The AI Promise: Why the Expected Revolution Hasn’t Delivered Yet
Artificial intelligence (AI) has long been heralded as a revolutionary technology with the potential to transform industries and redefine how businesses operate. However, as the discussion around generative AI intensifies, it becomes clear that not all AI implementations deliver the anticipated value. This divergence between expectation and reality often stems from a misunderstanding of AI’s role and capabilities. As a SAAS vendor providing the Keto AI+ Platform for Strategic Portfolio Management, it is crucial to recognize that the true value of AI lies not merely in its existence but in its strategic application—what is often referred to as AI+ or "Applied AI".
The Current State of AI Adoption
In recent months, concerns have surfaced about the sustainability of generative AI. High-profile experts, such as MIT’s Professor Daron Acemoglu and NYU’s Emeritus Professor Gary Marcus, have questioned the long-term productivity gains that AI can offer. Their skepticism is echoed by industry leaders like Jim Covello of Goldman Sachs, who suggest that significant investments in AI might not yield proportional returns, particularly in complex tasks that AI is not yet equipped to automate effectively.
A case in point is the experience of some companies with Microsoft’s Copilot tool, where the expected productivity gains did not materialize, leading to the discontinuation of pilot programs. These instances underscore a critical point: generic AI solutions, while powerful, may fall short when not customized or adapted to specific business needs.
Applied AI: A Tailored Approach to AI Implementation
The conversation shifts when we consider the concept of AI+ which involves tailoring AI models to meet the specific requirements of an organization. Unlike generic large language models (LLMs) like ChatGPT, which offer broad capabilities, AI+ focuses on developing specialized solutions that align closely with a company’s operational goals. This approach is crucial in strategic portfolio management, where the stakes are high, and the margin for error is minimal.
Customization is Key
This highlights a fundamental truth: the effectiveness of AI in business is heavily dependent on how well it is integrated and aligned with the company’s objectives. Off-the-shelf AI tools might offer some benefits, but they are unlikely to provide the level of insight and efficiency required for complex decision-making processes in strategic portfolio management.
For instance, generic AI models might struggle to account for the nuanced dependencies and relationships that exist within a portfolio of strategic initiatives. In contrast, a tailored AI+ solution, developed through Applied AI principles, can be trained on specific datasets, adjusted to consider industry-specific factors, and fine-tuned to reflect the unique challenges and opportunities faced by the organization. This level of customization is what transforms AI from a general-purpose tool into a strategic asset.
The Role of AI Engineering
AI Engineering plays a pivotal role in the successful deployment of Applied AI. It involves not just the development of AI models but also their integration into existing business processes, ensuring that they operate seamlessly within the organization’s technological infrastructure. AI+ engineers are responsible for bridging the gap between AI research and practical application, translating complex algorithms into usable, business-focused solutions.
This is particularly important in strategic portfolio management, where decisions are often made based on complex data sets, including financial projections, market trends, and risk assessments. An AI+ engineer’s job is to ensure that the AI+ tools used in this context are not only accurate but also reliable and aligned with the company’s strategic goals.
Overcoming Challenges in AI Implementation
The path to successful AI+ implementation is not without its challenges. One of the most significant hurdles is the gap in specialized AI+ expertise. As noted by industry experts the integration of AI into business processes requires a deep understanding of both AI technology and the specific domain in which it is applied. This dual expertise is rare and often limits the effectiveness of AI+ initiatives.
Building a Skilled AI Team
To overcome this challenge, companies must invest in building a team of skilled AI+ professionals who understand the intricacies of AI+ Engineering. This team should include data scientists, AI+ engineers, and domain experts who can work together to develop, implement, and refine AI+ models that meet the organization’s needs. Additionally, continuous training and development are essential to keep the team updated on the latest advancements in AI technology and methodology.
Strategic Leadership and AI Governance
Another critical factor is strategic leadership and governance. Leveraging AI effectively requires more than just technical expertise; it requires a strategic vision and strong leadership. Organizations must establish clear policies and guidelines for AI usage, ensuring that AI initiatives are aligned with overall business objectives and ethical standards.
Leadership plays a crucial role in fostering a culture of innovation where AI is not seen as a silver bullet but as a tool that, when applied correctly, can drive significant value. This involves setting realistic expectations, prioritizing AI+ projects that align with strategic goals, and ensuring that the AI+ team has the resources and support needed to succeed.
The Long-Term Value of Applied AI
The long-term value of AI in strategic portfolio management comes from its ability to enhance decision-making, improve efficiency, and drive innovation. By applying AI+ in a way that is closely aligned with the organization’s needs, companies can achieve these benefits while avoiding the pitfalls of generic AI solutions.
Enhancing Decision-Making
In strategic portfolio management, decision-making is often complex, involving multiple variables and uncertainties. Applied AI can provide valuable insights by analyzing large datasets, identifying patterns, and predicting outcomes with greater accuracy than traditional methods. This allows decision-makers to evaluate options more thoroughly and make more informed choices.
Improving Efficiency
AI+ can also improve efficiency by automating routine tasks, freeing up human resources to focus on more strategic activities. For example, AI-driven tools can streamline the process of data collection and analysis, allowing portfolio managers to spend more time on higher-value activities such as strategy development and risk management.
Driving Innovation
Finally, AI+ can drive innovation by enabling companies to explore new opportunities and identify emerging trends. Applied AI solutions can help organizations stay ahead of the competition by providing insights into market dynamics, customer preferences, and technological advancements. This proactive approach to innovation is essential for maintaining a competitive edge in today’s fast-paced business environment.
Conclusion
The value of AI in strategic portfolio management is not inherent in the technology itself but in its strategic application. AI+ and AI Engineering are essential for developing tailored solutions that align with an organization’s goals and deliver tangible business value. By investing in the right expertise, fostering strategic leadership, and focusing on customization, companies can harness the full potential of AI and drive long-term success.
At Keto Software, we understand the importance of Applied AI in strategic portfolio management. Our Keto AI+ Platform is designed to provide the tailored insights and advanced analytics that organizations need to make informed decisions and stay competitive in today’s rapidly evolving market. By leveraging the power of Keto AI+ Platform, we help our clients not just to keep up with the pace of change but to lead the way in their respective industries.
Markus Halonen
COO, Keto Software