The AI Gold Rush: Ensuring Your Enterprise Doesn't Get Left Behind

Hugo Pinto

Oct 9, 20243 min read

In today's fast-paced business landscape, the rapid adoption of generative AI is akin to a modern gold rush.

With nearly 90% of companies deploying or piloting AI solutions, the pressure to keep up is intense[1]. But how can you ensure your organisation isn't just following the crowd, but implementing AI strategically, and not doing AI for AI's sake. And do you need to be planning for?

The FOMO Factor

Many leaders feel pressured to adopt AI without a clear strategy. And many others, try to formulate an AI Strategy without a clear long-term business strategy. This rush can lead to poorly executed implementations that fail to deliver value or expose the organisation to unnecessary risks.

For instance, Gartner reports that 85% of AI projects fail to deliver on their promises [2]. A notable example is Air Canada's chatbot, whose hallucinations led it to make promises outside the companies policies and led to damages and legal fees payments and a stern ruling that made the airline responsible for whatever the output of the chatbot was [3].

Think about the end to end lifecycle and how to manage quality on data, outputs and impact measurement.

Budget Allocation Challenges

Balancing AI investment with other priorities is a significant challenge, especially since most Medium to Large corporations are still mid-way through their transformation projects, others already deep into cost-cutting exercises, or under M&A activities.

According to a KPMG survey, 83% of $1Bn+ corporates believe that GenAI investments will increase over the next three years, but might struggle to justify the ROI before 2027 [4]. The retail giant Walmart faced this challenge when implementing its AI-powered inventory management system, initially struggling to quantify the benefits to shareholders [5].

This has the potential to shape the future of the company, it's employees, clients and communities around your business, don't manage it to get quarterly returns.

Skill Gap Reality

The shortage of AI expertise in the job market is a critical hurdle. LinkedIn's 2024 Jobs on the Rise report highlights AI and machine learning specialists as one of the fastest-growing roles, indicating a high demand but limited supply [6].

A nuance of the LLM-age AI opportunity is the technical skills now need subject matter experts and product strategists working side by side, so just hiring Data Science PHD's and Engineers will not suffice.

Healthcare provider Mayo Clinic partnered with Google to overcome this challenge, leveraging external expertise to drive their AI initiatives [7]. Looking at how best to leverage your ecosystem, and training non-technical staff will be the only sustainable people strategy.

Integration with Legacy Systems

Garbage In, Garbage Out - as simple as that. It's not that you can't build AI without your own data being sorted and ready, but it will be the same as everyone else's, and not building on your company's expertise and uniqueness.

Overcoming technical hurdles when integrating AI with existing infrastructure is crucial. A Deloitte study found that 55% of companies cite integration complexity as a top challenge in AI adoption [8]. Certainly for scaling and rolling out products and solutions this needs to be executed and well governed - 2025 will be the year of the first success stories and spectacular failures as well...

JPMorgan Chase successfully navigated this challenge by gradually modernising its core systems while implementing AI solutions, demonstrating the importance of a phased approach [9]. Yet, they are still struggling to show the ROI.

While the AI adoption race is on, success lies in strategic implementation, and how it's managed across the lifecycle. By addressing these challenges preemptively and putting the right teams, processes and governance into place, enterprises can ensure their AI initiatives align with business goals and deliver measured value.

Get in touch if you want to know more about how to enhance and accelerate your AI programs to success.

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