Beyond the Headlines: What AI Overtaking Blockchain in Fintech Really Means for Marketing Agencies in Switzerland

Direct answer: AI and data analytics have become the largest technology segment inside Switzerland's fintech industry, overtaking blockchain — a category that dominated Swiss fintech's public identity for years. For marketing agencies serving Swiss fintech clients, this means the pitch, the deliverables, and the internal tooling all need to shift toward AI-literate work, because that is where client budgets and product roadmaps are actually moving.

FintechNews.ch reported in August 2026 that AI and data analytics have overtaken blockchain to become the largest fintech technology segment in Switzerland. This is a meaningful marker for a market that built much of its international fintech reputation on crypto and blockchain infrastructure — Zug's "Crypto Valley" branding, distributed ledger pilots at major banks, and blockchain-native startups clustered around Zurich and Geneva. A category shift like this does not happen because of one product launch; it happens because capital, hiring, and client demand move in the same direction over a sustained period. For a marketing agency working with fintech clients in Switzerland, that shift in where the money and headcount are flowing is more useful signal than any single funding announcement. It tells you what your clients will be asking for in briefs over the next 12 to 18 months, and what your own agency needs to be able to talk about credibly. The rest of this post works through what the shift actually means in practice, why it matters specifically to agencies operating in the Swiss market, and what to change first.

What This Trend Actually Is — And Why It's Real

"AI overtaking blockchain" sounds like a headline built for clicks, so it's worth being precise about what the FintechNews.ch data point actually says: AI and data analytics is now the largest technology segment within Swiss fintech, by whatever measure of company count, activity, or investment FintechNews.ch used to categorize the market. It does not say blockchain is dead, that Swiss fintech has abandoned crypto infrastructure, or that every fintech company is now "an AI company." Categories like this are built from real company classifications, and a segment-leadership change reflects a genuine shift in where new companies are forming and where existing companies are directing engineering effort.

Why is this credible rather than hype? Two structural reasons. First, blockchain-native fintech had a narrower practical surface area — payments rails, custody, tokenization, settlement — and much of that work matured into infrastructure rather than a growth category needing constant new tooling. Second, AI and data analytics apply horizontally across nearly every fintech function: fraud detection, credit underwriting, wealth management personalization, regulatory reporting, customer service, and internal operations all have an AI angle, which naturally pulls more companies and more spend into that bucket over time. A category with broader applicability outgrows a narrower one — that is a structural pattern, not a fad.

Why Switzerland Specifically

Switzerland's fintech sector has always leaned on precision, regulatory credibility, and wealth management depth rather than sheer volume. Those same traits make it fertile ground for AI adoption in fintech: private banks and wealth managers have dense structured data and high-value use cases (portfolio insight, compliance automation, client reporting) where AI tooling pays for itself quickly. It is a logical place for AI-in-fintech to consolidate share.

There's a second, more practical reason this consolidation is happening in Switzerland specifically rather than being purely a global pattern showing up locally. Swiss financial institutions carry decades of structured client and transaction data, held under some of the strictest data governance standards in Europe. That combination — rich data plus a mature compliance culture — is exactly the environment where AI and analytics tooling has the shortest path from pilot to production, because the governance questions that stall AI adoption elsewhere are already partly answered by existing regulatory practice. Blockchain, by contrast, often required building new infrastructure and new trust models from scratch, which is a slower, more capital-intensive path to scale. That difference in adoption friction is a plausible structural explanation for why one category grew into the larger segment while the other matured into a smaller, more specialized one.

It's also worth being honest about what this shift is not. It is not evidence that blockchain was a mistake, that Swiss regulators have soured on distributed ledger technology, or that companies built around blockchain infrastructure are struggling. Segment-size comparisons are relative — one category can grow rapidly in absolute terms while still ending up smaller than a category that grew even faster. Treating this as "blockchain lost" rather than "AI and analytics grew into the larger share" is the kind of oversimplification that leads to sloppy client messaging, which is precisely the trap this post is trying to help agencies avoid.

Why This Matters to Marketing Agencies in Switzerland

If you run or work at a marketing agency serving Swiss fintech clients, this segment shift changes three things: what your clients are building, what they are hiring for, and what they expect a marketing partner to understand.

Fintech marketing briefs have historically leaned on blockchain-adjacent language — decentralization, tokenization, "trustless" infrastructure — because that vocabulary matched what founders and product teams were actually building. As AI and data analytics become the larger segment, the substance of what needs marketing changes: AI-driven underwriting decisions, automated advisory tools, fraud-detection features, and data-driven personalization in banking apps. An agency that keeps producing blockchain-flavored messaging for a client whose actual product roadmap has moved to AI-driven features will produce content and campaigns that misrepresent the product — which shows up quickly in sales conversations and investor decks that don't match the marketing.

There's also a talent and vendor-selection angle. Swiss fintech companies making this shift are going to be evaluating agency and vendor partners partly on whether those partners understand AI-driven product work well enough to message it accurately, build the demo experiences that support it, and avoid overclaiming or underclaiming what the technology does — a real risk in a regulated market like Switzerland's financial sector, where imprecise AI claims can create compliance exposure. Agencies that can speak fluently about model-driven features, data pipelines, and automation — without resorting to vague AI buzzwords — have a credibility edge in new-business conversations with this client base.

Consider what a typical new-business conversation looks like today versus what it will increasingly look like. A fintech founder or head of marketing describing their product two years ago would have spent real time explaining blockchain concepts to an agency team before the agency could even draft accurate messaging. Today, that same conversation is more likely to involve explaining a recommendation engine, a fraud-scoring model, or an automated compliance check — and the agency's ability to ask sharp, informed follow-up questions in that conversation is itself a signal of competence. Clients notice when a prospective partner needs the basics explained versus when a partner already understands the shape of the problem and can push back intelligently on a proposed feature description. That difference shows up in which agencies get shortlisted for the work in the first place, well before any creative is produced.

This also changes what "understanding the client's business" means for an account team. It used to be enough to understand a fintech client's market position, competitors, and regulatory environment. Now it also means understanding, at a working level, what their AI-driven features actually do — not to the degree of a data scientist, but enough to write, edit, and approve marketing language that won't need to be walked back by the client's compliance or product team after the fact. Agencies that treat this as a one-time briefing rather than an ongoing competency tend to fall behind as client products keep evolving.

What Changes in Practice for Your Website, App, and Product Work

For an agency, "the client moved to AI" is not an abstract observation — it changes concrete deliverables.

Case studies and portfolio positioning. If your agency's public case studies still lean heavily on blockchain fintech work from a few years ago, that portfolio increasingly reads as dated to a Swiss fintech buyer evaluating partners in 2026. Refreshing case studies to include AI-driven features — even ones layered into otherwise traditional fintech products — signals that the agency is current with where the market actually is.

Website and app copy for fintech clients. Product pages, onboarding flows, and app store descriptions written for a blockchain-first fintech client often need updating when the underlying product adds AI-driven features like automated categorization, predictive alerts, or conversational support. Getting this copy accurate — not overstating what a model does, not underselling a genuinely useful automation feature — is now a core part of the fintech marketing brief rather than a nice-to-have.

Internal agency workflows. This is where the shift is most direct for the agency itself, separate from client output. Fintech clients moving toward AI and data analytics are, almost by definition, more comfortable with — and more likely to ask about — AI-powered workflows in general. An agency that has already integrated automation into its own operations (content production pipelines, reporting, lead qualification, campaign optimization) can speak from experience rather than theory when a client asks how AI fits into their own marketing stack. Our guide on AI Automation ROI: How to Measure Whether It's Actually Working is a useful starting point for agencies that want to make this case to clients with real measurement discipline rather than vague promises.

The Compliance and Trust Layer

Swiss finance is a heavily regulated environment, and AI-driven features in fintech products — automated credit decisions, algorithmic advice, fraud flags — carry real compliance weight (FINMA oversight, data protection rules, disclosure obligations). Marketing copy and app experiences that describe these features need to be accurate about what the system does and does not do. Overstating AI capability in a regulated financial product is a bigger liability than in most other categories, and agencies producing client-facing content need to build that discipline into their review process, not treat it as the client's problem alone.

In practice, this means building a review step that specifically checks AI-related claims before anything ships — not folding it into a general proofread. A useful discipline is to ask, for every sentence that describes an AI-driven feature, whether a compliance officer at the client's institution could read it and confirm it's accurate without qualification. If the answer is "mostly, but it implies more autonomy than the system actually has," that sentence needs rewriting before publication, not after a client flags it. Agencies that build this checkpoint into their standard workflow for fintech clients — rather than relying on the client to catch every overstatement — become materially easier to work with for compliance-conscious Swiss financial institutions, and that reliability itself becomes a competitive differentiator worth mentioning in new-business pitches.

What to Do About It

The practical response for a Swiss marketing agency is not to chase every AI trend uncritically, but to build genuine competence in the specific area where fintech client work is actually heading: AI-driven automation and data-backed features, communicated accurately.

Start by auditing current fintech client relationships and prospective briefs for where AI or data-analytics features already exist in the product but aren't reflected in current marketing assets. That gap is often the fastest, lowest-risk piece of work to propose — updating existing collateral to match a product that has already changed, rather than pitching a large net-new AI initiative.

Second, invest in the agency's own automation capability so it can be demonstrated, not just described, in new-business conversations. Building this via AI Agents & Automation gives an agency a working reference point — internal automation for reporting, content workflows, or client onboarding — that it can point to when a fintech client asks "how would you actually help us use this." Agencies that only talk about AI in the abstract lose credibility quickly against ones that can show a working example.

This is worth sequencing deliberately rather than treating as a side project squeezed between client deadlines. A reasonable order is: pick one internal workflow with a clear, measurable before-state (time spent, error rate, turnaround); build a working automation for it; measure the actual change over four to six weeks; then use that concrete before-and-after as the reference case in client conversations. Skipping the measurement step and going straight to "we use AI internally too" in a pitch is a much weaker claim than being able to say, specifically, what changed and by how much. Fintech clients in particular — an audience trained to expect quantified evidence — respond better to an agency that can show its own numbers than one that gestures at AI adoption without specifics.

Third, keep the visual and brand execution disciplined even as the underlying technology story gets more technical. A fintech product built around AI-driven features still needs a coherent, trustworthy visual identity — arguably more so, since trust signals matter more when the product is making automated decisions on someone's money. Our guide on how to choose a colour palette for brand and website work is a practical reference when rebuilding or refreshing a fintech client's visual system alongside a product repositioning.

Finally, it's worth noting this shift is part of a broader pattern of Swiss and European businesses recalibrating priorities in 2026 — not unlike the pullback happening in other sectors, such as the one detailed in Corporate Net-Zero Rollback: Inside 2026's Year of the ESG Retreat. In both cases, the lesson for a marketing partner is the same: track where client priorities are actually moving, rather than where they were two or three years ago, and adjust positioning before the client has to ask you to.

Where This Kind of Work Typically Falls in Scope

Agencies asking Scult to help build or refresh AI-driven marketing and product experiences for fintech clients generally land in one of three tiers, depending on scope:

Tier	Typical scope	Starting price
Essential	Website/app copy and page updates reflecting new AI features, light automation setup	$1,000
Growth	Case study rebuild, AI-agent-driven workflows (lead qualification, reporting), updated visual system	$2,000
Enterprise	Full AI Agents & Automation build for the agency or a fintech client, custom app features, ongoing support	$4,000+

These are starting points, not fixed quotes — actual scope depends on how many client touchpoints and automations are involved. An Essential-tier engagement is usually the right starting point when the underlying product hasn't changed dramatically and the work is mostly about bringing existing marketing assets up to date. Growth-tier work tends to fit agencies that want a working automation to point to in pitches, alongside a refreshed set of case studies and visual assets for one or two flagship fintech accounts. Enterprise-tier scope generally applies when either the agency itself needs a fuller automation buildout across multiple workflows, or when a fintech client wants the agency to help build AI-driven features directly into their own website or app rather than just market around them — at which point the work shifts from marketing support into genuine product development, and the pricing reflects that broader scope.

Key Takeaways

• Swiss fintech's technology center of gravity has genuinely shifted to AI and data analytics, per FintechNews.ch (Aug 2026) — this is a segment-level change, not a single company's pivot.
• Blockchain fintech work in Switzerland hasn't disappeared, but agency portfolios and case studies leaning heavily on it now read as dated to fintech buyers.
• Website and app copy for fintech clients needs review wherever AI-driven features exist in the product but aren't reflected in current marketing.
• Regulatory accuracy matters more for AI claims in Swiss financial products than in most other categories — build review discipline around this.
• Building real internal automation capability via AI Agents & Automation gives an agency a demonstrable answer when fintech clients ask about AI, rather than a theoretical one.
• Track adjacent shifts in client priorities, like the ESG retreat, as part of the same discipline of staying current with where budgets are actually moving.

Swiss fintech clients are going to keep asking sharper questions about AI as this segment shift plays out over the next year, and agencies that can answer with substance — not buzzwords — will win more of that work. If you want help figuring out where to start, book a meeting with our team.

Frequently Asked Questions

What does it mean that AI overtook blockchain in Swiss fintech?

It means AI and data analytics companies now make up the largest technology segment within Switzerland's fintech industry, based on FintechNews.ch's August 2026 reporting, surpassing what had been blockchain's leading position. It reflects where new company formation and product investment are concentrated, not a claim that blockchain fintech has disappeared.

Is blockchain fintech disappearing in Switzerland?

No. The data point is about relative segment size, not decline — blockchain infrastructure, custody, and tokenization work continues in Switzerland, particularly around Zug and Zurich. What has changed is that AI and data analytics now represent a larger share of the overall fintech landscape.

Why does this matter to a marketing agency rather than just fintech companies themselves?

Agencies serving fintech clients need their messaging, case studies, and creative output to match what clients are actually building. If a client's product roadmap has shifted toward AI-driven features, marketing built around older blockchain-first positioning becomes inaccurate and can undercut sales conversations and investor materials.

What specific agency deliverables need to change first?

Product and website copy describing client features, portfolio case studies used in new-business pitches, and any messaging that implies a client's core value proposition is blockchain-based when the product has actually shifted toward AI-driven functionality.

How do I know if my fintech client's product has already shifted toward AI features?

Ask directly, or review recent product release notes and app updates. Features like automated categorization, predictive alerts, fraud flags, or personalized recommendations are common signs of AI-driven functionality that may not yet be reflected in marketing copy.

Does this trend apply only to banks, or to fintech startups too?

Both. The FintechNews.ch segment classification covers the fintech sector broadly, including startups, scale-ups, and established financial institutions building fintech products. Agencies working with any size of Swiss fintech client should consider this shift relevant.

What is "AI Agents & Automation" as a service, in plain terms?

It refers to building automated workflows — using AI models and rule-based logic together — that handle tasks like data processing, customer responses, reporting, or lead qualification without manual intervention at every step. For an agency, this can mean internal automation or automation built into a client's product.

How much does an AI automation project typically cost?

Scope-dependent. Lightweight automation setups and copy updates typically fall in the Essential tier around $1,000, mid-scope work like automated reporting or lead workflows sits around $2,000 in the Growth tier, and full custom builds with ongoing support start at $4,000+ in the Enterprise tier.

How long does it take to build an AI automation workflow for an agency?

A single-workflow automation (e.g., automated report generation or lead qualification) typically takes a few weeks from scoping to launch, depending on how many systems it needs to connect to. Larger multi-workflow builds take longer and are usually phased.

Do Swiss data protection rules affect AI automation projects?

Yes. Switzerland's Federal Act on Data Protection (FADP) applies to how personal data is processed, including by automated systems, and financial data carries additional regulatory attention. Any automation handling client or customer data should be scoped with this in mind from the start.

Does FINMA regulate AI-driven marketing claims?

FINMA's oversight focuses on financial conduct and product compliance rather than marketing copy directly, but marketing claims about AI-driven financial features can create compliance exposure if they misstate what a regulated product actually does. It's prudent to have compliance-aware review on any AI-related claims in fintech marketing.

What's the risk of overstating AI capabilities in fintech marketing?

Overstated claims about automated decision-making, credit scoring, or advisory features can mislead customers and create regulatory and reputational risk, particularly in a market like Switzerland where financial trust is a core brand asset. Accurate, specific language protects both the client and the agency producing the content.

Should my agency stop using blockchain terminology entirely?

Not if a client's product genuinely still relies on blockchain infrastructure — accuracy is the standard, not trend-chasing. The point is to make sure marketing language reflects the actual current product, whether that's blockchain-based, AI-based, or a mix of both.

How does this trend affect app store listings and app copy?

If a fintech app has added AI-driven features like automated insights or predictive alerts, app store descriptions and in-app onboarding copy should reflect those features accurately, since app store copy is often a prospective user's first exposure to what the product actually does.

What should a refreshed fintech case study include?

It should show specific, current features — including any AI-driven functionality — rather than positioning built around a client's earlier-stage product. Concrete descriptions of what a feature does and the outcome it produced for users are more persuasive than generic AI language.

How does color and visual identity fit into an AI-driven fintech rebrand?

Trust signals matter more when a product involves automated financial decisions, so visual identity work — palette, typography, layout — should reinforce credibility and clarity. Our guide on how to choose a colour palette covers practical considerations for this kind of brand work.

What is data analytics, as distinct from AI, in this context?

Data analytics refers to processing and interpreting data to surface patterns or insights, which can be done with or without AI models; AI typically refers to systems that make predictions, classifications, or generate content based on learned patterns. FintechNews.ch groups them together as one segment because they're frequently used in combination in fintech products.

Why did blockchain dominate Swiss fintech's identity for so long?

Switzerland built a strong "Crypto Valley" reputation, particularly around Zug, through early blockchain and cryptocurrency company formation, favorable regulatory clarity, and high-profile blockchain foundations choosing to base operations there. That reputation persisted publicly even as the underlying company mix diversified.

Is this shift specific to Switzerland, or is it happening globally?

The FintechNews.ch data point is specific to the Swiss fintech market. Similar directional trends toward AI adoption exist in fintech markets globally, but this post is grounded specifically in the Swiss data point and should not be extended to claims about other markets without separate verification.

How can an agency measure whether its own automation investment is paying off?

Track concrete metrics like time saved per workflow, error rate reduction, or output volume per team member before and after automation, rather than relying on general impressions. Our post on measuring AI automation ROI walks through a practical framework for this.

What's the first automation an agency should build for itself?

Common starting points are automating repetitive reporting (pulling and formatting client performance data) or lead qualification (triaging inbound inquiries), since both have clear before/after metrics and free up staff time relatively quickly.

Do I need a data science team to build AI automation for a fintech client?

Not necessarily. Many practical automation workflows use existing AI models and integration tools rather than requiring custom model training, which lowers the barrier for agencies without in-house data science capability, especially when working with an experienced automation partner.

How does this trend affect hiring for marketing agencies?

Agencies may find it useful to build at least baseline fluency in AI and automation concepts among account and creative staff, since client conversations increasingly involve these topics. This doesn't necessarily require hiring dedicated AI specialists, especially if the agency partners with a specialist for build work.

What happens if my agency ignores this shift entirely?

The main risk is gradually losing credibility with fintech clients and prospects who expect a marketing partner to understand their current product and technology direction, potentially losing pitches to agencies that demonstrate more current fintech and AI fluency.

Are Swiss consumers more or less trusting of AI-driven financial products?

Swiss consumers generally place a high value on privacy, security, and institutional trust in financial services, which means AI-driven features need to be communicated with clarity about data handling and decision logic to maintain that trust, rather than assumed to be automatically welcomed.

How specific does AI messaging need to be to avoid sounding like buzzwords?

Effective messaging names the specific task the AI performs (e.g., "flags unusual transactions for review" rather than "AI-powered security"), the boundaries of what it does, and ideally a plain-language explanation of the benefit to the user.

Can a small agency compete for Swiss fintech clients without heavy AI expertise?

Yes, particularly by partnering with a specialist for the technical automation build while focusing agency strengths on brand, messaging, and creative execution — the accuracy and specificity of communication matters as much as in-house technical depth.

What role does app UX play when a fintech product adds AI features?

UX needs to make automated recommendations, alerts, or decisions understandable and, where relevant, reviewable by the user — poorly explained AI-driven UI can undermine trust even when the underlying feature works well.

How often should fintech marketing collateral be reviewed for accuracy?

Given how quickly product features can change in this space, a quarterly review of core marketing assets (website copy, case studies, app store listings) against actual current product functionality is a reasonable baseline for fintech clients.

What's a realistic timeline for updating a fintech client's website copy to reflect new AI features?

A focused copy update across key pages typically takes one to two weeks including client review cycles, assuming the underlying feature information is readily available from the product team.

Does this trend affect B2B fintech (software for banks) differently than B2C fintech apps?

B2B fintech buyers tend to scrutinize AI claims more rigorously during procurement and due diligence, so marketing and sales collateral for B2B fintech may need more technical depth and documentation than consumer-facing B2C messaging.

What kind of AI features are most common in Swiss wealth management products right now?

Common categories include portfolio analysis and insight generation, automated client reporting, and personalized recommendation tools, reflecting the sector's data-rich, high-value client base — though specific implementations vary by firm.

Should agencies disclose when marketing copy was AI-generated?

This is increasingly a best-practice and, in some contexts, a regulatory consideration; agencies should check applicable Swiss and EU guidance for the specific content type and be prepared to disclose AI involvement where required or where it affects consumer trust.

How does the EU AI Act affect Swiss fintech marketing, given Switzerland isn't in the EU?

Swiss fintech companies serving EU customers or operating cross-border may still need to consider EU AI Act obligations depending on where their users are based, so agencies should flag this as a consideration for clients with EU-facing products rather than assume Swiss-only rules apply.

What's the difference between AI automation and AI-generated content for marketing purposes?

AI automation typically refers to workflow processes (like automated reporting or lead routing), while AI-generated content refers to using AI to draft or produce marketing materials themselves — both are relevant to a fintech marketing partner but involve different tools and review processes.

How do I pitch an AI automation project to a fintech client who is skeptical of AI?

Lead with a narrow, low-risk use case with a clear before/after metric (like reduced response time or fewer manual errors) rather than a broad AI transformation pitch, and be explicit about what human oversight remains in the workflow.

What ongoing support does an AI automation build usually need?

Automated workflows typically need periodic monitoring for accuracy drift, updates when connected systems or data sources change, and occasional retuning as business needs evolve — this is usually scoped as part of Growth or Enterprise tier engagements.

Can existing website content be reused, or does it need a full rewrite?

Most existing content can be reused with targeted updates to sections describing specific features or capabilities, rather than requiring a full rewrite — a content audit is the right first step to identify exactly what needs updating.

What's a reasonable first project size for a Swiss fintech client cautious about AI spend?

An Essential-tier engagement focused on updating existing copy and light automation setup is a low-risk way to demonstrate value before committing to a larger Growth or Enterprise-tier build.

How does this trend interact with Switzerland's broader reputation for financial privacy?

AI and data analytics adoption in Swiss fintech generally needs to be paired with strong data handling practices to remain consistent with the market's privacy-conscious reputation, which should be reflected in both product design and how it's marketed.

Does Scult work with agencies directly, or only with fintech companies themselves?

Scult works with both — marketing agencies building internal automation capability or client-facing AI features, and fintech companies building these features directly into their own products or websites.

What's the biggest mistake agencies make when messaging AI features?

The most common mistake is using vague, generic AI language ("powered by cutting-edge AI") instead of specific, concrete descriptions of what the feature actually does for the user, which reduces both trust and conversion.

How do I explain to a client why their blockchain-first positioning might need updating?

Frame it around what has changed in their own product roadmap and hiring rather than the abstract market trend alone — the market data point (FintechNews.ch, Aug 2026) is useful supporting context, but the client-specific product evidence is what should drive the recommendation.

Will this AI-over-blockchain trend reverse in the near future?

There's no way to predict this with certainty; segment leadership in a fast-moving industry can shift again, and the responsible approach is to keep tracking client-specific product direction rather than assuming any one segment split is permanent.

What metrics should an agency track to know if its fintech-focused positioning is working?

Track pitch win rates for fintech briefs, the technical accuracy feedback received from client product teams, and lead quality from fintech-specific marketing content, comparing periods before and after updating AI-related positioning.

How does automation affect campaign reporting for fintech clients?

Automated reporting can pull performance data from multiple channels into a consistent format on a set schedule, reducing manual reporting time and improving consistency, which is often one of the first automation wins agencies can demonstrate to clients.

Should smaller Swiss fintech startups care about this trend as much as larger institutions?

Yes — smaller startups are often the ones actively building AI-first products from scratch and benefit even more from marketing that accurately reflects their AI-driven differentiation, since it's frequently central to their competitive positioning.

What's the relationship between this fintech trend and the broader "AI agents" conversation in 2026?

The rise of AI and data analytics in fintech is one concrete, sector-specific instance of the broader shift toward AI agents and automation across industries in 2026, which is why building internal automation competence has relevance beyond fintech client work alone.

How should an agency budget for staying current on this trend?

Rather than a one-time project, treat ongoing AI fluency — through a working internal automation setup and periodic content review — as a recurring operational cost, similar to how agencies budget for other tooling and training.

What should I do next if I manage fintech accounts at a Swiss marketing agency?

Start with an audit of current fintech client marketing assets against their actual current product features, identify the highest-impact gaps, and consider booking a meeting to discuss scoping either a copy refresh or a working automation build for your own agency.