Artificial intelligence (AI) capabilities have expanded rapidly, but many companies still struggle to solve complex business problems with AI. The answer for the future is composite AI – systematically integrating different AI technologies to emulate human intelligence. This multidisciplinary approach combines the strengths of various analytical techniques into a unified solution.
In this in-depth guide, we will explore what composite AI is, why it matters now more than ever, how it works to drive business value, current applications, and what the future looks like for this crucial capability.
Defining Composite AI
Composite AI refers to combining multiple AI approaches, potentially with other analytical techniques, into an integrated system that can holistically address complex issues. This includes leveraging:
- Machine learning and deep learning algorithms
- Natural language processing (NLP)
- Computer vision for analyzing visual data
- Descriptive and predictive statistical models
- Knowledge graphs and semantic search
- Simulation systems and digital twin models
- Specialized decision support systems with encoded domain expertise
The key is systematically unifying these methods instead of relying on one single technique. This allows the composite system to harness complementary strengths, offset limitations, and handle multifaceted problems. The integrated whole becomes more than the sum of its parts.
Contrasting Composite AI with Ensemble Methods
Some familiar approaches like random forest modeling and boosting represent ensemble methods – combining multiple models to improve predictive accuracy. However, composite AI goes beyond just predictions.
The table below summarizes some key differences:
| Composite AI | Ensemble Learning |
| Multipurpose – can support various tasks like classification, forecasting, recommendation, optimizing, etc. | Focused on improving predictions |
| Integrates radically different methods like NLP, simulation, vision analysis | Combines similar algorithms (all ML or statistical models) |
| Workflows bridge across tools and datasets | Typically all run within one tool |
| Continual enhancement from ongoing human input | Static after initial development |
This greater flexibility to combine complementary capabilities is what makes composite AI so versatile and adaptive.
The Strategic Importance of Composite AI
Gartner identified composite AI as one of the most important emerging technologies in their 2020 Hype Cycle report. But why does integrated AI matter so much right now?
Rapid Growth in AI Adoption
Driving urgent enterprise interest in AI is rapid acceleration in adoption and market growth. Per IDC forecasts, the global AI software and services market already exceeds $65 billion and will reach $126 billion by 2025 – reflecting a 23% compound annual growth rate [see Figure 1].
Figure 1. Global AI market size historical and forecast from IDC showing rapid expansion at 23% CAGR continue driving adoption.
Specifically, ML and data science tools see some of the strongest demand. But to harness AI‘s potential, companies recognize the need to augment these foundational methods with expanded intelligence.
Enabling True Human-Like Decision Making
Humans dynamically draw upon many mental capabilities to make decisions. We combine linguistic processing, visual pattern recognition, statistical reasoning, accessed knowledge, imaginative simulation, and accumulated life experience. Composite AI aims to emulate this adaptable, multifaceted intelligence.
Instead of narrowly focusing on specific tasks, composite systems take a generalist approach to ingest diverse information, analyze from multiple perspectives, and make judicious trade-off decisions. This allows more flexibility and better handling of surprises.
Reducing Reliance on Large Teams and Big Data
Many AI applications require enormous datasets and extensive data science teams. Composite AI offers an alternative by blending knowledge-based specialized systems with data-driven machine learning. Even with limited data, such hybrids can reason effectively.
This fusion of encoded human knowledge and empirical pattern recognition mirrors how people learn. We combine subject matter expertise with hands-on experience – not just one or the other. Likewise, integrated AI systems leverage both rules and data to better handle complexity with less overhead.
Industry AI Adoption Statistics
Across sectors, AI wave is accelerating, especially among industry leaders [see figure 2]. For instance:
- 72% of healthcare payers state they already use AI in business processes while 89% expect to within 3 years.
- 64% of industrial manufacturers indicate they currently employ some form of AI. Just 12% have no plans to adopt AI.
- In retail/ecommerce, AI adoption reached 54% in 2021. 98% of retail executives believe AI is becoming essential.
- For financial services, AI adoption crossed 62%. Leaders especially apply AI for security, risk analysis, and decision automation.
And across every domain, integrated multifaceted AI approaches increasingly drive the highest impact initiatives to further accelerate adoption.
Figure 2. Percent of companies in each industry using some form of AI already in 2021 showing over 50% adoption on average.
Generating Insights from Small Data
The multifaceted analysis of composite AI allows making reasonable inferences even when data is sparse or fragmented. For example, NLP can extract text insights from customer surveys while simulation models fill gaps by projecting possible scenarios.
This data fusion amplifies understanding beyond what isolated techniques could achieve. Composite AI can distill the "wisdom of the crowds" across diverse qualitative and quantitative sources – deducing meaningful signals from the noise.
Real-World Composite AI Applications
While still an emerging practice, composite AI implementations are starting to demonstrate value across many industries:
Dynamic Pricing Optimization
A European airline combined time series forecasting models, competitive price analysis, demand attribution models, and optimization algorithms into an integrated system that recommends highly calibrated pricing adjustments in response to shifting market conditions. This boosted annual revenue by over €250 million.
Predictive Maintenance
An industrial equipment company developed a composite solution with machine learning on sensor data, NLP analysis of maintenance logs, a simulation model of equipment degradation, and a knowledge-based recommender. This enabled accurately forecasting breakdown risks weeks in advance by considering multiple factors.
Figure 3. Example composite AI solution for predictive maintenance combining IoT monitoring, NLP, simulation, and a recommender system.
Holistic Brand Tracking
A top food and beverage provider combined computer vision analysis of products in stores, NLP across brand mentions online, econometric market modeling, and a customer journey simulation based on various promotional stimuli. This guided optimal marketing mix decisions and increased customer lifetime value by 7%.
Identity Fraud Detection
A financial services firm developed an integrated platform with transaction pattern analysis, graph algorithms to uncover networks, document verification models, biometrics, anomaly detection, and compliance red flag workflows. This reduced false positives in identifying potential fraud by over 40% compared to previous isolated approaches.
Comprehensive Investment Management
A wealth management firm integrated quantitative models, NLP for parsing news and filings, alternative data feeds, relationship graphs, and behavioral analysis of past client actions. The unified platform led to a 12% higher annual return across their portfolio compared to previous performance.
These examples demonstrate the versatility of composite AI approaches to drive tangible business value across domains. But implementing these advanced solutions does pose some unique challenges.
Risks and Challenges with Composite AI
While promising, composite AI also comes with hazards to anticipate much like pioneering any new complex technology:
Dependency on Advanced Data Infrastructure
Integrating large-scale diverse datasets, orchestrating data flow across tools, and providing flexible computing power relies on mature dataops engineering foundations companies may lack. Attempting composite AI without this established data backbone often fails.
Cultural Inertia
Because composite AI touches many parts of an organization, overcoming legacy divisions to adopt such disruptive capabilities encounters institutional resistance. Managers cling to isolated siloed practices rather than embracing ecosystem thinking.
Lack of Composite AI Expertise
Even organizations with strong data science teams can struggle since orchestrating multifaceted AI workflows requires scarce cross-disciplinary skillsets. Both software engineers and analytical modelers able bridge across specialties.
Regulation of Black Box Systems
Integrating less transparent techniques like deep learning neural networks and reinforcement learning creates AI solutions harder to audit and govern. New standards seek to increase accountability around complex composite approaches.
While surmountable through careful strategy and governance, enterprises must not underestimate these adoption challenges with composite intelligence.
Expert Perspectives on the Future of Composite AI
Technical leaders across the industry comment on the crucial role of integrated multifaceted intelligence for the future:
"Standalone machine learning hits a complexity wall in its ability to solve real human problems. Combining knowledge, data, and reasoning across modalities opens much vaster possibilities." – Dr. Richard Socher, Chief Scientist at You.com
"Point solutions have reached the end of the road. We need a more interconnected, orchestrated approach using the concepts of composite AI to drive the next generation of competitive advantage." – Amber Grewal, Senior AI Architect at ANZ Bank
"The sophistication of decision making with unified analytical ecosystems far outstrips what isolated tools can support. Composite AI is the culmination of everything we‘ve worked towards for decades." – Professor Yoshua Bengio, Pioneer in Deep Learning
"Humans dynamically meld many cognitive capabilities and life experiences to make judgments. For AI to augment human performance, it must fuse together complementary techniques in a similar fashion to handle complexity." – Sophie Lebrecht, Principal Researcher, IBM
Industry innovators clearly recognize the immense yet still largely untapped potential of integrated intelligence. But successful adoption depends on discipline.
Best Practices for Composite AI
For organizations pursuing composite AI, some recommendations include:
Validate Subcomponents First
Ensure analytical modules have stable accurate performance before connecting in the full workflow to simplify troubleshooting. Faulty inputs will cascade failures.
Stress Test with Adversarial Data
Attack the integrated system with malformed data, contradictory samples, and edge cases to surface stabilities and events the hybrid architecture still needs to handle.
Maintain Explainability Features
For techniques like deep learning that have opaque reasoning, ensure explainability interfaces so users understand drivers behind composite recommendations and can provide essential human oversight.
Formalize Responsible AI Reviews
Conduct scheduled bias and fairness reviews across network analytics, perform impact analysis on decisions, and correct issues through continuous improvement.
With the right precautions and governance, enterprises can harness composite AI as a crucial capability for disruptive innovation and accelerated competitive advantage.
The Outlook for Composite AI
As the examples show, we are just scratching the surface of multivariate integrated intelligence for business applications. Adoption is still in its early stages but poised for massive expansion over the next decade.
Some key market dynamics to expect:
- The global market for AI software and services will grow from $65B in 2021 to $126B in 2025 per IDC projections – a compound annual growth rate (CAGR) of 23%. Increasingly, companies will recognize composite AI capabilities within this expanding market.
- Gartner forecasts that by 2025, 70% of organizations will use composite AI to improve decision quality, collaboration, and personalization capabilities compared to just 5% in 2021.
- Surveys indicate 93% of senior executives believe integrated AI will be crucial for business performance within 5 years.
- Open composite AI platforms will emerge to facilitate easier linkage of different analytical components using low-code workflows – allowing faster hybrid solution development.
- Specialized composite AI consultants and system integrators will grow dramatically given the more advanced technical aptitude required compared to standalone solutions.
The bottom line is that systematically unified, versatile intelligence is the ultimate future for AI’s business impact – and forward-looking companies need to start building relevant capabilities now.