The AI World Summit in Amsterdam in October 2019 was a global event featuring the biggest names and minds in the modern AI community, including Stuart Russell and Werner Vogels, who delivered keynote lectures. From the many speeches and discussions taking place at this event, a common theme arose that artificial intelligence for business should not be considered merely an end in itself, like an anchor meant to fix the ship of productivity and achievement in its place in this new great sea of chance.

Rather, participants concluded, it's like a sail, catching the winds of progress and change and thereby keeping that ship always moving forward through the shifting tides into the ever-changing future. When you consider your business to be that ship, then the promise of enterprise AI for your own future prospects may start to take a more appropriate and practical shape.

Enterprise AI is not a mission or a result, nor is it a panacea. Rather it is a route from where your business is now to where you would like it to be. Enterprise AI can serve, not as an anchor, but as a sail for the ship of your business to catch the winds of change, and carry you and your customers along the sea of progress into a future of prosperity.

Enterprise AI is useful for business of all types, whether you operate an insurance firm or an online shop. What are some of the truly practical ways your business can apply enterprise AI directly to problems you’re facing and goals you’re setting today? Here are a few:

• Consistency: Enterprise AI allows you to provide a consistent user experience and consistency of intelligence at scale. These enable a large company to give local communities that same "mom and pop store" feel as small businesses owned and operated by people living in their communities, such as remembering important names, dates and preferences in their customers’ lives.

• Economy of Scale: Whether you operate a hundred stores or thousands of stores, enterprise AI gives you the same simplicity of management with multi-presence capabilities, integrated service modalities, brand leveraging and national reach.

• Corporate Culture: Instead of thinking of IT as something separate from your core business, a necessary "evil" you need to help keep your business relevant in today’s climate, enterprise AI becomes an intrinsic element of your business model, branding and success.

• Personalization: With enterprise AI in your corner, you can create a hyper-personalized experience for each customer with each interaction, from customized promotional offers to consistent and ultra-transparent services. The tools of enterprise AI empower you to lower customers’ total cost of ownership (TCO,) reduce friction and improve life-cycle strategies for your products and services, thereby incentivizing greater subscriptions and upgrades.

• Direct-to-Consumer (D2C) Model Support: Enterprise AI was built to make D2C businesses flow seamlessly. Even if your business model doesn’t operate this way yet, by adopting enterprise AI tools, it could soon transform its process with ease. With a D2C model, you could build vertical, digitally native brands over which you have total and direct control of the product life cycle and customer experience. The potential benefits of such a model include greater growth, customer satisfaction and loyalty and five to 10 times the profit margin of standard retailers.

When implementing enterprise AI, there are two main domains for a business to consider:

• Incremental improvements such as creating more customer relevancy and discovering more efficient ways to operate.
  
• Game changers such as opportunities to reinvent your business model, disrupt the industry and transform the marketplace.

Now, let’s explore these incremental improvements and game changers enterprise AI facilitates, each in greater depth.

Incremental improvements

The most common and prolific uses businesses find for enterprise AI can be found in this category. Here, businesses use its tools to generate a multiplicity of models that convert data into predictions, using a scoring system. Depending on the batch, latency may run between real time and near-real time, but regardless, 99% of AI adoption and implementation today occur in this realm.

Currently, companies most typically build machine-learning (ML) models for AI problem sets like:

• Customer propensity to buy.
  
• Customer propensity to churn.
  
• Fraud detection pattern.
  
• Merchandise, planning, SKU-level assortment, demand forecasting.
  
• Outlier performers, anomaly condition detection .
  
• SCAM/SPAM/phishing patterns.

Companies more experienced with enterprise AI, or with more far-seeing visions of the potential the technology offers them, have started applying ML toward:

• Branching ontologies between products and categories to propel sales.
• Assessing accuracy of data sources.
• Improving the quality of data, including by reducing duplication and inferring definitions on poor datasets.

Companies using enterprise AI in these fashions typically have content-rich visual dashboards allowing them to easily navigate, operate and comprehend the technology. This gives business users and data scientists alike a level of intuitive insight into customers lacking in purely metrics-based reporting.

Game changers

Enterprise AI can go far beyond making daily operations more efficient and improving annual margins. It can also help transform entire business processes, promote more efficient organizational alignments, and design altogether better business models.

One way it can do these things is by replacing the traditional, restrictive, overly simplistic NPS scoring model with dynamic and contextual customer-affinity ones.

These models help companies to maintain greater control of their customers’ journeys. They help retailers more easily and effectively meet or exceed their customers’ expectations and foster more loyal and enduring relationships with them. They do so by creating greater relevance and consistency, thereby increasing trust while reducing friction. Dynamic and contextual ML customer affinity models assess and improve these objectives specifically.

To work most effectively, they require the right combination of data, including data from strategically acquired sources and data-augmentation services plus marketing data obtained through market analytics agencies and data aggregators.

Much of the conversation in the business world about AI to this point has been about its usefulness in describing what has already happened — in other words, how you’ve done. The key to modern enterprise AI, however, is in its ability to suggest potential wise steps a business might take to achieve optimal results in relation to its objectives — in other words, what to do.

Many business leaders and data scientists still rooted in the past discuss how AI reveals customer behavior, but those who recognize the breakthrough element ML offers to the field can now take that awareness to the next level and suggest future behavior based on this data from the past.

Keep in mind, the goal here is not simply to maximize transaction value anymore. Rather, it is to improve the customer’s perception of value. For that to occur, data-driven, next-generation applications must use the content of the external signal as the primary feature of their models.

They must use enterprise AI to extend event-driven architecture organically and build microservices around them.

Until now, the conversation has mostly been about how enterprise AI can help improve its business model. Another way enterprise AI can disrupt industries and markets is to use its data discovery tools to help develop whole new business models altogether.

Instead of creating new competition for incumbent businesses, however, this creates tremendous opportunities for those forward-thinking enough to begin applying those tools now to start seeking out those newer and better business models.

Currently, D2C still seems to be the prevailing leader for its:

• Proactive management and innate understanding of customer wants and needs.

• Total control of the customer experience.

• Ability to fully reimagine products and services as customer data requires.

Applying the tools of enterprise AI to the achievements and challenges of D2C may help unlock new opportunities for businesses to create entirely more effective, and heretofore unimagined, ways to engage with customers.

Many corporations like Warby Parker, Dollar Shave Club, Stitch Fix, Thread and MTailor have already implemented enterprise AI into their business structure and operating models with extraordinary success in a variety of business models, including:

• D2C
• Subscription commerce
• On-demand
• Secondhand products

The common threads to all their successes with enterprise AI have been a combination of a customer-based focus and a consistent increase in their adoption and integration of enterprise AI tools and practices to propel all facets of their workflows.

A company truly state-of-the-art in its embracing of enterprise AI and ML is one that realizes operating with both wide and deep-level ML is required to remain competitive, and that any under-investment in enterprise AI will inevitably blow back on a business as the accelerating rate of change continues to reward only the ready.

Now you know how enterprise AI can keep your business sailing forward with your customers into a bright and prosperous future.