“Perfection is achieved not when there is nothing more to add, but when there is nothing left to take away.”
That line from Saint-Exupéry was about airplanes, but it might as well be about today’s enterprise tech stacks.
Over the last decade, we’ve added tools, dashboards, platforms, bots, and now “AI assistants” to almost every corner of the business. Each addition made sense at the time. Together, they’ve created a complexity beast that quietly eats productivity, customer experience, and P&L.
The complexity beast is real
Employees are managing intense work: deadlines, coordination, emotional highs and lows, and customers whose expectations now move at the speed of ChatGPT. On top of that, they’re asked to juggle a growing constellation of systems that rarely feel like one system.
Individually, most tools are fine. The problem is the gaps between them:
- Context doesn’t travel, so the same information is re-entered over and over.
- Signals are scattered, so decisions are slower and often made on partial data.
- Features people actually need are buried under license tiers no one has time to learn.
- Engineering teams spend their time wiring up yesterday’s point solutions instead of building tomorrow’s capabilities.
This is fragmentation. And fragmentation is where value leaks first.
Customers have already moved on
Outside the organization, people live in a different world. They type or speak an intent and expect a relevant answer, not a menu. They don’t want to click through ten screens to get something done. They expect systems to remember context, anticipate needs, and do more of the tedious work for them.
Inside many enterprises, the experience is the opposite: more forms, more tabs, more dashboards. The gap between external expectations and internal reality gets wider every quarter.
The opportunity: convergence plus a new AI layer
The good news: the technology now exists to subtract complexity, not just add to it.
The next wave is not “AI sprinkled everywhere.” That only accelerates the chaos. The next wave is an orchestration layer that sits across tools and channels and makes the work feel lighter. Think of it as a click-light, intention-based spine that lets intelligence follow employees and customers across moments, instead of forcing them to hunt for it.
At Vivaldi we think of this as building systems that can:
- Sense what’s happening across interactions and signals
- Reason in full context using clear policies and guardrails
- Act by executing tasks in-flow (auto-logging, enrichment, summaries, routing, drafts, approvals)
- Learn from outcomes so the system gets better every week
This SRAL loop is the difference between “AI features” and an intelligent system.
When SRAL runs across journeys, tools stop pretending to be the system. They become components of one system. That’s where P&L savings and future growth come from: less friction inside, more value outside.
Design Standards for the Agentic, Interface-agnostic Era
AI changes the “where” of experience. Users no longer want to navigate screens to find answers; they expect answers to find them. As agentic systems mature, the primary interface becomes intention and conversation. That shifts the design requirement from crafting pages to crafting behaviors.
Three practical design standards define whether an AI system will scale adoption or stall:
1. Interface-agnostic architecture
The system must deliver value consistently across chat, voice, email, mobile, CRM, and embedded product surfaces. If the architecture assumes a single UI, it will collapse under platform reality. Intelligent systems need a shared orchestration layer that can render outcomes in any interface.
2. Click-light, intent-first workflows
Reduce switching, forms, and manual upkeep. Admin tasks like logging activity, summarizing interactions, enriching records, or filing expenses should be automated by default. People don’t want another dashboard — they want help getting the work done.
3. Evaluation-driven system design
AI systems must be continuously measured against real outcomes: accuracy, latency, relevance, hallucination rates, completion rates, and adoption friction. If an engineering partner cannot articulate how they will evaluate and improve the system in production, they’re building a demo, not a durable capability.
The next wave of competitive advantage will not come from deploying more tools. It will come from designing systems that feel invisible because they remove burden — and that improve themselves because they are measured, governed, and adaptive.
What Leaders Do Next (Practical, not theoretical.)
So how do you move from complexity and leakage to an intelligent, compounding system? A few concrete moves:
1. Start with the moments that matter
Don’t start with tools. Start with episodes in the lives of your customers and employees: onboarding, renewal, support, planning, decision moments. Within each episode, identify the specific moments where people feel the most friction or the most value. That’s where orchestration belongs first.
2. Quantify leakage with telemetry, not anecdotes
Measure how the current system behaves before you change it:
- licensed vs. used features (by role, by business unit)
- time-in-motion on manual upkeep (logging, reconciling, re-entering)
- integration debt (partial, brittle, or duplicated integrations)
- overlapping tools that serve the same job
Convert each into dollars and hours, not just “adoption scores.” That’s your value-leakage baseline and your business case.
3. Define a shared backbone
In complex enterprises, variation is inevitable — but it should be intentional. Define one cross-enterprise backbone for how work should flow, and allow variation only where it clearly adds value for a segment, region, or product.
One best way doesn’t mean one BU, one motion, one customer. It means one spine with smart branches.
4. Pilot the orchestration loop in a single high-value episode
Pick one episode where the stakes and the pain are both high. Design the SRAL loop end-to-end:
- what the system must sense
- how it will reason (with guardrails)
- which actions it will take automatically
- how it will learn from the outcome
Run that pilot in the real world with real users. Prove that work feels lighter, outcomes improve, and leakage shrinks. Then scale systematically to adjacent episodes.
5. Govern for convergence
Without governance, fragmentation regrows. Someone needs clear decision rights over:
- which tools are core vs. local
- which integrations are mandatory vs. optional
- which moments get orchestration investment first
- how success is measured and how quickly overlap is retired
Governance isn’t bureaucracy; it’s what makes compounding possible.
So what future do you see?
We’re at an inflection point. One path is familiar: keep layering new features on top of old complexity and hope training can close the gap. The other path is harder—and far more rewarding: use AI to subtract complexity, converge tools around an orchestration layer, and free employees to focus on work that actually grows the business and deepens customer value.
At Vivaldi, we see that second path becoming the new standard:
- huge productivity gains from systems that carry the admin load,
- clearer P&Ls as value leakage is made visible and reduced,
- and a stronger foundation for new products, services, and business models built on top of a coherent, intelligent system.
The question is no longer whether AI will reshape your operating model. It’s how much complexity you’re willing to subtract so that intelligence can really flow.
What future do you see for your organization: more tools, or fewer steps?
If you’re ready to explore what an orchestrated, agentic, click-light system could look like for your customers and your people, we’d love to continue the conversation.