What Is Applied Artificial Intelligence?
Applied AI is the use of artificial intelligence to enhance and extend software applications.
To accelerate the use of AI in the software companies we invest in, Georgian Partners has developed a pragmatic framework to assist the adoption of machine learning and other building blocks of AI: The Principles of Applied AI. Our investment thesis is that businesses that adopt applied AI will outperform the market because AI will enable superior service levels in terms of capability, delivery, availability, accuracy, convenience, engagement, cost and brand.
The Best Applied AI Companies Today:
Use machine learning to automate decision-making with high accuracy.
Put a high emphasis on continuous learning from process and feedback data.
Use human experts-in-the-loop to fill in the gaps.
Proactively monitor for quality and bias.
The Principles of Applied AI
When we talk about AI, we’re looking at business applications with:
An increased focus on end-to-end process automation.
Use of advanced machine learning techniques.
Better articulated performance objectives and model accuracy.
Appropriate use of human judgement and automated predictions.
Continuous learning and adaptation through feedback loops.
Approaches to limit the impact of model error and bias.
Many businesses find it challenging to get started with AI because it’s such a complex subject and there are so many areas where it can help. We wrote the framework below to provide a high-level mental model to use when you’re adopting AI. You get get more details on what’s involved here.
Start with the Processes
AI has the potential to affect every business process and job role either through augmentation or automation. Business value can be impacted by injecting insights into existing processes, optimizing processes, or automating entire or parts of business processes. Beyond that, AI provides opportunities to redefine or create entirely new processes that weren’t possible before, even with human effort. To identify the places with the greatest potential for AI in your business, start with the processes your business controls or should control, and where improved outcomes or efficiency would have the highest benefit to your and/or your customers’ businesses. Understand which parts of the process involve human judgment, and which of those parts should be optimized, augmented, automated or reinvented.
Integration
Integrate artificial intelligence-enabled processes into your business via APIs for software and intuitive interfaces for people. Integrate human judgment as required to improve predictions. Leverage your customer base, in-house domain experts or crowdsourcing engines to supplement model predictions. Enable continuous learning by observing, measuring and improving outcomes as actions are delivered within a business process.
Models
The first step in model selection is to determine the desired level of performance from a business perspective. Select techniques to build machine learning models that achieve desired outcomes and to fit budget, objectives and available training data for each of the most valuable predictions and integration points. A lack of planning and achievement of objectives for error and bias will increase business risk and reputational harm. Business differentiation will come from the optimal combination of model integration, the modeling techniques themselves, and the data used to train and optimize the models.
Data
The type and volume of data required for applied AI depends on the machine learning technique(s) selected. This data may be that which you already collect and have access to (albeit perhaps with additional labeling), or it may need to be acquired from your own sensors or from third parties. Develop plans and infrastructure to capture and cleanse relevant data to support your AI models, and adjust frequency and breadth as necessary.
Governance
Monitor performance and fairness, and build customer trust. Ensure sufficient understanding of models and data to satisfy expectations of repeatability and interpretability. Adopt a market leadership position on ethical, privacy and legal concerns related to use of artificial intelligence.
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