Why you need intelligent AI adoption
Karthik Ramakrishnan Karthik Ramakrishnan
May 1 5 min

Why you need intelligent AI adoption

Artificial intelligence offers genuinely new capabilities for organizations in decision-making and data analysis. Yet, there is no straight line between the technical capabilities of machine learning and business applications that create value over the long term.

This is as much a management problem as it is an engineering problem. To unlock the greatest value from AI, you need to understand what AI technologies match your business needs, how to customize AI to the unique strengths of your organization, how your internal processes need to change to enable success, and how your business strategy may be reshaped as a result. We call this intelligent AI adoption: adapting AI to your business, and adapting your business to AI.

This article is the first in a four-part series describing intelligent AI adoption. The following articles detail how to judge your organization’s AI readiness, how and why to build a proper roadmap for AI adoption, and why you need to start now.

Adapting AI to your business

Intelligent AI adoption means matching AI capabilities to your business needs, then using AI applications to augment the unique strengths of your organization. That’s adapting AI to your business.

Consider the example of AI-powered translation, which helps people communicate across language barriers. An MIT study found that when eBay launched automatic translation on their e-commerce platform in 2014, trade between English-speaking and Spanish-speaking countries increased by 17.5%. By achieving a good fit between AI capability and business application, eBay opened up new markets for buyers and sellers around the world, benefiting the company and its users alike.

The essence of strategy is competitive differentiation: to create value that no one else can. Business-grade AI-powered translation was relatively new in 2014, but high-quality machine translation is now generally available. To sustain competitive benefits from their early investment in AI, eBay had to do more than achieve it first: eBay had to adapt their AI application performance using the unique elements of their business.

eBay did this by training their machine translation system with proprietary data, adding manually coded rules, and fine-tuning the results with help from human language experts. Rather than make their AI application more accurate for general-purpose translation, they adapted it to become more specialized for the most important and value-adding areas of their site. And they did so using data and context that only they possessed.

To create lasting value with AI, you too must position AI systems to learn from and strengthen your business DNA—the unique way of creating value that’s encoded in data, processes, expertise, and other elements of your business.

Adapting your business to AI

Adapting your business is also about understanding what needs to change in your business to support your AI ambitions, from updating internal processes to reassessing the business strategy itself. That's adapting your business to AI.

Consider the decades-long journey to AI-powered merchandising at Amazon. Starting with their use of advanced algorithms for product recommendation in the 1990s, Amazon now uses AI to automate a range of merchandising activities, including setting prices and controlling inventory levels. This success required steady internal change in combination with advanced AI.

Amazon’s data-driven strategy has been a critical enabler for their AI goals over time. Amazon tailored their website and regularly acquired new companies to enhance the breadth, quality, and speciality of their data to continuously improve and automate their decision-making. They obtained the Internet Movie Database—featuring actor and director information, as well as passionate user reviews—back in 1998. Similar acquisitions followed for audiobooks (Audible: 2008), shoes (Zappos: 2009), video games (Twitch Interactive: 2014), and comic books (ComiXology: 2014). Amazon now enjoys best-in-class data about products and user behavior by design.

Amazon similarly rebuilt internal processes and teams to support AI-enabled decision-making. They only automated individual merchandising decisions on a case-by-case basis, over the course of years, as experiments proved the technological and organizational readiness for change. Along the way, they invested in technical as well as business talent: first to understand and prepare markets in the 2000s, then to accelerate internal change in the mid-2010s once the company’s AI strategy was clear. That AI strategy evolved over time, changing the company’s business strategy with it.

To create lasting value with AI, you too must prepare your business to use AI capabilities: by enhancing data, redesigning processes, developing and organizing people, and revisiting business strategy in light of new options.

Putting it all together

To achieve intelligent AI adoption, you must adapt AI to your business, like eBay did by selecting and specializing machine translation AI to their unique strategy. You must also adapt your business to AI, like Amazon did by readying data, people, and other elements for merchandising automation.

These two processes don’t operate in a vacuum. They can be a feedback loop: as your AI portfolio grows, it will impact your business, which will in turn change the demands placed on the AI portfolio itself. The art of adopting AI is to accelerate these processes while balancing short-term value with building long-term capabilities and vision.

In the remainder of this series, we discuss how to achieve this balance and intelligent AI adoption using a strategic roadmap approach—and why you need to start now.