The pace of AI investment continues even in the face of economic headwinds but the focus has shifted. The initial “land rush” to deploy AI solutions is now giving way to a focus on value as enterprise leaders begin to demand evidence of ROI for their AI spend. This is part of an overall trend – according to Gartner, nearly one third of generative AI projects fail to go past the proof of concept phase in large part because the ROI isn’t clear.
This does not mean that enthusiasm for AI is flagging but it does bring a new level of discipline to AI investment and implementation. It also affects the kind of AI project that gets the green light, with the potential for higher investment in commercial out-of-the-box AI solutions and more hesitancy about higher-cost custom AI projects.
None of these observations should come as a surprise. Our research establishes that companies get the greatest benefit from an AI transformation if it builds value via each of the following:
- Revenue growth, through the development of new products and services based on the company’s unique data or role in the value chain.
- Enhanced customer experience, via truly personalized experiences, dynamic pricing and digital commerce enabled by AI and its mastery of data.
- Productivity gains, through agentic decisions, automation and streamlined workflows.
What is becoming evident is that leaders need to shift their focus in several ways – away from technology and toward business impact, and away from a “gold rush” investment strategy and toward a more strategic AI implementation.
AI is not a technology challenge
The key to value creation is, first, to recognize AI is not a technology challenge. It’s a business challenge (and opportunity.)
Too many leaders make the mistake of seeing AI as just a productivity tool. But in fact, it is much more than that. It is a value creation lever. It connects the whole organization and applies data analysis to the organization’s farthest-reaching strategic challenges.
This means that it is unlike past technology transformations that automate a particular process. Unlike Robotic Process Automation, where a robot automates a task that used to be performed by a human worker but does not change the process itself, the value of AI is that it applies data and analytics across processes and functions. Thus it enhances the organization’s fundamental ability to deliver on its strategy.
This means that AI should be the remit of the CEO and the entire C-suite, not just technology leaders.
Approach AI as a growth driver
The companies that win at AI will be those that take a portfolio mindset across growth levers and align AI investment to growth strategy.
The focus of their AI deployment should be on:
- Performance. Companies often grapple with challenges such as high operational costs, slow processes and inefficient supply chains. By applying AI to the most critical of these cost items, they can drive significant improvement.
A software-as-service (SaaS) company deployed coding completion tools and testing solutions that increase productivity. Leaders targeted areas of coding that represent 70% of time spent.
- Competition. Businesses must think carefully about their true differentiators – and how AI can accelerate them. If your opportunity lies in personalized customer interactions, then generative AI can be used to improve workflows and customer experiences.
A construction and engineering firm deployed AI to review RFPs and draft an initial response, decreasing time spent by 20%. This enabled them to focus their efforts on the high-value-add parts of the response and to take on more competitive bids while increasing their win rate.
- Unique opportunities. Most companies are challenged to find new revenue streams. One way is to unlock the value of the company’s data. AI can aggregate and synthesize data to generate useful insights – which in turn lead to new ways to monetize data, new business models, and unique opportunities.
An equipment manufacturer that offers solutions for warehouse operations is deploying AI on an integrated data platform. They use data from multiple external sources to help drive new revenue streams and increase customer value. As a result, they are able to shift their offering away from only capital intensive hardware sales and toward integrated solutions. Thanks to their strategic use of AI, they are now able to provide their customers with a robust insights platform for supply chain efficiency.
Success begins with defining the opportunity
As these examples show, strategic AI deployment begins by carefully defining the opportunity.
Success also depends on quantifying the value of the opportunity. To accomplish that, it is essential to ask these key questions:
- What decisions are we making here? Can we enhance the consistency or effectiveness of the decision with AI?
- How risky is it to get the decision wrong?
- Is the process defined well enough to deploy AI or does the process itself need to change or be refined?
- How will I quantify the value created – both for internal teams and our customers?
Focus on transforming the organization
The AI transformation is like past technology transformations in one important respect. Like past transformations, it requires commitment to change and an acknowledgement that change comes from the human element. Successful leadership teams will be those that recognize that fact early on – and that manage the transformation accordingly, with the C-Suite taking the lead.
