more than it might seem. Big agencies don’t build secure, unified AI portals because it’s trendy. They do it because it fixes three painful problems at scale: speed, consistency, and control. If you’re trying to hit growth targets with a small team (and you are), those same problems are probably stopping you from turning marketing into a predictable engine.

This post sits in our Technology, Innovation & Digital Economy series because it’s a real-time example of how digital capabilities are becoming competitive advantages—especially in the UK, where startups are expected to do more with less, prove ROI fast, and expand beyond the domestic market earlier.

A good LLM setup doesn’t “write marketing.” It standardises how decisions get made, content gets produced, and insight gets shared across the company.

What Havas’ Ava signals about AI marketing in 2026

Answer first: Ava is a signal that marketing organisations are moving from “using AI tools” to building AI infrastructure—secure, governed, and integrated into everyday workflows.

From the article details available, Ava is described as a global LLM tool that unifies several AI models into one secure portal, with rollout planned for spring. That choice—unifying multiple models—isn’t a technical flex. It’s a strategy: different models perform differently depending on the task (copy drafting, summarising research, analysing tone, ideating variants, multilingual output). A single “front door” makes adoption easier and governance possible.

Why a “secure portal” matters more than the model

Most companies get this wrong. They obsess over which model is “best” and ignore what actually breaks in practice: people paste sensitive data into random tools, outputs contradict brand rules, and nobody can trace where claims came from.

A secure portal approach typically aims to:

• Reduce data leakage risk (client info, commercial plans, customer data)
• Standardise prompts and workflows (repeatable outputs)
• Centralise access (who can use what, and for which tasks)
• Create internal knowledge reuse (winning messages don’t live in someone’s notes)

If a global network thinks it’s worth investing in those controls, startups should assume the market expectation is moving that way too—especially as procurement and compliance scrutiny increases.

The startup version: you don’t need Ava, you need an “AI layer”

Answer first: You don’t need a bespoke enterprise platform to copy Havas. You do need a deliberate AI layer that connects your brand, data, and workflows so marketing output becomes faster and more consistent.

Here’s what I’ve found when startups adopt LLMs successfully: they treat them like process tools, not content toys. The goal isn’t “more posts.” The goal is more throughput per person, with fewer mistakes.

The 4 assets your AI layer should include

1. A Brand Memory Pack

   • Tone of voice (do/don’t examples)
   • Key messages by audience segment
   • Proof points (with sources inside your own docs)
   • Competitor positioning notes

2. A Product & Customer Truth Pack

   • Pricing and packaging rules
   • Feature claims you can and can’t make
   • Common objections and best responses
   • Call recordings or sales notes (sanitised)

3. A Campaign Kit Template

   • Landing page structure
   • Email sequence outline
   • Ad angle matrix (pain × persona × proof)
   • Social variants and repurposing rules

4. A Measurement Prompt Set

   • Weekly performance summary format
   • “What changed and why?” analysis prompts
   • Experiment backlog prompts (next best tests)

The “Havas move” is that Ava likely sits on top of these kinds of internal assets at scale. You can do the same thing with simpler tooling, as long as you’re disciplined.

Practical use cases: where LLMs actually drive growth for startups

Answer first: LLMs create the most value when they compress time in high-frequency marketing tasks: research synthesis, positioning, content variation, and customer communications.

Below are four places UK startups can apply this immediately—without turning marketing into an AI spam factory.

1) Positioning and messaging: faster iteration, fewer zig-zags

Startups waste months in vague messaging cycles: new homepage copy every two weeks, inconsistent decks, different claims from sales and marketing.

Use an LLM to:

• Generate positioning options by segment (SMB vs mid-market vs enterprise)
• Translate features into benefit-led claims with constraints (no exaggeration)
• Produce a message house (one-liners, supporting points, proof)

A simple rule: don’t ask the model what your positioning is. Ask it to propose options, then validate with customer conversations and conversion data.

2) Content production: more variants, tighter quality control

LLMs are excellent at producing structured variation: 10 angles for the same webinar, 5 subject lines for one email, 6 ad headlines that fit character limits.

What works is pairing variation with a quality gate:

• One prompt to generate variants
• One prompt to check compliance with your brand and claims
• One prompt to rewrite for a specific persona

That’s how you get speed without chaos.

3) Customer engagement: support, onboarding, and retention

Marketing doesn’t stop at acquisition. If you’re in B2B SaaS (a lot of UK startups are), expansion revenue and retention depend on communication quality.

LLMs can help with:

• Onboarding emails tailored by use case
• Help centre article drafts based on common tickets
• Churn survey analysis and summarisation

The important part: build a human-in-the-loop step for anything customer-facing until you’ve proven reliability.

4) International scaling: localisation that isn’t just translation

The article frames Ava as global. That’s the point UK startups should steal.

Scaling beyond the UK isn’t mainly a media buying problem. It’s a message-market fit problem. LLMs can support:

• Market-specific objections (e.g., procurement expectations differ)
• Terminology shifts (what “value” means to different buyers)
• Cultural tone adjustments (without losing brand identity)

If you’re planning EU or US moves in 2026, LLM-assisted localisation can cut weeks off your go-to-market prep.

Governance: the part startups skip (and later regret)

Answer first: If you want AI to help you generate leads reliably, you need basic AI governance now—especially around data, claims, and approvals.

Havas emphasising a secure portal hints at the same thing: unmanaged AI usage creates risk. Startups feel immune because they’re small. They’re not.

A lightweight AI policy that won’t slow you down

Put this on one page and actually use it:

• Data rules: what can’t be pasted into AI tools (customer data, contracts, pricing exceptions)
• Claims rules: which product claims require proof links in your internal docs
• Approval rules: which assets need review (paid ads, PR, regulated industries)
• Source rules: when AI summarises research, it must cite the internal source doc

You’re not doing this to be bureaucratic. You’re doing it so marketing can move fast without stepping on a landmine.

A 30-day implementation plan for UK startups (lead-focused)

Answer first: The fastest path is to pick one funnel (one ICP, one offer) and build an LLM workflow that improves speed and conversion quality in measurable ways.

Here’s a pragmatic month-long rollout I’d use for a 5–50 person startup.

Week 1: Build your “truth packs”

• Collect best-performing assets (landing pages, ads, emails)
• Write a single source-of-truth doc for claims and proof
• Define your ICP and top 5 objections

Week 2: Create a campaign kit and prompts

• Build a campaign kit template (LP + emails + ads)
• Create 10 reusable prompts (ideation, rewrite, compliance check)
• Decide who approves what

Week 3: Run one campaign sprint

• Produce variants (ads, emails, landing sections)
• Launch small tests (don’t wait for perfection)
• Use LLM to summarise results weekly in a consistent format

Week 4: Standardise what worked

• Freeze the winning message set
• Turn prompts into a shared library
• Document 3 things you won’t do again (this is where compounding happens)

If your goal is leads, the metric to watch isn’t “content output.” It’s:

• Landing page conversion rate
• Cost per lead (or cost per MQL)
• Sales acceptance rate of leads

Where this is heading in the UK digital economy

Answer first: The UK’s innovation-led growth will increasingly favour startups that treat AI as a capability layer—governed, measurable, and tied to revenue.

Havas launching Ava is another indicator that the market is consolidating around AI-enabled operating models. In 2026, the question for startups isn’t “should we use AI in marketing?” It’s “can we build a repeatable system where AI increases output without weakening trust?”

If you can do that, you don’t just publish faster. You learn faster, ship clearer messages, and build a brand that scales.

So here’s the question I’d sit with this week: If a major agency is building a unified LLM portal as its ‘heart,’ what would you call the ‘heart’ of your marketing system—and is it strong enough to scale?