Enterprise AI is verticalized by default—business is the context

Business leaders can leverage an “AI-in-a-Box” approach quickly to build disruptive, AI use-cases for first-mover competitive advantage, disrupting competitors at their own game. The key is to bring all relevant components required to build AI solutions for your business together “in a box” so that you can quickly build, train, and deploy the use cases, rather than reinvent the wheel.

AI-in-a-Box is a simple verticalized business AI use-case framework with partially pre-built modules including:

• Re-curated and processed, clean, and statistically validated training, testing, and validation datasets;
• Generic ontologies for the business vertical;
• A collection of applicable algorithms based on the use case, e.g., for classification, anomaly detection, pattern recognition, image processing, NLP, features, entity, and semantic extractions;
• Pre-built domain-specific knowledge bases and patterns—in suitable forms of knowledge representations.

The recent HFS study on machine learning (ML) adoption showed that different industry verticals (see Exhibits 1 and 2) are applying these core AI technologies and algorithms at different levels of the 3Vs of volume, velocity, and variety.

Exhibit 1: What Machine Learning means to enterprises (by Industry)

Question: How much do you agree or disagree with these statements? (N=10 to 26)

Source: HFS Research, 2018; Sample: 153 senior level executives from organizations with $1 billion or more in revenues

Exhibit 2. Where Machine Learning is expected to provide impact

Source: HFS Research, 2018; Sample: 153 senior level executives from organizations with $1 billion or more in revenues

While all industries are using various AI solutions to reduce operational costs, BFSI, CPG and retail, high tech, healthcare, and transportation are leading the way (see Exhibit 2). BFSI and healthcare are unique verticals because 75% to 80% of respondents from these verticals have prioritized revenue growth over cost reduction.

Design verticalized AI-in-a-Box solutions to disrupt your competitors.

Here are three simple steps to start this transformational practice:

1. Mine your future digital initiatives for requirements to determine which AI applications will empower and enable them.

Digital businesses cannot work without leveraging the speed, scale-up and scale-out capabilities, and infinite variations provided by AI and intelligent automation. Two use cases illustrate this concept:

• Digital banking: If a bank’s digital strategy is to offer mass-personalized banking to differentiated segments of its high-net-worth customers, it can build AI use cases for users designing their own personal-banking assistants and services offerings. It can also create use cases for fair and informed negotiations between autonomous agents negotiating rates and service parameters within applicable regulatory and legal frameworks.
• Digital manufacturing and Industry 4.0: If an automobile manufacturer’s digital strategy is to build a “green” brand image, it can build AI use cases to help it design green transport solutions, including engines powered by non-conventional energy sources.

2. Design and build verticalized AI-in-a-Box solutions that are quickly trainable, quickly deployable, and pre-enriched with critical knowledge components and required infrastructure.

Just as ERP started as common business process solutions that eventually matured into vertical-specific stacks, AI solutions are also closely linked with specific verticals. Just as reusability fosters adoption of object-oriented systems, partially pre-built domain context, quick training, and quick deployment of new-age AI use cases scales up AI adoption. In AI-in-a-Box verticalized solutions, there should be five key components, as Exhibit 3 explains.

Exhibit 3: AI-in-a-Box solution architecture components example

3. Brainstorm with business leaders and different customer groups to frame strategically disruptive AI use cases that can generate a sustainable competitive advantage for your organization.

The transformational value of AI will manifest only if a business uses it as a critical source of strategic competitive advantage beyond just operational cost reduction; for example, a business can use it to transform existing business and operating models, design and create new products and services faster, generate new demand and revenue, enter new markets, and attract new customers. Exhibit 4 provides some examples.

Exhibit 4. Disruptive and strategic AI use cases

The Bottom Line: Verticalized AI-in-a-Box solutions will become the next big-bets for organizations that can utilize AI for a competitive advantage.

AI-in-a-Box models can deliver new and strategically disruptive integrated use cases quickly, which can change the rules of the game in any business domain. Verticalized AI solutions with pre-built components in a box can make AI truly transformational. Organizations that are thinking ahead about competition will leave the traditional players far behind in the red ocean of cost competitions; they will reinvent themselves into new digital avatars emerging out of the blue oceans of AI-empowered opportunities. Human imagination will be the only limiting factor on what AI can do—to disrupt or be disrupted—no matter which vertical your business belongs to.