In early 2025, a boardroom of an Italian SME in the Northeast. Four people: CEO, CFO, IT head, marketing. The agenda item is "define the company's AI strategy". On the table is a stack of printed newsletters, a consulting firm's 80-page report, two quotes from different vendors, and the weekend edition of Sole 24 Ore with a full-page headline on generative AI.

The meeting lasts three hours. They come out with a decision: "evaluate over the next six months". In June 2025 a plan is presented. In September 2025 it's postponed. In January 2026 it's resurrected. In May 2026 a budget is approved. In September 2026 — now — implementation will begin.

Seventeen months.

Yes, the company is a real case. No, we can't name it. But it's exactly *the average Italian company* — among the SMEs that did something on AI in 2024-2026.

This piece is a long-form review. Eighteen months of generative AI applied to Italian companies, distilled into what we've seen work, what turned out to be hidden cost, and where it's worth investing in the next 12 months. It's deliberately long. It's deliberately frank. It's deliberately written **to be re-readable at Christmas** — because many things said here might already be outdated, and those that won't be will be our shared compass for 2027.

## Three phases, one trajectory

In 18 months, AI in business went through three clear phases.

**Phase 1 — The promise (January 2024 — June 2025).** Everyone wants to do AI. Nobody knows exactly what to do with it. Budgets inflate, quotes proliferate, "ChatGPT for managers" courses multiply. AI strategy is a statement of intent: *"we want to be AI-first"*. Underneath, little concrete.

**Phase 2 — The disappointment (July 2025 — December 2025).** First pilots go badly or go *less well than expected*. Companies discover that integrating an LLM into their operations is more complex than the demo suggested. CMOs who pushed AI projects come under pressure from the CFO. Some initiatives get frozen, others reduced. *"AI deception"* becomes common talk.

**Phase 3 — Discipline (January 2026 — today).** Companies that survived phase 2 stopped asking "should we do AI?" and started asking "*for which specific problem is AI the best solution?*". Pilots become smaller, more targeted, more measurable. The vocabulary refines: *generative AI*, *agentic AI*, *RAG*, *context engineering*, *multimodal* are distinguished. Decisions are made with discipline.

The three phases are not over. They coexist. Every new company entering the AI market lives the three phases *in compressed scale*: from promise to discipline in a few months if lucky, in two years if less attentive. But the trajectory is clear, and it's that one.

## What really worked (in Italian SMEs)

We give you the real cases, anonymized but recognizable as patterns. They are the AI applications where we saw **verified ROI** in Italian SMEs.

**1. AI-assisted (not replaced) customer service.** For companies with recurring weekly ticket volumes (services, e-commerce, software), an AI assistant that *prepares* human responses (doesn't send them directly) reduces average handling time by 40-60%. It works because it preserves human responsibility, but eliminates repetitive typing. Investment: medium. Time to return: 3-6 months.

**2. Internal data research and analysis.** Companies with lots of internal documentation (legal, consulting, manufacturers with technical manuals) found great value in RAG systems (see [the hardword piece on RAG](/blog/hardwords-rag)) that allow querying the archive in natural language. It works because it solves a real problem — *"I have a specific question and the answer is in one of our 4,000 documents"*. Investment: medium. Time to return: 6-9 months.

**3. Creative variant generation.** Marketing teams managing many campaigns used AI to generate copy and creative ad variants, accelerating testing. Doesn't replace the creative. *Amplifies them.* Works very well for ambient marketing, performance marketing, social ads. Investment: low. Time to return: 1-3 months.

**4. Accelerated software development (with rules).** As written in the [July 23 vibe coding piece](/blog/vibe-coding-in-production), internal development teams got +50-80% release speed. *With governance.* Investment: medium-high (in training and processes). Time to return: 6 months.

**5. Complex document audit.** Professional firms and consultancies are using multimodal AI for first passes on balance sheets, contracts, technical sheets. Human review remains indispensable, but initial "decoding" time collapses. Works very well for accounting firms, mid-sized law firms, tax consultants. Investment: low. Time to return: 3 months.

## What was hidden cost

Now the part less willingly told. Three things that, in many SMEs, turned out to be expensive without anyone noticing first.

**1. Time spent "evaluating vendors".** Italian companies above a certain size spent dozens of half-days of the CFO, IT head, marketing, in meetings with AI vendors in 2024-2025. The vast majority of these meetings produced no decisions. Submerged opportunity cost: easily 30-50 thousand euros of managerial time per average company. On a thousand Italian SMEs, we're talking about **30-50 million euros a year** of managerial time burned on evaluations. Few notice because it's not in any cost center.

**2. Overlapping "tool stacks".** Many companies today pay simultaneously: ChatGPT Enterprise licenses, Claude Team licenses, Copilot licenses for Microsoft 365, and one or two vertical tools (for chatbots, for analysis, for coding). Aggregate cost: often 30-60 thousand euros a year for a 50-employee company. Effective usage: ridiculously low. Rationalizing the tool stack is one of the most profitable exercises of Q4 2026.

**3. Late-managed compliance.** As written in [the June 25 piece on AI Act phase 2](/blog/ai-act-phase-2), many companies discovered *after* putting AI systems into production that they had to document, evaluate, and supervise. The cost of doing it *after* is triple compared to doing it *before*. Italian SMEs that structured AI governance practices already in 2025 are saving money and time in 2026.

## The compass for 2027

Let's come to the prediction part. Four trends we see consolidating, and which you should budget for in the next 12 months.

### Trend 1 — AI becomes "vertical", not "horizontal"

For the first two years of generative AI in business, the dominant tools were horizontal: ChatGPT, Claude, Gemini, Copilot. Tools that do *many things mediocrely well*.

In 2027 value shifts to *vertical* tools. AI systems built specifically for: accounting, legal management, manufacturing, healthcare, consulting, education. They work *much better* for the specific case — because they're designed around it — and in return *they only work for that*.

**What to do:** evaluate what you really need, and look for the *vertical* vendor that solves it. Stop thinking "ChatGPT is the universal base".

### Trend 2 — AI agents start replacing workflows (not people)

Agentic AI — systems that execute chains of actions autonomously — is moving from promise to operability in the next 12 months. But the way it replaces people is different from what everyone thought.

It doesn't replace customer service. It replaces *the customer service workflow* — the three different tools the operator uses to handle a case. The operator remains, but their work environment simplifies radically.

**What to do:** identify the most *friction-filled* workflows in your company (those requiring passages between many tools, many hands, many waits). Agents will enter there. Not elsewhere.

### Trend 3 — "AI-native" become a new segment

In 2027 we'll see companies emerge that are **AI-native from birth**: organizations built with the assumption that AI is a constitutive part of the process, not an addition. It's what we're trying to do with [OpificioAI](https://opificio.ai), Tuken, BP, BI, and Blogger. We don't do it for fashion — we do it because we understood, in these 18 months, that AI-native companies *operate in a structurally different way*. They have smaller teams, faster decisions, products that scale with almost zero marginal cost.

**What to do:** if you're planning a new business unit, evaluate building it AI-native from the start. On existing products it's difficult to retrofit. On new ones it's the right move.

### Trend 4 — Trust becomes the real asset

In 2026 we saw a strange phenomenon: some technically *worse* AI vendors won contracts against technically *better* vendors. Why? Because they knew how to build *trust*. Data transparency, cost clarity, ready compliance documentation, open audit processes.

In 2027 this trend will amplify. Companies will increasingly buy AI from vendors who can **demonstrate**, not just **declare**, to be reliable. Trust becomes part of the product.

**What to do:** if you're an AI vendor, invest in transparency and documentation now. If you're a customer, choose vendors based on transparency, not just performance.

## Five operational tips for the next quarter

To close, five concrete actions for Q4 2026. All doable in 30-90 days.

1. **AI tool stack audit.** How many tools do you pay for? How used are they? What can you consolidate? Typically: -30% annual costs, +20% clarity.

2. **Audit of AI systems in production.** How many are in "high risk" tier per the AI Act? For each, is there a compliance plan? Involve legal. It's done in half a day, avoids millions of problems.

3. **Define the product metric, not the agent.** For each AI system in the company, what's the *business* metric telling you if it's working? If you don't have it, build it. A clear metric beats a thousand engineering dashboards.

4. **Invest in governance before the next tool.** House rules (see [the May 28 piece on the 4 recurring mistakes](/blog/ai-agents-in-production-4-recurring-mistakes)) are worth more than a new model, a new agent, a new license. Spend there.

5. **Start a single vertical pilot, not a horizontal one.** See trend 1. Identify the most specific problem in your company where AI can help, and put three months on it. More focus, more ROI, more learning.

## The final note for those reading us

This is the last piece of the May-August 2026 editorial plan. Twenty-six articles, written two a week, without skipping any. A promise we made you in May when we reopened the blog after seven months of silence, and that we kept.

In September we'll be back with a new editorial block. Topics, cadence, tone — we'll define them in the coming weeks. If you have suggestions, write to us. This blog isn't a monologue, it's a conversation.

Happy Ferragosto. Good return. See you in September, fresh, with some more ideas than now. And maybe — with a little discipline and a good dose of curiosity — one more piece of AI that really works in our SMEs.

---

**Do you feel halfway between the disappointment phase and the discipline phase?** Under [artificial intelligence for business](/intelligenza-artificiale-per-il-business) and with [OpificioAI](https://opificio.ai) we help Italian SMEs choose and put into production the AI that makes a difference. [Let's talk](/contatti) in September, when you're really ready.

Eighteen months of generative AI applied to Italian SMEs. What worked, what was hidden cost, where to invest in the next 12 months. The long-form closing of the summer — to re-read at Christmas to see if we got it right.

AI in business: 18-month review and compass for 2027

While you're on vacation (or, if you aren't, while half of your team is), your brand is working under the surface. It's connected to the gestures people make in August: opening a fridge in the evening, choosing sunscreen, buying a book for the trip, listening to the radio in the car with the window open.

These gestures have an **emotional grammar**. There are winter gestures (coffee at mid-morning at the bar), spring gestures (the first outdoor aperitif), autumn gestures (the new notebook for back-to-school), summer gestures (the evening gelato). They are different in emotions, symbols, rhythm.

Brands that knew how to hook into these gestures — not with a seasonal campaign, but with an identity that declines in the seasons — built memory lasting decades. Those that *faked* seasonality (a red pack in December, a graphic with the sun in summer) built boredom.

This piece is a map of both attitudes. With an operational ending: what you can do, if you're an Italian SME, to take the first path and not the second.

## The two masters: Mulino Bianco and Lavazza

### Mulino Bianco — seasonality as family narrative

Mulino Bianco is the most sophisticated Italian case of seasonal brand. Forty years of advertising, packaging, product lines, editorial partnerships — all coherent around one idea: **the seasonality of the Italian family**.

Let's see the details we often miss.

In September, *the new notebook*. The snack becomes the ritual of going back to school. The packs become more "binder-like": more orderly colors, more disciplined illustrations, educational claims.

In December, *the holidays*. Panettone lines, focus on gift products, coherent red-gold palettes. No shouting. It's festive order, not the party.

At Easter, *spring in domestic key*. Packaging evokes the home garden, illustrations with animals, themed products (Doria chocolate eggs, Buondì doves).

In June-July, *the pause*. A communication deceleration: less TV advertising, fewer product launches, some summer line (lighter crackers, fruit cookies). The brand is *still but present*.

In August, *almost silence*. Very few campaigns, nothing new, the company is there but doesn't insist.

In September, *we restart*. The cycle begins again, with an amplification coherent with "back to school".

Four decades of this coherence built an identity that's practically an *Italian family calendar*. The brand doesn't *follow* seasonality: it *interprets* it. And every gesture of the Italian consumer in the kitchen has a possible point of contact with Mulino Bianco.

### Lavazza — the seasonality of sociality

Lavazza makes a different but equally refined move. They don't lean on *weather* seasons, but on the *seasons of sociality*.

Lavazza Calendar (the annual photographic calendar, one of the longest-lived institutions of Italian marketing) — different every year, but always tied to an idea of social life, ritual, time flowing gracefully. It's a brand that declares: *coffee is what puts the day on pause, not energy to speed it up.*

Seasonality in campaigns: in September, *back to office* (coffee of the work break). In December, *back home* (the moka on the table, families reunited). In May-June, *out at the bar* (the cappuccino in the piazza). In July, *iced coffee* (summer, deceleration).

Unlike Mulino Bianco, Lavazza doesn't decline on the *domestic gesture* (family lunch), but on the *social gesture* (coffee with another). Two different seasonal grammars, both coherent for decades.

## The three Italian SMEs doing it (almost no one notices)

Let's come to SMEs. Three Italian cases that don't have the budgets of Mulino Bianco or Lavazza, but who built a coherent brand seasonality.

### 1. Vergani Milano — the seasonality of artisanal panettone

Vergani is a historic Milanese producer of artisanal panettone. The product is essentially "Christmasy" — but they built an annual grammar that avoids the trap of "disappearing from January to September".

How? By distributing the calendar over other themed seasonal products: Easter dove, summer sweet focaccias (Venetian focaccia, Carnival tortelli), autumn products (raisin panettone, holiday focaccia). The brand is always present, always with products the season *wants*. In December it amplifies, but never disappears.

**Lesson:** if your core is seasonal, build *around* the core a rotation of minor products that keep you present all year.

### 2. Pomì — the tomato as summer flag

Pomì in recent years has refocused its communication on summer — the natural moment of the tomato. Packs that get more yellow/red in May-June, summer recipes featured, partnerships with festivals and village fairs.

September-October, the brand returns to "comfort" mode: tomato as ingredient of cold-season recipes (long sauces, ragù, parmigiana). Different packs, different palette.

Winter: more sober presence, focus on base product. No shouts, no forcings.

**Lesson:** a mono-product brand can still have seasonality, *if* it knows how to tell itself in the different ways the product is used in the seasons.

### 3. Pellini Caffè — coffee as work cycle

Pellini (Verona coffee, family-owned) has developed in the last five years a seasonality that leans not on weather but on the *Italian work cycle*.

In September-October, communication like "coffee to restart, coffee for the first day". In December-January, "coffee for domestic holiday breaks". In June-July, "coffee on the road, coffee on summer trips". In August, *a lot of silence*: the feeling is that they're on pause too.

It's an *empathic* seasonality. The brand moves at the customer's rhythm. It doesn't force them, doesn't precede them, doesn't follow them: it walks together.

**Lesson:** the most powerful seasonality is not that of the four meteorological seasons. It is that of the seasons *of the customer's life*.

## How to do it in your SME (the practical version)

Three steps.

**1. Define your customer's "emotional calendar".**

Not the calendar of your promotions. The calendar of *the customer's emotions*. When does your customer buy lightheartedly? When do they buy under stress? When do they gift? When do they replace? When do they renew?

Map six or seven emotional moments in the year. Each evokes a color, a language, a tone.

**2. Decline your brand in each moment.**

Don't change the logo, don't change the system. But *amplify or decelerate*. For example: at Christmas raise the chromatic volume by 20%, in August lower it by 30%. In September widen the tone of voice toward "let's restart", in May toward "let's allow ourselves a pause".

It's an orchestration, not a substitution.

**3. Measure coherence, not innovation.**

The seasonal brand that works is *predictable*. Your loyal customers expect that in December you'll show up a certain way. If you suddenly change register for "not being repetitive", you disappoint them. *Intelligent* repetition is the superpower of seasonality.

## The honest final note

This piece arrives mid-August, when half of you are on vacation and the other half are doing the September editorial plan. It's deliberate.

An emotional calendar map is done **best in August**, when you have distance from your brand and can look at it as a customer looks at it. Try to jot it down in 90 minutes, on a notebook, while your team works at half speed. It's one of the most underrated exercises of annual planning.

Thursday, August 13 we close the summer editorial plan with the AI pillar long-form: *AI in business: 18-month review and compass for 2027.* What really worked in Italian SMEs, what was hidden cost, where it's worth investing the next 12 months.

See you Thursday — the last before real rest.

---

**Want to understand if your brand has intelligent seasonality or just a calendar of promotions?** We look at it together on [substantial branding projects](/branding-su-misura-strategia-marketing-aziendale). [Let's talk](/contatti) when you come back.

Mulino Bianco, Lavazza, and three Italian SMEs that made the calendar a brand asset, not a marketing cost. What they have in common — and why smart seasonality is a positioning move that Italian SMEs underrate.

Seasonal brands: who knows how to use the emotional calendar (and who fakes it)

Fourth episode of the series. Today we tackle a word that has become central in 2026 AI presentations and which, for how it gets used, sounds much more technical than it actually is: **multimodal**.

Let's start with the word itself. *Modality* in AI language means "type of data": text, images, audio, video. A *unimodal* model works with one type. A *multimodal* model works with two or more types together.

That's it. It's the basic concept.

What makes the thing interesting is not the definition. It's **what becomes possible** when an AI can look, listen, read, and understand — all at the same moment.

## The metaphor

Think of a specialist you can consult by email.

If your specialist can only read your words, you have to *tell them* everything: the problem, the context, the details, what the object looks like, what sound it makes, where the symptom is. That's what a text-only AI does (base ChatGPT, text Claude). It works, but much of the time is spent *describing* instead of *solving*.

If your specialist can also *see* the photo you send them, *hear* the audio recording, *read* the floor plan you forward — then a big part of describing disappears. You say *"this thing makes this noise when I turn it on"* and send a 5-second video. The specialist knows exactly what you're talking about.

This is a multimodal AI. They understand you with less effort on your part. And often they give you more precise answers, because they have more context to start from.

## The four business cases where everything changes

Three years ago, these applications were advanced research or demos. Today, in 2026, they are real products you can buy or build.

### 1. Customer service: the user sends the photo

Real example. A customer of an appliance company writes to customer care: *"the washing machine is leaking water, what do I do?"*. Classic customer service: ten back-and-forth messages to figure out where it leaks from, how much, in what conditions, model, year of purchase.

Multimodal customer service: the customer sends the photo of the washing machine. The AI recognizes the model, visually identifies the leak (under the gasket, from the filter, from the drain hose), and in two exchanges provides the right procedure.

What's needed for it to work: a multimodal model (today easily accessible via API), a knowledge base of the product (see [the RAG hardwords piece](/blog/hardwords-rag)), an integration with the ticketing system.

Typical ROI: average ticket resolution time drops 40-60%. For high-volume companies, it's huge.

### 2. E-commerce: image search

Example. A user sees a sofa in a photo on Instagram, would like to buy it but doesn't know brand/model. Opens your site, uploads the photo. The multimodal AI recognizes visual characteristics — shape, color, structure, fabric, legs — and finds in your products the 5 most similar.

It works very well for furniture, fashion, accessories. It works less well for categories where the difference is functional and not visual (one mouse from another mouse).

Typical ROI: conversion rate +20-35% on products where image search is activated. Higher on marketplaces, lower on single-product brands.

### 3. Manufacturing: visual quality control

Classic Italian example. A production line that assembles components. Rare defects, but expensive. Quality control traditionally done by expert operators looking "by eye".

Multimodal AI: a camera over the line, a model trained to recognize specific defects. The model flags every suspicious piece to a human operator who decides. The model doesn't replace the operator — *it amplifies them*.

What was *random quality control* becomes *systematic quality control*. Defects that previously escaped get caught. False positives get educated.

Typical ROI: depends heavily on the sector, but for Italian precision engineering manufacturing companies — think your [Loccioni](/blog/bravehistories-loccioni-silent-brand-marche) — it's one of the most mature and best-quantifiable cases.

### 4. Compliance and documentation: "reading complex documents"

Example for professional studios, consultancies, healthcare. An accountant has to check a balance sheet: 80-page PDF with tables, charts, marginal notes. The text-only model struggles with complex PDFs (tables "break" in the text). The multimodal model looks at *the image* of the page and understands the table the way a human would.

Same goes for medical records, contracts with highlighted clauses, floor plans with annotations, technical sheets.

Typical ROI: time spent "deciphering" a complex document drops 70-80%. The part where AI can err (a wrong interpretation) is managed with a final human review, which remains indispensable.

## How to know if you need it

A quick checklist. If you answer yes to one of these, multimodal AI is a serious lead for you.

- Do your customers send you photos, videos, audio in daily communications?
- Do your products have a visually recognizable physical component?
- Does your work include "reading" documents with many images, tables, schemas?
- Do you have specialized operators doing visual or auditory checks?

If the answer to all is no, text AI (more mature, cheaper) probably suffices. If the answer to one is yes, a multimodal pilot is worth evaluating.

## When you don't need it

Three red flags.

**They propose "multimodal AI" for problems that are text-only.** Resist. Adding modalities you don't need is just complexity and cost.

**They propose a "custom" multimodal model without having done a pilot.** Commercial models (GPT-5, Claude 4, Gemini 3) are natively multimodal and work very well on almost all common use cases. Custom is needed only for *really* specific cases (specialized medicine, precision industrial vision). For the rest, start with the commercial model.

**They propose a system that does "everything" — text, images, audio, video, advanced reasoning.** It's probably a package sale, not a targeted solution. Start from a use case, scale when the first works.

## Summary in three lines

A multimodal AI is a model that understands more types of data together — text, images, audio, video.

It changes everything in business cases where communication with the customer or the process isn't only textual.

It's worth starting from a pilot on a specific case, not from an "enterprise multimodal AI strategy".

Monday, August 10 we return to the branding pillar with a piece on **seasonal brands**: who knows how to use the emotional calendar (Mulino Bianco, Lavazza) and who fakes it. Three Italian SMEs that made the calendar an asset, not a cost.

---

**Are you wondering if multimodal AI can really help you?** Under [artificial intelligence for business](/intelligenza-artificiale-per-il-business) we do half-day AI use-case audits. We tell you if it makes sense, and how much it costs to do well. [Let's talk](/contatti).

When an AI model 'sees' beyond words: images, audio, video. What changes practically in customer service, e-commerce, manufacturing, and why the difference between text-only AI and multimodal AI is huge for many business cases.

#HardWords: Multimodal

If you're planning to open a round in Q4 2026 — Series A, Seed, bridge, whatever it is — the useful preparation moment is *now*. Not in September, when you're already sending the first emails. Now, while the company runs at 60%, you have a little headspace, and you can calmly review what you produced hastily during the year.

Three specific things. All doable in two or three days of work distributed in the month. None requires consultants. All make the difference between an investor meeting that proceeds and one that runs aground.

## 1. The deck: cut 30%

The decks we see passing through in 2026 all have the same problem. **They're too long and say too much.**

Thirty-eight slides, each with its title, paragraph, chart. The investor flips through in 4 minutes — it's the declared average time they dedicate to an inbound deck — and forgets everything the next day.

The 2026 deck that works has between 12 and 16 slides. One idea per slide. One memorable sentence per slide. Everything else is in the appendix, or told verbally, or not told at all.

Exercise for August: take your current deck, count the slides, and put a black cross on the 10-15 slides you can remove without changing the substance. You'll probably notice you can remove more than you thought. When you've finished, look at it again. If it's still too long, cut more.

A heuristic rule: if the slide doesn't add an *information that changes the reader's opinion*, it must come out. *"We also do X"* doesn't change opinion. *"We're the only ones in Italy doing X this specific way"* does.

## 2. The narrative: the story in 90 seconds

Regardless of the deck, you must have a **90-second oral narrative** of your business. Ready. By heart. Natural.

Not a one-minute pitch memorized *"we're the Uber of X"* style. A real story. Beginning, development, where you are today, where you're going.

Example structure:

- **Beginning**: the problem that made you meet your market. *"Three years ago we were looking for a solution to X, we didn't find it, and we understood the problem was more widespread than we thought."*
- **Development**: what you built and why in that specific way. *"We tried approach Y for six months, understood it didn't scale, and went back to logic Z — which today is our competitive advantage."*
- **Today**: the present situation, in concrete numbers. *"Forty paying customers, ARR of 800k, growth of 15% month-on-month on enterprise customers."*
- **Where you're going**: the promise for the next 18-24 months. *"We want to reach 3 million ARR by bringing the model to another three adjacent sectors. That's what we're opening the round for."*

90 seconds. Spoken. Without notes. From when you enter the investor's room, you must be able to say this thing naturally — it's the *first* test the investor gives you, even before looking at the deck.

Practice it. Literally. With a stopwatch. Record yourself. Listen back. It's boring, but it's where the first 30% of the valuation is played.

## 3. The numbers: three tables, not twenty charts

Italian investors — and the European ones who flank them — in 2026 ask for *fewer* metrics than a year ago, but *much more seriously*. They've moved from the "wow factor" phase to the "due diligence anticipated at the first meeting" phase.

The three tables that must be **ready, updated to the previous month, and immediately defensible** are:

**Table 1 — Complete commercial funnel.** From qualified lead to signed customer to active customer, month by month, last 18 months. Absolute numbers, not percentages (they calculate themselves). If you don't have 18 months of data, put what you have and declare it honestly.

**Table 2 — Unit economics.** Customer acquisition cost (CAC), customer lifetime value (LTV), payback period, gross margin. For each main customer segment. Even if the numbers are bad (high CAC, still low LTV because you're young), put them — *a serious investor prefers honest bad numbers to clean fake numbers*.

**Table 3 — Cash runway.** How many months do you have at the current spending pace? What changes if growth slows? What changes if you increase sales? Three scenarios (base, prudent, aggressive), all on one page.

These three tables go *outside* the deck, in an appendix or separate Excel. They serve in follow-up conversations, but they go prepared before the first meeting. If the investor asks for them and you reply "we have them, I'll send by Wednesday" — you lose speed. If you send them within 30 minutes of the call — you gain credibility.

## Four things NOT to do in August

For honesty, also what to avoid.

**Don't send emails to investors in August.** 90% won't reply until mid-September. The 10% who reply will be distracted. You waste first-impression chances.

**Don't completely rewrite the deck from scratch.** Iterate on what you have. Revision pays more than restart.

**Don't try to "perfect traction".** Traction is made by product and team, not by August. If the numbers aren't sufficient now, they won't be on September 15 due to a month of extra work.

**Don't talk about your round with everyone.** Voices circulate. Having "round opening" as market rumor without yet a serious plan is a disadvantage. Keep everything in the family (founder + max 2 advisors) until September.

## A note on how we're seeing valuations change

What we observe in rounds closed in the first half of 2026: Italian Series A valuations are **about 30% below the 2021-2022 peaks** but they're *stabilized*. The downward pressure of 2024 is no longer seen.

What does it mean for you: if you open a round in September, start with realistic expectations. Valuation multiples on ARR have normalized in the 6-10x range for B2B SaaS (down from 15-20x in 2021). For applied AI it goes higher, but with very strict conditions on milestones. For consumer / DTC it struggles.

Indicative numbers, varies a lot by sector and geography. But the strategic message is: **evaluate what valuation you've mentally prepared for, and drop by 25-30%**. That will be the realistic starting base of the conversation.

Thursday, August 6 we return to the glossary with a much-requested episode: **#HardWords: Multimodal.** When a model "sees" beyond words — and what changes practically in customer service, e-commerce, and manufacturing.

---

**Are you preparing a round for Q4?** Under [brand and narrative strategies for startups](/startup-innovative-sviluppo-su-misura), we help founders build the story they sell to investors. [Let's talk](/contatti) — even in August, even at the end of August.

Deck, narrative, numbers. What will change in the next six months among Italian investors — and what they'll ask you first if you open the conversation in September. A preventive checklist to do calmly now that there's no pressure yet.

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