Generative AI Isn't Overhyped...It's Underused: Why Companies Are Stalling and How to Push Through
In my work with businesses adopting Generative AI, I've seen the same pattern emerge time and again...early excitement gives way to frustration!
After experimenting with quick wins such as meeting summaries, support chatbots, and automated content, companies struggle to unlock AI's broader and higher potential.
It's a classic case of what Gartner calls the "trough of disillusionment" - the hype fades, and the real challenges set in.
I don't believe, though, the issue is that AI's overhyped; it's that most businesses are approaching it in the wrong way. At this point leaders and teams need to consider how they push through if they're to realise the full promise of AI.
AI Reality Check
Generative AI has followed a familiar trajectory seen with other disruptive technologies: rapid adoption fuelled by immense hype. Gartner's Hype Cycle illustrates this journey, where new innovations peak in inflated expectations before plummeting into the "trough of disillusionment." According to Gartner, Generative AI is nearing this stage as organisations realise that delivering on its promise requires more than initial enthusiasm.
Critics argue that the hype is unsustainable, pointing to challenges like unclear ROI, data quality issues, and limited expertise. However, the excitement around Generative AI is not misplaced. A McKinsey report found that early adopters using AI in core business processes saw productivity gains of up to 40%, highlighting its transformative potential when deployed strategically.
The problem doesn't lie in the technology itself, but in its application. As the Harvard Business Review notes, "AI's true value emerges when organisations stop treating it as a shiny object and integrate it into long-term strategies." While companies may feel disillusioned by unmet expectations, this stage is crucial. Those who push through and align AI with clear business goals stand to unlock competitive advantages, while others risk stagnation.
The hype is real...and so are the opportunities.
Quick Wins Aren't Enough: Why Most Companies Hit a Wall
Many companies rush into Generative AI adoption by targeting basic use cases like chatbots, content creation, or email personalisation. These are often quick to implement, providing fast results that fuel excitement. While these efforts demonstrate AI's potential, they often fall short of delivering substantial and sustained impact.
The problem arises when organisations fail to move beyond these 'low-hanging fruit'. Scaling AI requires deeper integration across processes, yet businesses often lack the infrastructure, strategy, and internal expertise to make this leap. As Accenture research points out, only 12% of companies have achieved a level of "AI maturity" by embedding it throughout their operations.
One common barrier is a siloed approach to AI deployment. "Many teams operate AI as an isolated experiment rather than a cohesive strategy," notes an MIT Sloan report. Without cross-functional collaboration and investment in scalable systems, companies hit a wall.
Generative AI's true value lies in its ability to transform complex workflows and enable data-driven decision-making. To achieve this, businesses must move past quick wins and embrace AI as a core part of their long-term vision - something few are really doing.
The Real Barriers to AI Adoption
While Generative AI holds immense promise, many companies struggle to realise its potential due to several entrenched barriers.
First, there is a glaring lack of internal expertise. 2024 research by Randstad reveals that while 75% of companies are adopting AI, only 35% of talent have received AI training in the past year, indicating a significant skills gap. Without technical knowledge and AI-savvy leadership, organisations struggle to move beyond surface-level implementations.
Unclear ROI is another significant challenge. Generative AI’s benefits are often indirect and long-term, making it difficult for businesses to justify initial investment. As Forrester Research notes, “Leadership teams often prioritise initiatives with immediate financial returns, sidelining AI programmes that require time to mature.”
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Data silos also prevent progress. Generative AI thrives on robust, integrated data, but many companies lack the infrastructure to connect disparate systems. IBM research highlights that 80% of business data remains unstructured, creating bottlenecks in AI adoption.
Finally, organisational resistance to change is a pervasive issue. Teams are often wary of automation, fearing job displacement or disruption to familiar workflows. Gartner notes, “Without a strong cultural shift and transparent communication, AI adoption faces internal pushback that limits its impact.”
To overcome these barriers, businesses must invest in education, infrastructure, and cultural readiness to fully embrace AI’s transformative potential.
Pushing Through the Trough of Disillusionment
To overcome disillusionment and realise Generative AI's potential, businesses need a structured approach:
By implementing these steps, businesses can bridge the gap between initial AI experimentation and long-term transformational impact.
Rethinking AI Use Cases for Maximum Impact
To unlock Generative AI's full value, businesses must move beyond generic applications like automating customer support or generating simple reports. Instead, they should focus on identifying tailored solutions that address critical business challenges and opportunities. For example, AI-driven demand forecasting can optimise inventory management, while dynamic pricing models help maximise revenue based on market trends and customer behaviours.
The process begins with a clear understanding of organisational goals. Businesses should map their key pain points, assess where AI could provide the greatest impact, and evaluate the data required for success. Collaborative workshops involving cross-functional teams can uncover innovative, future-facing use cases.
Once these opportunities are identified, companies must prioritise them based on feasibility and expected ROI. By aligning AI initiatives with strategic objectives and testing use cases in small-scale pilots, businesses can iterate towards scalable solutions. As Deloitte notes, "AI succeeds when it's applied to the right problems with the right data."
By embracing this structured, forward-looking approach, businesses can deploy Generative AI where it delivers measurable value, driving both innovation and long-term competitive advantage.
The Risk of Standing Still
In the rapidly evolving AI landscape, doing nothing is no longer an option. Companies that hesitate risk falling behind competitors who leverage Generative AI to enhance efficiency, reduce costs, and deliver personalised customer experiences.
According to Accenture's 'The art of AI maturity' research, only 12% of firms have advanced their AI maturity enough to achieve superior growth and business transformation; yet these "AI Achievers" can attribute nearly 30% of their total revenue to AI, on average.
By standing still, businesses miss opportunities to innovate, access new markets, and improve decision-making. Worse still, they risk obsolescence as industry standards shift towards AI-driven solutions.
To remain competitive, leaders must embrace AI as a core part of their strategy, or risk being left behind in the new-world order.
With a focus on Customer Data Platforms, I help publishers and other businesses optimize their marketing, technology, operations and fulfillment functions. If you have a technology problem, contact me.
1 个月Good article. My 5-second summary after reading it. Step 1: What sucks right now? Step 2: Can AI solve that? Step 3: Let's try.
Executive Vice President @ SOME1.AI | Artificial Intelligence
1 个月100% John. I totally agree, but I would. Obviously! Great piece. It's my second week in this market, and my initial out reach has seen nothing but positive feedback. Its opened new doors and there are already deals on the table. But, it is the big shiny new toy of industry. My findings are whilst most people get a vague yet excited understanding of the WHY. It is only those that really embrace the WHAT and HOW, that will advance. But it is far less complicated than most believe it is. Most are used to an automated (controlled) way of working, and the thought of complete autonomy scares them.