What AI Investments Can Learn From Past Digital Transformation Efforts
Cristian Randieri is Professor at eCampus University. Kwaai EMEA Director, Intellisystem Technologies Founder, C3i official member.
Despite massive expectations, businesses have historically been disappointed in the actual returns from their digital transformation investments.
In fact, the Harvard Business Review’s 2022 analysis of various studies found that digital transformations failed to meet their original objectives between 70% and 95% of the time. The main reasons for these failures included a lack of a clear value proposition, misalignment between stakeholder expectations and poor alignment between technology and business strategies.
When investing in AI aimed at improving efficiency and productivity, the risk of failing to meet the desired results remains equally high—maybe even more so, considering the scarcity of in-depth analyses on the outcomes of AI investments. Organizations must adopt an even more specific strategic approach to avoid repeating the mistakes made with past digital transformations.
These past failures, though, can offer valuable lessons to develop more effective strategies and improve the efficiency of AI implementation. By aligning AI investments with business objectives and adopting new ROI metrics, companies can move beyond traditional financial measures and better account for the peculiarities of AI.
Redefine AI ROI metrics.
To fully capture the value of AI investments, companies must broaden their approach to evaluating ROI beyond conventional financial metrics, such as cost savings or revenue increases, and develop a framework that also considers AI’s strategic and operational effects on business performance.
AI-based predictive tools, for example, can help companies better understand market trends and consumer behavior, which are capabilities that traditional KPIs often fail to capture.
The framework must also consider the benefits in terms of efficiency, thanks to the automation and optimization of workflows. AI helps reduce errors, accelerate processes and free up resources for higher-value tasks. In this sense, employee productivity and satisfaction should also be closely monitored.
Another key element is risk management and regulatory compliance. Real-time data analysis, combined with predictive modeling, offers more effective tools for risk control, while AI-based solutions simplify compliance processes. Extending the ROI assessment to include these dimensions can make it possible to obtain a more realistic vision of the long-term value of AI investments.
Align AI investments with IT and business strategy.
To successfully implement AI projects, companies must also define clear objectives and identify specific use cases that align with those goals. This alignment is crucial to avoid wasting resources.
When AI models are guided by a coherent strategic vision, they are more likely to meet business needs—particularly when cross-functional teams are in place to foster collaboration and drive innovation. By combining expertise from multiple functions, companies will be better equipped to drive integration and innovation.
Additionally, the insights generated by AI models must be translated into real operational and decision-making strategies. The information provided by AI must be understandable and accessible to decision-makers to ensure that they can make timely and informed decisions.
Classify AI investments to obtain competitive advantages.
Organizations that want to maximize AI investments must divide them into three categories, each having a specific role in delivering value:
1.”Off-the-shelf” AI solutions like chatbots or predictive analytics tools. Even though these tools may not provide a competitive advantage, they are essential for a solid foundation in day-to-day business operations.
2. “Enabling” AI solutions designed to increase the efficiency and scalability of existing processes, like supply chain optimization or customer segmentation.
3. “Differentiating” AI solutions, which is the most strategic category. These are proprietary models and solutions, developed internally, that help the organization differentiate itself in the market. Investing in this type of AI means being able to build a sustainable competitive advantage, as it is difficult for others in your industry to replicate these solutions.
To maximize long-term value, adopt a balanced approach that accounts for the costs of “off-the-shelf” and “enabling” solutions and focuses strategic resources on differentiating solutions, which is the engine of sustainable growth.
Adopt a financing model based on proof-of-concept.
Like with past digital transformations, success with AI-based solutions often involves a phase in which you experiment with the technology.
To succeed, your financing model should be based on a proof-of-concept approach, which allows you to define clear and precise success metrics. Along the way, you can also identify intermediate milestones to monitor the project’s progress and objectively evaluate the results.
Financial teams are more likely to allocate resources toward initiatives that have shown potential as a proof of concept. At the same time, projects that did not achieve their objectives can be paused or shut down promptly, minimizing the risk of the projects becoming financially burdensome.
In other words, this financing model can balance the need to manage financial risk with the imperative to promote innovation.
Conclusion
Investing in AI can be profitable and transformative, but it requires a strategic approach.
By looking to past digital transformation efforts, companies can redefine ROI metrics and focus on strategies that align AI initiatives with their specific business goals.
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