The AI Productivity Platform: Making AI Work at Scale

Across industries, investment in artificial intelligence has surged, yet despite billions spent most enterprise programmes remain stuck in pilot mode. Proof-of-concepts proliferate but real operational transformation remains elusive. 

Readers may recall a recent MIT paper claiming that 95 percent of AI projects fail to return ROI. Now, whether or not every figure in that report stands up to scrutiny is beside the point — it resonated because it mirrors the lived experience of enterprise leaders who still feel they have yet to realise the promise of agentic AI.

What lies behind these disappointing outcomes is not a lack of ambition but a set of structural blockers. Monolithic SaaS tools remain costly, rigid and poorly adapted to the complexity of real work. Models are often disconnected from the proprietary data that would make them accurate and trustworthy, leading to hallucinations, sycophancy and compliance concerns. Interfaces and workflows are designed for generic use cases, depressing adoption and driving up training costs. Worse still, too many initiatives remain piecemeal — isolated proofs of concept that never scale across silos or evolve into systemic change. The result is a landscape of abandoned pilots and mounting frustration rather than transformative productivity gains.

Worse, the market has followed a scorched-earth approach: overpromising transformation, underdelivering results, and leaving behind a trail of half-integrated tools and disillusioned teams. Too many initiatives remain piecemeal — isolated proofs of concept that never scale across silos or evolve into systemic change. The result is a landscape littered with pilots, not productivity.

It does not have to be this way. Here at Elsewhen we have developed a different path: the AI Productivity Platform — a layered, agentic approach that enables enterprises to map, build, scale and evolve their AI capabilities while avoiding the pitfalls of blunt SaaS and vendor lock-in. Split across three interdependent stages — AI Activation, AI Integration, and the Agentic Enterprise — this whitepaper charts how organisations can move from isolated pilots to systemic transformation.

Because at the end of the day, it’s not about AI for its own sake; it’s about harnessing intelligence to deliver measurable, even radical, productivity at scale.

Leon Gauhman | Co-Founder, Chief Product & Strategy Officer

The four blockers to successful enterprise AI

Most enterprise AI initiatives fail for the same underlying reasons. At the surface level the problems look technical — models that hallucinate, integrations that lag, user adoption that never quite materialises — but underneath lies a deeper structural mismatch between today’s tools and the complexity of large organisations. 

First, incumbent, generic SaaS platforms are marketed as “AI-ready” or “agentic,” yet in practice these systems are costly, rigid, and poorly aligned with the real workflows they claim to transform. Worse still, Gartner now estimates that out of thousands of vendors using the “agentic AI” label, fewer than 130 are genuine — a stark reminder of how far marketing has outpaced capability. This wave of agentwashing has left organisations cautious and often trapped in expensive dead ends.

Second, even when models are powerful, they are often disconnected from the proprietary data that would make them accurate, compliant and trustworthy. Without that grounding, they operate with limited context, producing answers that seem convincing but lack substance. The result is a higher risk of hallucination — fabricating information to fill the gaps it cannot see. This creates confident but unreliable systems: the appearance of intelligence without the dependability enterprises require.

Thirdly, and compounding this further still, is the user experience: research has repeatedly shown that tools designed for broad use cases are by their nature not designed for the specific tasks at hand, creating friction in adoption and driving up change-management costs. As a result employees retreat to familiar manual processes, leaving the promised gains unrealised.

Finally, enterprise AI has been treated as a collection of experiments rather than the development of a new operating model. Isolated proofs of concept proliferate, each with its own budget, vendor and architecture, but few ever scale. Siloed initiatives fail to reinforce one another, and the lack of orchestration means that even successful pilots plateau instead of building towards something truly transformational.  

On top of all this, the uncomfortable truth is that many consultancies implementing enterprise AI simply aren’t up to the task. The boom has exposed deep cracks in the traditional consulting model — firms optimised for process and billing rather than engineering or product delivery. Too often, they’re reselling Microsoft Copilot licences or rebadging SaaS integrations instead of building real, domain-specific systems. In The Wall Street Journal, enterprise leaders from Bristol Myers Squibb, Merck Healthcare, and AmeriSave described how Big Four consultancies struggled to turn GenAI pilots into scalable outcomes, with some even admitting they were “learning on the client’s dime.”

Deloitte’s refund to the Australian government after an AI-generated report riddled with errors underscores the same issue: those selling AI transformation often don’t understand the technology well enough to deliver it, falling into the avoidable traps that non-deterministic technologies entail. 

Taken together with these four blockers, it becomes clear why industries are awash with announcements yet short on measurable productivity gains — and why a new approach is needed, one that starts with an organisation’s own data and workflows and builds upward from immediate wins to systemic transformation.

95%

of generative AI projects fail to return on investment

~35%

success rate of Salesforce’s agents on multi-step tasks

42%

of companies abandoned most of their AI initiatives in 2025

80%

of AI projects fail

The four elements behind successful enterprise AI

For decades, enterprise transformation has meant entering a blank space and filling it with structure — replacing paper trails with databases, manual workflows with digital tools, and absence with something “better than nothing.” That logic worked for digital transformation. It doesn’t work for AI.

The past two years have made that clear. Dropping a large language model into an existing system or adding a chat interface on top of legacy software doesn’t create transformation — it just creates another layer of complexity. True productivity doesn’t come from adding more tools; it comes from rethinking how work itself gets done in collaboration with a machine.

And this machine is unlike anything before it. It can understand language, code, and images; it can analyse, summarise, and propose solutions for a human to act on. The challenge — and the opportunity — is to redesign the system of work so that human and machine operate in convergence, not in sequence.

Central to this shift are AI agents — systems that don’t just assist, but act. These autonomous, context-aware components are the mechanism through which intelligence becomes operational. They turn insight into execution, coordinating across workflows and systems to deliver measurable outcomes.

With agentic AI, for the first time, intelligence isn’t something added to the system — it becomes the system itself. The connective fabric through which work flows, decisions are made, and productivity compounds.

To build an enterprise that works this way requires a different foundation: one that is tailored to the organisation, grounded in its data, adaptive in its interfaces, and capable of reasoning and acting autonomously across multi-stepped tasks continuously.

Here are the four key elements that underpin genuine AI productivity.

Built for you

Moving beyond one-size SaaS to systems tailored to your unique context.

Intelligence is now a commodity. The real advantage no longer comes from access to models — whether large or small — but from shaping that commodity to fit your organisation’s unique needs.

Every enterprise has its own data models, workflows, and constraints. Generic SaaS tools overlook that complexity, forcing teams to work around the software instead of through it. The result is inefficiency, duplication, and mounting frustration.

“Built for you” means creating AI systems that connect seamlessly to your existing infrastructure — enhancing what already works rather than demanding costly rip-and-replace. The outcome is targeted ROI without SaaS bloat: intelligence that runs inside your own stack, under your governance, on your terms.

You own the IP, you control deployment, and you keep the freedom to evolve — without hidden lock-ins eroding value over time.

As model performance continues to advance, an important trend has become clear: frontier systems are converging. Across benchmarks, the leading models now reach broadly similar levels of capability, with the gap between them narrowing over time. This means the strategic advantage no longer lies in selecting one model over another, but in how that intelligence is shaped — grounded in your data, integrated into your workflows, and orchestrated across your organisation. The chart below illustrates this convergence, highlighting why differentiation now depends on the system you build around the model, not the model itself.

Grounded Intelligence

AI that knows your business because it runs on your data.

Grounded intelligence ensures that AI is accurate, verifiable, and contextually aware. Rather than relying on generic datasets, it connects models directly to live enterprise information — from PDFs and wikis to transactional systems — ensuring agents operate with compliance and trust.

Through protocols like the Model Context Protocol (MCP) and Agent-to-Agent (A2A) communications — complemented by foundational tools such as retrieval-augmented generation (RAG) and knowledge graphs — we build systems that understand meaning and relationships, not just keywords.

When intelligence is grounded in your data, it becomes not only more useful but also more accountable.

Generative UI

Tools that fit the user, not the other way around.

Most enterprise AI tools stop at the chatbot. Generative UI goes far beyond that. Instead of slapping a chat window onto an existing interface, it creates the interface itself — in real time — generating layouts, forms, and workflows that adapt to the task at hand.

This isn’t about asking a model to “help you” — it’s about the model shaping how the work gets done. Using large language models to dynamically compose elements across text, image, data, and action, Generative UI transforms AI from a passive assistant into an active collaborator.

The result is a new kind of software: one that builds itself around your people and their work. No steep learning curves. No endless dashboards. Just fluid, contextual interfaces that appear when and where they’re needed. It is AI that meets the user, not the other way around.

Agentic Enterprise

From using AI to running on it.

The agentic enterprise isn’t about adding smarter tools — it’s about creating a system that runs itself. Here, AI doesn’t wait for prompts; it acts, coordinates, and improves continuously. Specialised agents work across departments and systems, automating decisions, resolving tasks, and surfacing insights — all while humans stay firmly in control, guiding outcomes rather than managing inputs.

This shift changes the nature of work itself. Processes that were once linear become adaptive; human teams move from execution to orchestration. Oversight stays human, but the loop between intent, action, and result becomes near-instant. AI doesn’t just inform decisions — it carries them out safely, at scale, and with accountability.

In doing so, AI becomes the connective tissue of the organisation — orchestrating workflows, powering decisions, and enabling resilience through distributed intelligence. This is when AI stops being a tool and becomes part of the system itself: the backbone of productivity, innovation, and adaptability.

The AI Productivity Platform

With all this in mind, the question becomes how to turn intent into structure — how to build a system that compounds value rather than starting from scratch with every use case. We call it a platform because it is a foundation: a way to organise intelligence, data, and workflow into a living system that gets smarter over time. It’s not a single product or one-size-fits-all solution, but a framework that enterprises can build upon and extend as their capability matures.

The AI Productivity Platform is not a rigid sequence of steps; it’s a compounding system. The three layers — AI Activation, AI Integration, and Agentic Enterprise — operate in parallel, reinforcing one another as capabilities mature. Each represents a different altitude of focus: Activation delivers immediate productivity,I integration builds the technical and data foundations for scale, and the Agentic layer ensures coordination, learning and adaptability across the enterprise. Progress happens simultaneously across all three — the point is not to wait, but to evolve continuously.

AI Activation

AI Activation is about turning intent into impact — fast. Here, enterprises deploy working agents quickly to prove value in live environments. Feasibility pilots are replaced by production-grade agents embedded in real workflows — automating handoffs, surfacing insights, and freeing teams to focus on higher-value work. Early wins build confidence and generate the operational data needed to inform broader transformation.

Key Goal: Deliver measurable value in weeks, not years. Prove that AI can enhance real work.

Key Question: Where can an agent make an immediate impact in your current workflows?

AI Integration

As soon as the first agents go live, the real picture of what needs to change starts to emerge. AI Integration is the work of building the foundations that allow those early wins to scale — ensuring the right access, the right data pathways, and the right environments are in place as value begins to materialise.

This phase doesn’t run ahead of Activation; but in parallel Early deployments surface the gaps: data that needs cleaning, systems that need modernising, interfaces that need unifying. Integration addresses these one by one — upgrading pipelines, improving governance, and preparing enterprise systems for safe, reliable, multi-agent operation.

The result is an infrastructure that compounds value: every new agent can plug into a cleaner, more connected, more governed ecosystem, rather than standing alone.

Key Goal: Build the foundation for scale — ensuring reliability, security and interoperability. 

Key Question: How ready is your infrastructure to support multiple agents operating across teams and systems?

The Agentic Enterprise

At the same time, the principles of the Agentic Enterprise begin to take shape. Multiple agents coordinate with each other and with human teams, exchanging context through shared protocols and knowledge graphs. Processes that were once linear become adaptive, adjusting to changes in workload, regulation or business goals in real time. This is where AI stops being an assistant and starts becoming part of the operating model — autonomous, collaborative and self-improving.

Key Goal: Transition from automation to autonomy — creating a self-improving system of work.

Key Question: Where do the biggest opportunities lie for agents to take initiative, collaborate, or make decisions autonomously?

FAQs

1. What is the AI Productivity Platform?

The AI Productivity Platform is Elsewhen’s AI consulting and integration frameworkframework for helping enterprises turn AI from isolated pilots into a scalable operating system. It’s a layered approach — spanning activation, integration, and agentic orchestration — designed to build productivity, not just prototypes.

2. Why do most enterprise AI projects fail to scale?

Over 80% of AI projects fail because they rely on generic SaaS tools, disconnected data, and siloed pilots that never integrate into core workflows. Without context, orchestration, and user adoption, even strong models produce little measurable value.

3. What makes an AI system “agentic”?

Agentic AI and RAG-based agentAgentic AI refers to systems composed of autonomous agents that can reason, act, and collaborate — both with humans and with each other. Using protocols like Model Context Protocol (MCP) and Agent-to-Agent (A2A), agentic systems coordinate across tools and data to deliver real outcomes.

4. How does Elsewhen’s approach differ from traditional AI consulting?

Traditional consulting firms focus on proof-of-concept projects and time-based billing. Elsewhen builds outcome-led systems that run in production, integrate with live enterprise data, and are fully owned by the client — no vendor lock-in, no black boxes.

5. What’s the first step to building an AI-powered enterprise?

It starts with identifying high-value use cases where AI can create measurable productivity — then mapping how those agents integrate with data, systems, and teams. Elsewhen helps organisations move from pilots to platforms through modular squads that deliver fast, compounding results.