Artificial intelligence (AI) is advancing rapidly, delivering new possibilities for businesses to harness data and tackle complex problems. However, as companies adopt AI solutions, many are realizing the limitations of narrow systems focused on a single technique like machine learning or natural language processing (NLP).
These siloed approaches often fall short in solving multidimensional business challenges that require broader capabilities. This has led to the emergence of composite AI – combining different AI technologies in an integrated fashion to create holistic and robust solutions.
In this comprehensive guide, we‘ll dive deep into everything you need to know about composite AI, including:
- What is composite AI and how does it work?
- Key benefits and business use cases
- How it differs from other analytical techniques
- Its current state and future outlook
- Why composite AI matters now more than ever
I‘ll draw on my over 15 years of experience applying AI and advanced analytics in Fortune 500 companies to provide unique insights into this powerful new approach. Let‘s get started!
What Exactly is Composite AI?
Composite AI refers to the systematic integration of different AI technologies into a unified solution pipeline. It brings together diverse analytical techniques including:
- Machine learning (ML) / deep learning
- Natural language processing (NLP)
- Computer vision (CV)
- Knowledge graphs
- Descriptive statistics and math modeling
- Rules-based expert systems
- Optimization algorithms
- Simulation and modeling
- Signal processing
- Neuro-symbolic AI
The key is to strategically combine these techniques based on their different strengths to build an end-to-end AI solution tailored to the problem at hand.
For example, a predictive maintenance application would fuse:
- Time series analysis – To forecast asset failure
- Computer vision – To visually inspect equipment
- NLP – To parse maintenance logs
- Graph algorithms – To detect anomalies and relationships in sensor data
- Descriptive analytics – To contextualize insights and trends
Instead of disparate models built separately, these different analytical models are tightly integrated into a comprehensive pipeline. The unified insights generated by looking at a problem from multiple perspectives lead to more robust decisions aligned with business goals.
This multiplicity of approaches is the essence and power of composite AI.
The Need for A Broad Set of AI Capabilities
Modern business problems are multifaceted. For instance:
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Personalizing customer experiences requires understanding language, analyzing emotions, predicting interests, generating recommendations – necessitating NLP, computer vision, different ML techniques and more.
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Optimizing manufacturing requires monitoring equipment, predicting failures, coordinating logistics, adjusting production – involving sensor data analytics, time series forecasting, optimization algorithms, simulations to balance tradeoffs.
Narrow AI systems limited to a single technique cannot incorporate the diversity of capabilities needed. Composite AI provides an architecture to integrate the required breadth of AI skills.
Why is Composite AI Important Now?
There are several key factors that explain the growing relevance of composite AI approaches today:
1. Increasingly complex business problems
As companies digitally transform, AI is being applied to new complex problem domains like personalized healthcare, dynamic pricing, predictive maintenance, and autonomous vehicles.
These multifaceted challenges cannot be effectively addressed by any single analytical technique in isolation.
For instance, self-driving cars require fusing sensor inputs, visual analysis, planning algorithms, physical simulations, real-time optimization, and more to navigate safely on roads. Composite AI provides the methodology to bring together the required diverse capabilities.
2. Limitations of narrow AI
Narrow AI systems have achieved impressive results by specializing in a specific technique like computer vision, NLP, reinforcement learning, or predictive modeling. However, these narrow systems are often brittle, opaque, and limited in dealing with real-world ambiguity and novelty.
Integrating multiple narrow AI systems leads to more generalizable and robust models that complement each other, similar to how the brain uses different functional regions together.
3. Lack of massive training data
Deep learning techniques at the core of modern AI require huge datasets to train the models. However, many companies simply don‘t have access to such massive data required to effectively train complex deep learning models.
Composite AI reduces data dependency by complementing deep learning with other techniques like knowledge graphs, symbolic reasoning, and human oversight.
4. Need for flexible and explainable decisions
Narrow AI systems optimize for accuracy metrics but lack the transparency and flexibility to adapt decisions to different contexts. In contrast, composite AI can incorporate both black-box and transparent models to enable more nuanced, contextual decisions while also providing explainability.
5. Operational efficiency
Combining analytical models into a unified platform reduces data science silos within organizations. It also enables continuous feedback flow across models to improve the integrated system over time. This makes composite AI solutions more scalable and maintainable.
For these reasons, forward-thinking companies realize they need to evolve beyond narrow AI to remain competitive. IDC predicts global spending on composite AI to grow from $3 billion in 2024 to over $30 billion by 2026, indicating its expanding role. [1]
IDC forecasts massive growth in composite AI adoption over the next few years. [1]
Real-World Examples of Composite AI
While still an emerging approach, composite AI has shown valuable results across diverse domains:
Autonomous driving – Self-driving cars integrate computer vision, LIDAR sensors, route planning, sensor fusion algorithms, traffic pattern analysis, human behavior modeling, and simulations to enable safe navigation.
Precision medicine – Analyzing clinical notes, medical images, genomic sequences, and population data in combination provides deeper personalized insights than any single data type alone.
Algorithmic trading – Combining time series analysis, sentiment modeling, news parsing, graph analysis, and portfolio optimization theory enables more informed trading decisions.
Smart energy management – Predicting renewable generation, analyzing usage patterns, load forecasting, price optimization, and constraint programming can better balance demand-supply.
Credit risk modeling – Fusing customer transaction data, financial statements, news analysis, graph linkages, and business sector knowledge improves credit and fraud detection.
These examples illustrate the diversity of business challenges being tackled by composite AI approaches today. Let‘s now look at how composite AI systems are architected and developed.
Architecting Composite AI Solutions
While there is no rigid process, here is a general methodology for developing composite AI solutions:
Step 1) Define the problem clearly – Understand objectives, metrics, constraints, stakeholders, and processes.
Step 2) Map required analytical capabilities – Analyze required data, decisions, and dynamics to determine needed AI techniques.
For example, optimizing inventory requires demand forecasting, logistics modeling, pricing optimization, and policy compliance capabilities.
Step 3) Develop modular analytical models – Build separate models for each required capability, using available data. Enable each model to be improved continuously.
Step 4) Integrate models into an orchestration pipeline – Connect the models into an end-to-end workflow. Output from one model can serve as input to others.
Step 5) Continuously improve solution – Monitor system performance and dynamically fine-tune individual models as well as their integration and hand-offs.
Modular modeling integrated into an end-to-end pipeline
Instead of a fixed architecture, the models and their interconnections can be adapted as needed by the business problem.
Prebuilt composite AI platforms significantly simplify the integration complexity for enterprises. Let‘s examine the benefits of this multi-pronged approach.
Key Benefits of Composite AI
Composite AI offers significant advantages over conventional analytical techniques:
Holistic insights – Integrating multiple analytical perspectives provides more comprehensive and contextualized insights for complex problems.
Wider capabilities – Allows incorporating a diverse spectrum of capabilities like creativity, goal-driven planning unlike just statistical modeling.
Resilience – Combining complementary techniques makes the system more robust and reliable than relying on one model.
Human-aligned decisions – Enables more flexible and nuanced decisions tuned based on business needs.
Reduced data needs – Augmenting data-hungry techniques like deep learning with knowledge-based approaches reduces training data dependency.
Operational efficiency – Consolidating analytical models into a connected system improves organizational efficiency.
Continuous learning – The integrated pipeline enables ongoing learning across techniques to continuously adapt and improve.
For these reasons, composite AI delivers exponential value over narrow analytical systems. Let‘s look at the state of adoption and future evolution.
Current Adoption and Evolution
While nascent, usage of composite AI is accelerating across industries:
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Increasing integrations – Expanding beyond AI to incorporate more analytical techniques like optimization, simulation, and neuroscience models.
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Smarter orchestration – Advancing model integration, pipelining, and interchange to minimize latency and errors.
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Explainable systems – Improving solution interpretability by explaining overall reasoning using interactive visualizations.
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Prebuilt solutions – Developing configurable solutions for common scenarios to accelerate adoption.
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Democratization – Low-code platforms to enable non-technical users to utilize composite AI more easily.
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MLOps integration – Streamlining management and monitoring of complex composite systems using MLOps practices.
Gartner predicts that by 2025, 15% of new enterprise AI projects will use composite AI, underscoring its increasing relevance.
While promising, there are still open challenges, including:
- Handling conflicts between model outputs
- Improving interconnectivity and handoffs
- Managing cascading uncertainties across models
- Auditability, compliance, and bias mitigation
- Abstracting complexity for business users
However, the pace of innovation is accelerating to address these gaps. Companies that dismiss composite AI risk falling behind more progressive peers.
Future Outlook and Possibilities
Looking ahead, here are exciting possibilities as composite AI capabilities grow:
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Composite AI platforms – End-to-end solutions requiring minimal data science skills to adopt for common business scenarios.
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Automated technique selection – Metadata-driven recommendation of optimal analytical methods for new problems.
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Tighter model integration – Contextual interchange between analytical models and reasoning systems for smarter handoffs.
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Democratization at scale – Low-code solutions allow any company to leverage composite AI, beyond just tech giants.
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Edge integration – Deploying parts of composite intelligence on edge devices like drones, robots, and cars for real-time decisions.
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Conversational interfaces – Natural interaction powered by contextual composite intelligence to understand user goals.
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Frontline augmentation – Field technicians, agents, and nurses augmented by composite AI applications via AR/VR.
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Continuous adaptation – Systems that constantly adapt through meta-learning algorithms by analyzing their own behavior.
The possibilities are amazing as composite AI evolves to replicate flexible human-like cognition. Companies that embrace this approach today will gain a sustainable competitive advantage.
Key Takeaways
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Composite AI strategically combines different analytical techniques into an integrated pipeline tailored to multifaceted business problems.
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It delivers more holistic and resilient solutions compared to isolated approaches.
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Leading analysts predict composite AI adoption to accelerate given its strong business value.
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Companies need to evaluate organizational readiness as composite AI becomes vital for AI leadership.
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Now is the time to start building expertise in architecting and implementing composite AI systems.
With exponential growth projected in AI software, platforms, and applications, composite AI represents the next major wave of innovation for enterprises worldwide. I hope this guide provided useful insights into this transformative technology paradigm. Leveraging composite intelligence will be key for digitally maturing organizations to make smarter data-driven decisions in the future.
