We are not in a technology crisis. We are in the middle of a trust crisis. For AI to truly transform the enterprise, it must cross the last mile: not into code, but into culture. This white paper explores why 42% of GenAI pilots failed in 2024 – not due to technical limitations, but due to psychological resistance. Through the lens of the H.U.M.A.N.E. framework, we reveal how trust, not just tech, has become the new infrastructure of transformation.
- The Great Divergence: Capability vs. Confidence
There comes a moment in every era when humanity faces a new force – a transformative leap, a moment of reinvention. Once, it was fire. Then the wheel. Later, electricity and the microchip. In this decade, it is artificial intelligence (AI). But for all its computational prowess and intelligence – with machines that can synthesize global financial trends, automate diagnostic medicine, simulate legal reasoning and even write poetry – AI does not understand the human heartbeat. It doesn’t yet comprehend fear, resistance, pride, hesitation, or hope.
This is why, as AI rises, we see a divergence in how organizations respond. Some organizations boldly embrace AI and transform rapidly, while others hesitate, held back by fear and uncertainty, creating a divide between capability and confidence, where some have the skills to lead and others lack the belief and confidence to follow. Two categories of organizations that are polar opposites in terms of the speed of AI adoption are:
- AI-Native Enterprises: Proactive Champions / Early adopters
These firms don’t adopt AI; they architect around it by committing to make all the internal changes required to integrate AI into their daily activities:
- Legacy-Reactive Enterprises: Caught in POC Purgatory
They invest in pilots but stall in scale. They are still stuck in PoC purgatory, where employees resist adoption because of their fear of obsolescence – AI success is judged by quarterly ROI instead of long-term capability-building and grappling with poor data infrastructure. Its over-optimism followed by rapid disillusionment due to change management deficit.This is not a technology lag, it is an organizational trust deficit.
Source: https://www.gartner.com/en/research/methodologies/gartner-hype-cycle
This can be well explained using the stages of the Gartner Hype Cycle in the context of AI:
Technology Trigger – AI breakthroughs (like ChatGPT, generative models) emerge, sparking curiosity, excitement, exploration and experimentation.
- Peak of Inflated Expectations – Hype surges as businesses expect AI to solve everything instantly. AI is hyped as a magic solution; bold promises outpace real-world integration.
- Trough of Disillusionment – Reality hits, AI adoption stalls due to cultural, structural, and execution gaps. Disappointment sets in as organizations realize adoption is hard and it’s not just about the model, but the mindset, change management, and culture. It disguises itself as a lack of belief or conviction in AI’s real utility
- Slope of Enlightenment – Organizations begin to learn where AI truly fits in and how to integrate it responsibly. Realistic use cases emerge, and companies start aligning AI with business goals and workflows
- Plateau of Productivity – AI becomes normalized and delivers consistent, real-world value across workflows. It becomes a trusted tool, delivering measurable value across industries at scale
Despite technological progress, many AI pilots fail—not due to poor models, but due to organizational unpreparedness, lack of internal belief, overhyped expectations and missing change management frameworks. The initial excitement gives way to reality, and it’s this psychological and cultural gap that pushes us into the trough. We are entering the Trough of Disillusionment and this time the drop isn’t technological, it’s psychological.
To quote a CEO of a tech firm based in San Francisco – “I’ve spent money on this. It’s not happening.” – captures the frustration of investing in AI without seeing transformational outcomes.
1. Why 42% of GenAI Pilots Failed in 2024
According to S&P Global, failure rates in GenAI pilots soared from 17% (2023) to 42% (2024). This is not due to poor models, but due to the lack of internal readiness and poor change management.
Without change management, even the smartest AI will crash into resistance – human doubt, skepticism, and fear. The fundamental truth is this: many organizations want AI’s outcomes but haven’t accepted AI’s implications. They overlook the internal rewiring of roles, incentives, workflows and expect employees to carry on to the future without ever being invited into its design. The disconnect between desire and preparedness often surfaces in conversations that seem forward-looking but lack foundational clarity.
One of our BFSI clients recently asked, “Can you suggest an AI solution for us? We want to integrate it to improve company performance everyone around us is doing it.” It wasn’t a question driven by a real need, but by FOMO: a fear of missing out, not a fear of missing results. There was no clarity on the problems they wanted to solve, no discussion of pain points, workflows, or business friction. The motivation was external, not internal – driven by trend pressure, not transformation intent but ambition to be a part of the AI fad. This reflects a foundational misunderstanding: AI is not a feature you plug in. It’s a capability you build – gradually, systemically, through clean data, contextual models and human-centric organizational readiness.
This is also a classic case of brand mimicry – adopting the language of innovation without investing in the substance of differentiation. The organization wants to be perceived as future-ready, without reshaping its internal logic to truly become so. To move from “We want AI because everyone else is doing it” to “We are ready for AI because it solves something real for us,” both the organization and its people need to evolve – in mindset, structure and workflow.
Let’s understand the four barriers of adoption that is more evident in the repeated patterns we now see across industries.
2. Four Barriers to AI Adoption
Below are the most common and avoidable reasons why organizations fail to scale AI beyond the pilot phase and why many now find themselves in the Trough of Disillusionment.
a. Lack of Strategic Alignment
Chasing Hype, Not Value – Many pilots are exploratory (“let’s try AI”) rather than outcome-driven, they are driven by FOMO. Too many companies were seduced by headlines instead of use cases. They rushed to “do AI” without first asking why or what business problem will it solve. Initiatives were often exploratory, driven by hype instead of clear business problems, with no North Star to guide them. Implementations happened in isolation from core operations. As a result, pilots run in silos, without strong collaboration between business and IT, or KPIs linked to revenue, cost, or risk. The result? Expensive experiments, with no ROI or business alignment. Senior leaders expect immediate ROI, but when results don’t materialize fast enough, support fades. AI is treated as a fad rather than a strategic capability to mature over time – leading to disjointed investments, low impact and early-stage disillusionment. AI isn’t a magic button. If there’s no pain point, there’s no need for a solution.
b. Data Readiness Challenges
Garbage in, hallucinations out – AI is only as powerful as the data behind it and for many organizations, that foundation is shaky at best. Fragmented, inconsistent and siloed data remains the norm, often buried within legacy systems that don’t talk to each other. Without robust data governance or modern engineering infrastructure, even the most advanced models are trained on flawed inputs. Add to that the constraints of regulatory compliance like GDPR and HIPAA and data becomes not an asset, but a barrier. In the end, AI doesn’t fail because it lacks intelligence; it fails because it’s fed chaos.
c. Off-the-Shelf Syndrome – Buying Off-the-Shelf, Expecting Tailor-Made Results
One size does not fit all – Organizations assumed that tools built for the masses would work flawlessly for their specific needs. But off-the-shelf solutions often lack the customization, flexibility and deep integration required for complex environments. Enterprise transformation demands custom-fit solutions, not one-size-fits-all tools. Off-the-shelf AI products lack the depth, flexibility, and integration necessary for complex, enterprise environments.
Without internal ownership or frameworks to identify and scale the right use cases, efforts remain fragmented and superficial. Add to that the “shiny tool syndrome” – chasing LLMs, copilots, and dashboards without a strategy and AI becomes a scattered failed experiment rather than a coherent transformation. True impact lies not in adopting more and more hyped tools, but in aligning the right ones to real business depth and customize them as per organization needs. Pilots are easy; next embedding AI into workflows like daily activities requires human alignment.
d. Talent & Change Management Deficits
AI transformation isn’t just technical, it’s cultural too. Organizations often underestimate the depth of change required across people and processes. Employees might resist the adoption of AI for fear of becoming irrelevant in the workplace. Without clear communication, leadership alignment, or upskilled cross-functional teams that can bridge domain and AI, the result is confusion and inertia. Even the best AI systems stall when they’re dropped into an unprepared human system. Adoption isn’t driven by code – it’s driven by trust. AI introduces a cultural shift, not just a technological one. Yet most organizations fail to communicate the “why,” “how,” and “what” it means to employees. Fear, resistance and confusion take over and adoption stalls as ROI stagnates. Without clear communication and leadership buy-in, even the best AI systems will gather dust.
4. Introducing the H.U.M.A.N.E. Framework – From Friction to Smooth Flow
A people-first system for AI transformation that overlays onto technical stacks like LangChain, Pinecone, Azure ML.
5. Call to Action: From Adoption to Workflow Integration
The Way Forward
More than ever before, we need human-aligned, context-driven, use-case focused custom AI Solutions that speak the language of industries and integrate into workflows, thereby earning trust. To transition from the Trough of Disillusionment to the Slope of Enlightenment, enterprises must shift their operating model:
- Shift from tech-centric to human-centric AI
- Move from pilots to platforms infused with trust and HUMANE
- Start solving business problems
- Stop chasing hyped up off-the-shelf tools and start solving specific business pain points using custom AI solutions. For example, vertical LLMs custom designed for the organization needs and data infrastructure
- Reframe and shift the narrative from “jobs vs AI” to “jobs with AI”
- Build data and workflow trust from within teams, including strategic and human resources alignment
- Adopt AI not as a one-time deployment, but as a continuous capability-building journey
6. Cortex Intelligence Suite: Trust & Industry-Calibrated AI Solutions
And that’s where we come in – The Cortex Intelligence Suite: Industry/ sector-specific company calibrated custom fit AI solutions
At AGR, through the HUMANE lens and the Cortex Intelligence Suite, we offer custom-fit, sector-specific AI products designed to meet enterprises exactly where they are and lead them into the profitable future.
Custom-fit AI tools for sector-specific adoption:
- Cortex360 BFSI: Enables instant querying, summarization, and reporting of complex financial documents like private equity reports
- Cortex360 Pharma: Empowers pharma retail and franchisee operations with AI-driven insights across sales performance, inventory optimization, stockout alerts, assortment planning and field force effectiveness, all in one intelligent query interface.
- Metal & Mining Insights: offers intelligence on metal premiums, global pricing spreads, recycling trends, company exposures, and supply chain dynamics, enabling actionable insights through natural language queries across commodities like metals for example – aluminium, copper, lithium, iron ore and others.
- GlobalBiz: Enables real-time newsletter generation by automatically scraping and summarizing the latest industry-specific articles within a chosen time frame across your sector of interest.
- DashCanvas: Prompt-to-dashboard for no-code insight generation
Unveiling Cortex Intelligence Suite’s Diverse Applications
Let’s move beyond deploying intelligence and design human‑centric, purpose‑built AI solutions.
Let’s build AI the H.U.M.A.N.E. way!
“The real transformation isn’t in the machine. It’s in us.”
About the Authors:
Sonia Laskar – Manager AI/Tech
With nearly four years of experience in data analytics, digital marketing products, and project management, she has contributed to SaaS product launches, data visualization initiatives, marketing campaigns, and the successful implementation of ISO 27001 and 9001 certifications. She holds a Post Graduate Program (PGP) in Data Science & Analytics and thrives in leading teams to solve business problems through practical, data-driven AI solutions.
Ankur Rastogi – President – Technology & Innovation
Ankur Rastogi heads the Technology and Innovation Practice at AGR, driving the design and delivery of bespoke AI platforms tailored to diverse client needs across industries and geographies. He has consistently delivered transformative, client-centric technology solutions that enable measurable business impact.
