Are Enterprise IT Teams Ready for Switzerland's Deep-Tech Advantage? in Switzerland

Direct answer: No, most enterprise IT teams in Switzerland are not fully ready for the deep-tech advantage building around them, because the gap isn't a lack of ambition — it's a lack of internal capacity to build and operate the AI-driven systems that biotech, robotics, and climate-tech peers are already shipping. The fix isn't hiring an army of specialists; it's pairing lean internal teams with focused external partners who can implement AI agents, automation, and modern software fast, without the multi-year build cycle.

Switzerland has quietly become one of the strongest deep-tech environments in the world, with enduring strength across biotech, deeptech, robotics, climate tech, and enterprise software, according to Swiss startup ecosystem reporting from 2026. This isn't a one-off funding cycle or a single hot sector — it's a structural pattern that has held across market swings, driven by strong research institutions, a dense concentration of specialized engineering talent, and enterprise buyers who are comfortable paying for precision and reliability over flashy demos. For enterprise IT teams inside Swiss organizations — whether the core business is pharma, industrial manufacturing, financial services, or logistics — this creates a specific kind of pressure. The companies around you are proving that deep, technically serious software and AI work is not just possible in this market, it's expected. A precise figure on how much of this activity is AI-native versus traditional deep tech is not publicly available for this specific breakdown, but the general pattern is unmistakable: Switzerland's competitive identity is shifting toward technically rigorous, AI-augmented systems, and internal IT organizations are the ones left to operationalize that shift inside their own companies. This post walks through what that trend actually means, why it lands differently on enterprise IT than on startups, and what a realistic response looks like.

Why This Story Is Different From the Usual "AI Hype" Coverage

It's easy to be skeptical of yet another headline about a country or sector being "AI-ready." Most of that coverage is thin — a survey of executive sentiment, or a projection built on assumptions nobody can verify. This trend is different in kind, not just degree, because it isn't a claim about intentions or plans. It's a claim about where technical capability has already concentrated, sector by sector, over years, in a country whose economy has always rewarded precision engineering over speed-to-market hype. That distinction matters for enterprise IT teams deciding how seriously to take it. A survey about AI optimism can be safely ignored. A structural concentration of deep technical capability across five distinct sectors, sitting right next to your own organization, is much harder to ignore — because it changes who your internal teams are implicitly being compared against, whether that comparison is explicit in a boardroom conversation or simply present in the expectations of new hires who've worked at more technically ambitious companies.

What "Switzerland's Deep-Tech Advantage" Actually Means

It's worth being precise about what this trend is and isn't. It is not a claim that Switzerland has suddenly produced a wave of AI unicorns, and it is not a claim tied to any single company or funding round. What the 2026 reporting on the Swiss startup ecosystem points to is more durable and, frankly, more useful for enterprise planning: a consistent concentration of serious technical work across biotech, deeptech, robotics, climate tech, and enterprise software. These are not hype-cycle categories. They are sectors that require rigorous engineering, regulatory awareness, and long product cycles — which is exactly why Switzerland, with its research base and engineering culture, has held a strength there for years rather than months.

Why This Is Structural, Not a Fad

The reason this matters to enterprise IT teams specifically is that structural trends don't fade when the news cycle moves on. A viral AI tool trend might disappear in a quarter. A country-level concentration of deep-tech capability across five distinct sectors — spanning life sciences, hardware, sustainability, and software — is a much stickier signal. It tells you that the expectations bar for technical execution in the Swiss market is rising steadily, not spiking temporarily. If your organization's internal systems, product experience, or operational tooling look the same as they did two years ago, you are quietly falling behind a market that is used to technical seriousness.

Why Five Sectors Together Tell a Bigger Story Than One

Any single one of these five sectors — biotech, deeptech, robotics, climate tech, or enterprise software — being strong somewhere in the world wouldn't be especially newsworthy. Plenty of countries have one standout vertical. What makes the Swiss pattern worth an enterprise IT team's attention is that all five are strong simultaneously, in the same relatively small country, feeding off the same research institutions and the same talent pool. That cross-pollination is exactly what makes the trend durable: a robotics engineer who moves to an enterprise software company brings hardware-grade rigor with them. A biotech data scientist who ends up building internal tooling for a logistics firm brings regulatory discipline to a domain that didn't previously demand it. This mixing is part of why "enterprise software" specifically, sitting inside this list, carries more weight than it would as a standalone claim — it's being built and staffed by people whose formative experience came from unusually demanding technical environments.

Why Enterprise Software Sits at the Center

Of the five sectors named — biotech, deeptech, robotics, climate tech, and enterprise software — enterprise software is the one every other Swiss company touches directly, regardless of industry. A biotech firm still needs internal tooling. A robotics manufacturer still needs a customer portal and an operations dashboard. Climate-tech companies still need to manage compliance data and reporting workflows. This is the connective layer, and it's exactly where enterprise IT teams live. When "enterprise software" strength is named alongside more specialized deep-tech categories, it signals that Swiss buyers and partners increasingly expect software to be built to the same standard of rigor as the deep-tech work happening around it — not treated as a lower-priority internal function.

Why This Matters Specifically for Enterprise IT Teams in Switzerland

Enterprise IT teams occupy an unusual position in this story. You're not the deep-tech researchers, and you're often not customer-facing in the way a product team is — but you are the group responsible for making sure your organization's internal capability doesn't lag the market it competes in. That creates three specific pressures worth naming honestly.

First, there's a talent-gravity problem. When robotics, biotech, and deeptech companies in Switzerland are hiring aggressively for technically demanding roles, the pool of engineers available to staff traditional enterprise IT projects tightens. You're not just competing with other IT departments for talent anymore — you're competing with sectors that can offer more technically interesting work. That makes it harder to justify large internal builds staffed entirely in-house, and it makes external delivery partners a more rational default rather than a fallback.

Second, there's an expectations problem. Business stakeholders inside Swiss enterprises read the same coverage of the Swiss startup ecosystem that everyone else does. When they see enterprise software named as a national strength, they expect their own internal systems — the ones IT teams own — to reflect that same level of capability. A clunky internal approval workflow or a customer support process still running on manual triage becomes harder to defend when the surrounding market narrative is about technical excellence.

Third, there's a real automation opportunity being left on the table. If robotics and deeptech companies nearby are automating physical and operational processes at a serious level, enterprise IT teams have a parallel opportunity in digital process automation — using AI agents to handle the repetitive coordination, data-entry, and triage work that consumes disproportionate internal engineering time. This is where the trend stops being abstract and starts being actionable.

There's also a fourth, quieter pressure worth naming: vendor and partner expectations. As Swiss enterprise software companies raise their own technical bar, the third-party tools, APIs, and integrations your internal IT team relies on are themselves becoming more sophisticated — and often more demanding to integrate correctly. A payments API, a compliance data feed, or a logistics partner's system built by a technically strong Swiss enterprise software team will assume a certain level of engineering maturity on your side too. Internal IT teams that haven't kept their integration practices current can find themselves increasingly out of step with the partners and vendors they depend on, not just with abstract market expectations.

The Compounding Effect of Doing Nothing

None of these four pressures is dramatic on its own in any given quarter. The risk is compounding, not sudden. A talent gap that costs you one strong hire this year becomes two next year. An internal tool that looks slightly dated today looks clearly dated in eighteen months if nothing changes while competitors iterate. This is precisely why the right response isn't panic or a sweeping transformation program — it's steady, deliberate investment in the specific areas where the gap is starting to show, starting now rather than waiting for the gap to become undeniable.

What Changes in Practice for Your Website, App, or Internal Systems

Stop Treating Internal Tools as Lower Priority

The most common mistake enterprise IT teams make is applying their best engineering standards to customer-facing products while letting internal tools — ticketing systems, approval chains, reporting dashboards — degrade over time. In a market where enterprise software is a named strength, that gap becomes a competitive liability. Internal tooling is often the first place AI agents and automation deliver measurable time savings, because the workflows are well understood, repetitive, and already documented in existing systems.

Reassess What "Built In-House" Actually Costs

Given tightening technical talent availability, it's worth recalculating the real cost of building and maintaining every internal system entirely in-house versus using a focused partner for scoped builds. This isn't an argument for outsourcing everything — it's an argument for being deliberate about which projects deserve full internal ownership and which are better handled by a team that can move faster on a defined scope, particularly around AI agent implementation, where the tooling and best practices are evolving quickly and internal teams often can't dedicate full-time headcount to staying current.

Get Deliberate About Where AI Agents Fit

AI agents and automation aren't a single tool you install — they're a layer that has to be designed into specific workflows: customer support triage, internal document processing, data reconciliation between systems, or first-line technical support. Enterprise IT teams that succeed here start with one well-scoped workflow, measure the time and error reduction, and expand from there, rather than announcing an "AI transformation" initiative that tries to touch everything at once. Scult's AI Agents & Automation work follows exactly this pattern: scoped, measurable automation projects rather than open-ended platform rebuilds.

Rethink the Build-vs-Buy Line for AI Capability Specifically

Most enterprise IT teams already have a working build-vs-buy framework for infrastructure and off-the-shelf software. Very few have updated that framework for AI agent capability specifically, and the default tends to be one of two extremes: either "we'll figure this out ourselves eventually" (which quietly becomes never, as other priorities absorb the team's time) or "we need a full platform re-architecture" (which is expensive, slow, and often unnecessary for the actual problem at hand). The more useful frame is to treat AI agent capability the way you'd treat any other specialized engineering discipline your team doesn't practice daily — bring in focused expertise for the build, keep ownership and oversight of the outcome internal, and use the first project as the basis for deciding how much of this to eventually bring in-house.

What to Actually Do About It

The practical response for most Swiss enterprise IT teams isn't a massive reorganization — it's three concrete moves. First, audit your internal and customer-facing systems against the standard the market is setting: would a technically sophisticated peer company look at your current tooling and see parity, or a gap? Second, identify one or two workflows where AI agents and automation could remove real manual effort — not hypothetically, but with a clear before/after in hours saved or errors reduced. Third, decide honestly whether that work should be built internally or handed to a partner who can execute it faster without pulling your core team off other priorities.

Run the Audit Like You Mean It

The audit step is where most teams either get real value or waste an afternoon producing a document nobody acts on. To make it useful, walk through your organization's core internal workflows the way a new hire would experience them on their first week — the ticketing system, the approval chain for a purchase order, the process for onboarding a new vendor or employee. For each one, ask two blunt questions: how many manual steps does a person have to perform that a system could reasonably handle, and how would this look to someone who just came from a company held up as a Swiss deep-tech success story? You don't need a formal scoring framework for this. You need honesty about where the friction actually lives, because that friction is almost always where the first automation opportunity sits.

Sequence the Work So Momentum Builds

Once you've identified candidate workflows, resist the urge to tackle the most ambitious one first. The teams that build lasting internal support for AI agent and automation work are the ones who pick the smallest workflow with the clearest, most visible win — even if it's not the highest-value one on paper — and use that early success to earn the internal trust and budget for the next, larger project. A visible win in six weeks does more to unlock future investment than a promising but unfinished twelve-month initiative. This sequencing matters just as much as the technical execution itself, because internal IT credibility is built incrementally, project by project, not declared in a single roadmap presentation.

It's also worth watching how the broader enterprise software market is consolidating around fewer, more capable platforms — a dynamic covered in The Great SaaS Consolidation: Inside the 2026 Enterprise Software M&A Wave, which is directly relevant if your IT roadmap includes vendor decisions this year. Even smaller technical details compound: something as simple as making sure your internal tools and customer portals are set up correctly down to details like how to add a favicon signals attention to execution quality, and lightweight technologies like QR codes — explained plainly in How Do QR Codes Work? — are increasingly used in Swiss operational and logistics workflows precisely because deep-tech and robotics-adjacent companies have normalized their use for physical-digital handoffs.

Pricing Context: What This Kind of Work Typically Falls Under

Enterprise IT teams evaluating AI agent and automation work usually map to one of three tiers, depending on scope:

Tier	Typical scope for this scenario
Essential ($1,000)	A single scoped automation — one workflow, one AI agent, clear before/after metric
Growth ($2,000)	Multiple connected workflows, integration with existing internal systems, ongoing tuning
Enterprise ($4,000+)	Organization-wide automation layer across departments, custom agent orchestration, compliance-aware design

Most first projects for internal enterprise IT use cases in Switzerland start at the Essential or Growth tier — a single, well-defined workflow proves the model before expanding scope.

Key Takeaways

• Switzerland's deep-tech strength across biotech, robotics, climate tech, and enterprise software is structural, not a passing trend, and it's raising baseline expectations for internal IT capability.
• Enterprise IT teams face a real talent-gravity problem as deeptech and robotics companies compete for the same technical hiring pool.
• Internal tools are often neglected relative to customer-facing products — and that gap is now more visible against a technically strong market.
• AI agents and automation deliver the fastest measurable wins when scoped to one well-understood workflow rather than an organization-wide initiative.
• Deciding what to build in-house versus what to hand to a focused partner is now a strategic question, not just a staffing convenience.
• Start small, measure the result, and expand — that pattern beats a large unscoped transformation project every time.

Switzerland's deep-tech environment isn't slowing down, and internal IT teams that wait for a "bigger initiative" to justify modernizing their systems will keep falling further behind the standard their own market is setting. If you want help figuring out where to start, book a meeting with our team.

Frequently Asked Questions

What does "Switzerland's deep-tech advantage" actually refer to?

It refers to Switzerland's enduring strength across biotech, deeptech, robotics, climate tech, and enterprise software, as reflected in Swiss startup ecosystem reporting from 2026. It's a structural pattern across multiple technically demanding sectors, not a single company or funding event.

Is this trend specific to startups, or does it affect established enterprises too?

While the reporting originates from startup ecosystem analysis, the underlying strength — deep technical talent and rigorous engineering standards — affects the entire market, including established enterprises whose internal IT teams now compete for the same talent and face the same rising expectations.

Why should an enterprise IT team care about a startup ecosystem trend?

Because talent, technical expectations, and vendor standards don't stay siloed to startups. When deeptech and robotics companies raise the bar for technical execution in Switzerland, enterprise buyers, employees, and stakeholders absorb that same expectation and apply it to internal systems too.

What is an AI agent, in plain terms?

An AI agent is a software system that can take a defined task — like triaging a support ticket, reconciling data between two systems, or drafting a first-pass response — and carry it out with limited human intervention, escalating only when it hits something outside its scope.

How is AI agent work different from traditional automation or scripting?

Traditional automation follows fixed rules and breaks when conditions change. AI agents can interpret unstructured inputs (like a customer message or a scanned document) and make contextual decisions within guardrails, which makes them useful for messier, more variable workflows.

Where should an enterprise IT team start with AI agents?

Start with one well-documented, repetitive workflow that already has clear inputs and outputs — internal ticket triage, document intake, or data reconciliation are common starting points — rather than attempting a broad "AI transformation" across the organization at once.

How long does a scoped AI agent implementation typically take?

A single-workflow automation project, like those in the Essential tier, is typically a matter of weeks rather than months, since the scope is deliberately narrow. Larger, multi-workflow integrations naturally take longer as they touch more existing systems.

What does the Essential tier at $1,000 typically include?

It's designed for a single, clearly scoped automation — one workflow, one AI agent, and a measurable before/after outcome. It's the right starting point for teams testing whether AI agent automation delivers real value before expanding further.

What does the Growth tier at $2,000 typically include?

Growth-tier work covers multiple connected workflows and deeper integration with existing internal systems, plus ongoing tuning as the agent handles real-world edge cases after launch.

What does the Enterprise tier at $4,000+ typically include?

This tier covers organization-wide automation layers spanning multiple departments, more complex agent orchestration, and compliance-aware design — appropriate for larger Swiss enterprises with regulatory considerations across teams.

Does this trend mean IT teams should outsource everything?

No. It means being deliberate about which projects deserve full internal ownership and which are better handled by a focused external partner for scoped, time-boxed work — particularly in fast-moving areas like AI agents where internal teams may not have dedicated capacity to stay current.

How does Switzerland's robotics and deeptech strength connect to enterprise software?

Enterprise software is the connective layer every Swiss company depends on regardless of sector — even a robotics or biotech company relies on internal tooling, portals, and dashboards. Naming enterprise software as a strength alongside robotics and deeptech signals that software is expected to meet the same rigor.

What's the risk of doing nothing in response to this trend?

The risk isn't a sudden crisis — it's a slow erosion of internal capability relative to a market that's visibly raising its technical standards. Internal tools that look adequate today can look outdated within a year or two if peers are automating and modernizing continuously.

Is there hard data on how much of Switzerland's deep-tech activity is AI-driven specifically?

A precise figure isolating AI-driven activity within this specific trend isn't publicly available. What is clear from the general pattern is that AI and automation are increasingly embedded across these deep-tech sectors as a baseline capability rather than a differentiator.

How does this affect hiring for enterprise IT teams in Switzerland?

Competition for technical talent intensifies as deeptech, robotics, and biotech companies hire for similarly demanding roles. This makes it harder to staff every internal project purely in-house and increases the value of partners who can execute scoped technical work quickly.

What's the first workflow most enterprise IT teams automate with AI agents?

Customer or internal support ticket triage is one of the most common starting points, since it's high-volume, repetitive, and has clear success metrics like response time and resolution accuracy.

How do AI agents handle sensitive or regulated data in a Swiss context?

Well-designed AI agent implementations are scoped with data handling and access boundaries built in from the start, particularly important for Swiss enterprises in regulated sectors like finance, healthcare, or pharma-adjacent operations.

Can AI agents replace an enterprise IT team's existing internal tools entirely?

Not typically, and that's not the goal. AI agents usually sit on top of or alongside existing systems, automating the coordination and repetitive work between them rather than replacing the underlying systems themselves.

What's a realistic first metric to track after implementing an AI agent?

Time saved per task and error/rework rate are the two most common and most convincing metrics for a first automation project, since they're easy to measure before and after implementation.

How does the SaaS consolidation trend relate to this deep-tech advantage story?

As covered in the piece on the 2026 enterprise software M&A wave, the software vendor landscape is consolidating around fewer, more capable platforms — which means enterprise IT teams evaluating new tools should factor platform stability and integration depth into vendor decisions alongside any AI agent build-vs-buy choice.

Does a small favicon or branding detail really matter in a deep-tech-adjacent market?

Small execution details compound. In a market where technical rigor is the norm, inconsistencies like a missing or broken favicon signal a lack of attention that can undercut trust in more substantial technical claims — it's a small but visible proxy for overall execution quality.

Why are QR codes relevant to enterprise IT teams in Switzerland right now?

QR codes are increasingly used as simple physical-to-digital handoffs in operational and logistics contexts common in Swiss manufacturing, robotics, and industrial settings — understanding how they work helps IT teams evaluate whether they're a fit for internal or customer-facing workflows.

How should an IT team decide between building an AI agent internally versus hiring a partner?

Weigh the workflow's complexity, how quickly it needs to launch, and whether the team has dedicated capacity to build and maintain it long-term. A partner is usually the faster, lower-risk path for a first scoped project, while ongoing internal ownership makes more sense once the pattern is proven.

What happens after an initial AI agent automation proves successful?

Most teams expand from the Essential tier into Growth-tier work, connecting additional workflows and deepening integration with existing systems, based on the measurable results from the first project.

Is this deep-tech trend likely to slow down in the near future?

Nothing in the underlying pattern — research strength, engineering talent concentration, and enterprise buyer expectations — points to a near-term slowdown. Structural trends like this tend to compound rather than reverse quickly.

How does this trend affect Swiss enterprise IT budgets?

It shifts the conversation from "should we invest in modernization" to "which specific workflow do we modernize first," since the competitive pressure is now visible rather than theoretical, making it easier to justify scoped, results-driven spending.

What industries in Switzerland are most directly named in this trend?

Biotech, deeptech, robotics, climate tech, and enterprise software are the five sectors named in the Swiss startup ecosystem reporting from 2026, but enterprise software's role as connective infrastructure means the effects reach every other industry too.

Should enterprise IT teams worry about being displaced by AI agents themselves?

The realistic pattern is augmentation, not displacement — AI agents take over repetitive, well-defined tasks so internal teams can focus on higher-judgment work like system architecture, vendor evaluation, and workflow design.

How technical does an enterprise IT team need to be to adopt AI agents?

Teams don't need deep AI research expertise to adopt agents successfully — they need clear documentation of the workflow being automated and a partner or internal resource who understands how to scope and implement the agent correctly.

What's the biggest mistake enterprise IT teams make when responding to this trend?

Treating it as a reason to launch a large, unscoped "AI transformation" initiative rather than starting with one measurable workflow. Broad initiatives without a clear first win tend to stall before delivering results.

How does climate tech's strength in Switzerland relate to enterprise IT work?

Climate-tech companies still rely on the same enterprise software backbone — compliance reporting, data management, internal dashboards — meaning the technical standards set by that sector's growth apply just as much to supporting IT infrastructure.

Can AI agents help with compliance and reporting workflows specifically?

Yes, structured reporting and compliance workflows are strong candidates for automation because they follow defined rules and required fields, which AI agents can handle reliably once the workflow is properly scoped.

What's a reasonable timeline to see ROI from a first AI agent project?

For an Essential-tier scoped project, teams typically see measurable time or error-rate improvements within the first few weeks of the agent going live, since the workflow is narrow enough to evaluate quickly.

How do enterprise IT teams justify this spend to leadership?

Frame it around the specific workflow's current cost — hours spent, error rate, delay impact — and the measurable improvement expected, rather than framing it as a broad strategic AI investment, which is harder for leadership to evaluate.

Does this trend apply equally across all regions of Switzerland?

The reporting reflects the Swiss startup ecosystem broadly rather than singling out specific cities or cantons, so the competitive and talent pressures described apply nationally rather than to one particular region.

What's the relationship between robotics growth and internal automation demand?

As robotics companies normalize automation in physical processes, it becomes a natural mental model for enterprise buyers and stakeholders to expect similar automation in digital and administrative processes too.

How should an IT team prioritize which workflow to automate first?

Prioritize workflows that are high-volume, well-documented, and currently consuming disproportionate manual time — these offer the clearest measurable win and the lowest implementation risk for a first project.

What ongoing maintenance does an AI agent require after launch?

Agents typically need periodic tuning as edge cases surface in real usage, plus monitoring to confirm accuracy holds over time — this is usually covered under Growth-tier ongoing engagement rather than a one-time build.

Are there specific risks in deploying AI agents in regulated Swiss industries?

Yes — any agent handling regulated data (financial, health-adjacent, or personal data) needs clear boundaries on what it can access and act on, and should be designed with audit visibility in mind from the start.

How does Scult's AI Agents & Automation service fit into this?

It's built around scoped, measurable automation projects — starting with one workflow, proving the result, and expanding — which matches how enterprise IT teams in Switzerland can realistically respond to rising technical expectations without overcommitting resources upfront.

What's the difference between a chatbot and an AI agent in this context?

A chatbot typically just responds to queries, while an AI agent can take action — updating records, routing tickets, triggering downstream processes — based on what it interprets from the input, making it more directly useful for operational workflows.

How do enterprise IT teams measure whether an AI agent is working well?

Track the specific metric tied to the workflow's original pain point — time to resolution, error rate, manual escalation frequency — and compare it before and after deployment over a consistent measurement window.

What's a common technical blocker when implementing AI agents in enterprise environments?

Integration with legacy internal systems is the most common blocker, since older systems may lack modern APIs — this is usually addressed during scoping rather than discovered mid-project when the implementation partner audits existing infrastructure first.

Is this deep-tech trend more relevant to large enterprises or smaller companies?

It affects both, but larger enterprises tend to feel the talent competition more acutely, while smaller Swiss companies feel the expectations pressure — customers and partners expecting the same technical polish regardless of company size.

How does the SaaS consolidation wave affect vendor choice for AI agent tooling?

As covered in the SaaS consolidation piece, fewer, larger platforms are absorbing smaller point solutions, so IT teams should factor a vendor's acquisition risk and platform stability into any multi-year automation tooling decision.

What role does documentation play in a successful AI agent rollout?

Clear documentation of the existing workflow — inputs, decision points, exceptions — is the single biggest factor in how quickly and accurately an AI agent can be scoped and implemented, regardless of which tier of engagement is used.

Should enterprise IT teams build AI agent capability in-house long term?

Some organizations eventually build internal capability once they've proven value with a few scoped projects, but even then, many continue partnering for new workflow types where speed matters more than long-term internal ownership.

How does this trend affect customer-facing systems versus internal tools?

Both are affected, but internal tools are often the more overlooked area since they don't face external scrutiny — this trend is a useful prompt to apply the same technical standard internally that customer-facing products already receive.

What's the realistic cost of ignoring this trend for another year?

There's no single dollar figure to cite here, but the pattern is clear: technical debt in internal systems compounds, talent competition intensifies, and the gap between your organization's tooling and the surrounding market's standard widens the longer it's left unaddressed.

How can an enterprise IT team get started evaluating AI agent opportunities?

Start by listing the two or three most time-consuming repetitive workflows on your team, estimate current manual hours spent, and use that as the basis for a conversation with an implementation partner about a scoped first project.