The dazzling pace of advancements in generative AI often overshadows the value of existing AI technologies. This phenomenon has led some business leaders to mistakenly believe that older AI technologies are becoming obsolete. Not so! But focusing solely on generative AI can lead to inefficient allocation of resources and ultimately stifle AI’s full potential.
Understanding AI’s Broad Spectrum
At the core of all AI technologies is their ability to discern intricate patterns within data, a fundamental skill that powers various AI applications. This is the foundation of all machine learning. While large language models excel at predicting text sequences, predictive AI has been reliably forecasting trends, behaviors, and outcomes for over a decade. These technologies are not in competition but are complementary.
The One-AI Strategy Can Maximize Potential Through Integration
The next leap in AI’s evolution doesn’t hinge on yet another breakthrough; instead, it requires a strategic shift in how AI is utilized. A holistic, or One-AI, approach that intelligently combines generative and predictive AI can harness their strengths more effectively than either could alone.
Real-World Impact of a Unified AI Approach
The biotech firm Insilico Medicine combined different types of AI including predictive AI’s analytical capabilities with generative AI’s innovative problem-solving. The company dramatically accelerated its drug development process, cutting costs and time dramatically.
That’s what I believe will happen when companies stop focusing on putting together a piecemeal platform of AI tools and solutions to meet their needs and instead, encourages AI providers to work together through open-source, interoperability and API sharing.
Operationalizing AI: Modes of AI Interaction
Businesses can adopt various operational models depending on their needs:
- Sequential Mode: AI outputs sequentially feed into one another, enhancing depth and relevance with each step. For instance, Spotify’s AI DJ uses historical data to curate personalized playlists, which are then enhanced with engaging narratives generated by another AI.
- Feedback-Loop Mode: This involves continuous interaction between AI models, enhancing their effectiveness through mutual feedback. An example is FedEx’s AI-driven robot, which adapts its package handling strategies based on real-time spatial assessments.
- Standalone Mode: Sometimes, distinct AI applications operate independently to solve specific parts of a problem, like in fashion trend analysis and demand forecasting. These applications might operate separately but are considered within a unified strategic framework.
Best Practices for a One-AI Approach for Strategic AI Deployment
Organizations looking to thrive in the AI-driven landscape should consider these best practices:
- Unify AI Teams: To avoid siloed knowledge and fragmented resource allocation, companies like Meta are integrating their AI resources into unified teams, enhancing agility and innovation.
- Employ Model-Agnostic Problem Solving: By focusing on the business problem first, companies like Capital One ensure their AI solutions are purpose-driven and tailored to actual needs rather than being merely technologically driven.
- Ensure Data Integrity Across Interactions: Maintaining robust governance frameworks like DBS Bank’s PURE helps safeguard data quality and compliance as AI applications become more interconnected.
- Manage One-AI Risks: Companies must be vigilant about the unpredictability of AI, particularly in decision-making scenarios. Keeping humans in the loop helps mitigate risks and align AI outputs with organizational values.
Real AI Power Is Cohesive
AkzisAI is being built-out as a holistic approach to AI solutions by integrating analytical, predictive, and generative into one easy-to-use template-driven platform. We like to think of it as Practical AI.
For businesses, the path forward involves embracing a comprehensive AI strategy that leverages the strengths of both generative and predictive technologies. By adopting a One-AI approach, companies can not only prevent the underutilization of AI capabilities but also set the stage for future innovations that fully exploit the integrative power of AI technologies.
