Unlocking AI: Three Key Strategies for Successful AI Adoption in Your Organization
Imagine harnessing the potential of artificial intelligence (AI) to revolutionize your business operations, drive significant growth, and outpace the competition. The path to leveraging AI might seem daunting with its ever-evolving nature, but fear not. By understanding the three core AI adoption strategies, your organization can make informed decisions and effectively integrate AI into your business model.
Understanding AI Adoption
AI comes in many different shapes and sizes. That applies to the use cases, the underlying technologies, and the approaches to adopting AI in organizations. Business leaders across various industries are increasingly seeking tangible frameworks to understand AI from a business perspective. Some of the most pressing questions include:
- How much time and money is required to adopt AI?
- What returns can we expect from an AI investment?
- What risks are associated with AI implementation?
These questions are valid and critical for any strategic planning process. Fortunately, as AI technology has matured and been packaged into ready-to-use solutions, organizations can now achieve clearer answers to these pressing questions. These AI solutions can be divided into three primary adoption approaches, each with its own set of benefits and challenges:
The Three Main AI Approaches
To simplify the efforts and outcomes of AI, let’s explore the three core approaches: Off-the-shelf AI, AutoAI, and Custom AI. Each approach comes with unique characteristics, and the trick lies in understanding these properties and knowing when to apply each type. This categorization provides a concrete foundation for predicting costs, risks, and returns when implementing AI.
1. Off-the-shelf AI
Some AI solutions are ready to use right out of the box with little to no adjustment required. Examples include the Siri in your iPhone, invoice capture software, or speech-to-text solutions. These solutions take minutes to get started, and the business models are often AI-as-a-Service (AIaaS), making the initial investments low. Often, these services operate on a pay-per-use model, which entails low financial risk. However, the challenge is that these solutions are usually not customizable—you get what you get, and any necessary tweaks to fit specific business needs are limited.
Example: Google’s Cloud Vision API allows businesses to extract information from images using pre-trained models. While powerful, businesses can only use the existing capabilities without significant customization.
2. AutoAI (Automated Machine Learning)
AutoAI, also known as Automated Machine Learning (AutoML), offers a hybrid approach by providing some level of customization without starting from scratch. With AutoAI, businesses can use their own data—such as documents, customer data, or images—to train AI models in pre-made environments that select the right algorithms for the task.
This approach requires some effort, particularly in acquiring and preparing data, but does not necessitate a large team of data scientists. The trade-off is that standardized tools may not offer the highest possible accuracy and can lack flexibility.
Example: IBM’s Watson AutoAI platform allows users to upload datasets and generate machine learning models without deep technical expertise. While users have some control, the models are constrained by the platform’s built-in options.
3. Custom AI
With Custom AI, almost everything is built from the ground up. This approach typically involves data scientists and machine learning engineers and is more suited for research and development (R&D) departments. Custom AI is the go-to choice when extremely high accuracy is required and when the organization’s needs are unique and cannot be met by standardized solutions.
Custom AI projects are time-consuming and costly, often taking months or even years of development and experimentation. However, they offer unparalleled flexibility and the possibility to tailor every aspect of the AI to specific business needs.
Example: Self-driving car technology companies like Waymo invest significantly in Custom AI to develop specialized algorithms and systems that require precision and high accuracy for safe autonomous driving.
A Final Note on AI Strategies
The most common approach for solving business problems with AI will likely involve a combination of Off-the-shelf AI and AutoAI solutions. By blending these strategies, organizations can balance customization with ease of implementation, achieving more significant advancements with less risk.
The evolution of AI adoption is not unique. Other technologies have followed a similar progression, moving from niche academic pursuits to mainstream tools. The maturity of AI signifies that it is becoming part of the public domain, no longer confined to elite academic institutions.
While the boundaries between these approaches may be fluid, applying this framework can demystify the technology, making AI a practical tool for businesses. By leveraging the right AI adoption strategy, organizations can unlock substantial value and drive meaningful innovation.
Join the Conversation
What AI adoption strategies has your organization considered? Have you experienced success or challenges with Off-the-shelf AI, AutoAI, or Custom AI? We invite you to share your insights and experiences in the comments below!