For nearly three years now, Generative AI (GenAI) has captured the imagination of businesses around the world, promising to transform customer experiences, improve productivity, and unlock new revenue streams.
However, today many large companies are struggling with the reality behind the hype. Market research and advisory firms place GenAI firmly in the trough of disillusionment as companies realize its true potential and limitations.
Executive Vice President of HCLTech.
Investment continues across the industry, but many companies are frustrated by the slow pace of tangible returns. At this critical stage, senior business and technology leaders are asking how they can manage GenAI deployment and scale-up to deliver real business value and avoid becoming part of the 30% of GenAI projects that Gartner predicts will be abandoned by 2025.
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What challenges do companies face when scaling GenAI?
Large enterprises rushing to adopt GenAI face many real challenges, including poor data quality, poor risk management, rising costs, and unclear business value, which can derail projects before they reach production.
A major hurdle is the mismatch between investment and immediate returns. Another important issue is organizational readiness. Many companies lack the data infrastructure and AI literacy to support GenAI at scale.
Less mature organizations struggle to identify the right use cases and have unrealistic expectations, while more mature companies face talent shortages and need to instill GenAI literacy across their teams. Ensuring data quality is also a persistent challenge, as GenAI systems, like other AI models, are dependent on the quality of the data used to train them.
Insufficient data makes the output unreliable. Governance and risk management are often playing catch-up, with early adopters facing issues such as model illusions, bias, and new regulatory compliance, such as advanced and legally binding EU AI legislation.
All these challenges highlight that GenAI adoption is not a pure technology challenge, but also a people and process challenge. Siled innovation efforts run the risk of stalling without cross-functional buy-in, and lack clear business outcomes if projects are driven in isolation from business needs.
How can organizations avoid GenAI project failure and increase value?
To move GenAI initiatives from pilot to production, companies must take a strategic, value-driven approach from the beginning. First, it is essential to establish a clear business case and success metrics.
Rather than deploying AI for AI’s sake, companies should start by identifying high-impact use cases where GenAI can solve real problems or deliver measurable improvements, such as reducing customer service wait times or automating costly manual processes.
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At the same time, organizations must rigorously analyze the total cost and potential business value of an initiative upfront to make informed investment decisions.
Another best practice is to foster strong collaboration between departments from day one. A successful GenAI program will break down silos between IT, data science, business units, and risk management.
This cross-functional approach ensures that technical teams understand the business context and value drivers, while business stakeholders stay informed about AI capabilities and limitations. Fostering collaboration between teams empowers people at all levels to make informed decisions and drive innovation together.
One approach is to establish an “AI Council” or similar governance body comprised of representatives from multiple sectors. This organization can champion initiatives, align them with corporate strategy, and monitor ethical and compliance considerations.
Equally important is managing the culture and change aspects. GenAI often extends or redefines jobs and processes, so organizations need to prepare their workforces. This means upskilling and change management so that employees can trust and use AI tools effectively.
Some early adopters find it useful to start with a pilot project involving end users and iterate based on feedback. Demonstrating small wins helps build momentum and build buy-in. In today’s climate of heightened expectations, setting realistic milestones and celebrating incremental progress can help prevent disillusionment.
While the hype may have promised instant value, in reality, success with GenAI comes from a set of well-placed, value-driven steps.
What frameworks can help you successfully deploy GenAI at scale?
Deploying GenAI in large enterprises requires structure. Companies need an operating model that can take AI from ideation to industrialized impact by enabling multidisciplinary teams to remain agile without sacrificing safety or accountability.
Many companies use product-aligned operating models to connect AI efforts to business outcomes.
An effective way to guide AI adoption is to apply a three-stage framework from pilot to production.
The first phase, Discovery and Baselining, focuses on understanding your enterprise’s readiness and opportunities. This includes assessing your current data landscape, technology stack, and AI maturity, as well as identifying priority use cases through workshops with business leaders.
The goal is to define the problem, align on success criteria, and build a common understanding across stakeholders.
The second phase, Tools and Design, handles the heavy lifting of building the solution. Here, organizations choose the right tools and models and design their solutions with scalability, security, and governance in mind.
This includes setting up cloud or on-premises infrastructure and integrating GenAI models with business workflows. Design also extends to user experience. For example, how GenAI-powered assistants will be integrated into employees’ daily tools.
The final phase, ROI and scaling, is about proving value and scaling up what works. During this phase, the GenAI solution is deployed in a real-world environment, often starting with a limited scope or group of users, and rigorously measured against the KPIs established during the exploration phase.
If outcomes meet or exceed goals, organizations can confidently expand the use of AI and institutionalize AI as a capability. This phase also focuses on implementation and enterprise change management.
Responsible AI must be included across all three phases of scaling GenAI. Discovery predefines intended use and guardrails, assesses data provenance and quality, and establishes measurable accountability metrics along with business KPIs.
Design involves designing systems to these standards, including enforcing policies and incorporating access controls, and applying bias and safety testing. Scaling and deployment incorporates human oversight of high-risk steps, continuous monitoring and incident response, audit trails, and periodic model re-evaluation.
Which companies are finding success with GenAI?
Following the right approach, GenAI can deliver great results. For example, in the banking sector, an Australian bank applied GenAI to its software testing process, which has traditionally been a time-consuming manual task.
By leveraging GenAI, the bank was able to significantly accelerate its testing lifecycle, improve software quality, and foster a more collaborative and adaptive testing culture. In practice, this means faster release of new features to customers and greater confidence in those releases.
Another example is the pharmaceutical industry, where a North American pharmaceutical company used GenAI to reimagine its compliance and audit processes. Their existing rules-based document audit system was costly and not user-friendly, so they worked with a partner to integrate the GenAI solution.
The result is an AI-powered assistant that can review regulatory documents and identify potential quality gaps with over 95% accuracy, while reducing manual documentation effort by 65% and improving readability scores by 50%.
Marathon, not a sprint
The journey to deploying GenAI across a large enterprise is a marathon, not a sprint. Many organizations are currently in a trough of disillusionment, with early experiments still not delivering the promised ROI. But this stage can be survived as companies rethink their AI strategies from hype to reality.
By tackling data quality head-on, investing in organizational readiness, and fostering collaboration across IT and business domains, companies can avoid common points of failure. Importantly, organizations can unlock significant value by looking at product-aligned operating models and setting realistic expectations.
But achieving it at scale requires thoughtful and responsible AI, governance, iteration, and a continued focus on business outcomes. Companies that treat AI adoption as a holistic transformation, aligning technology with people, processes, and purpose, are already turning initial AI investments into lasting ROI.
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