A New Dawn for The Intelligent Enterprise?

Organizations have poured millions into their existing information infrastructures. New technologies are providing organizations with big new opportunities to leverage that investment, to optimize current business processes and to transform future business opportunities.

Optimize current business processes

The internet of things and in-memory computing bring greater visibility into existing business processes than ever before, and allow organizations to react in real time to changing circumstances. For example, the ubiquity of cheap sensors and better data connections offer more precise tracking of every individual production machine, every delivery, and every customer touchpoint.

Enabled by all this fine-grained data, machine learning allows the creation of business processes that optimize themselves, automatically, over time and as people use them. Some examples include:

Shipping. Algorithms can track product shipments and automatically flag when there’s a predicted delay, so you can intervene before it affects the customer experience. And the algorithm can learn from experience, automatically improving shipping expectations over time.

Finance. A large chemicals company struggled for years to automatically match invoice data to bank payment information — references numbers would be different, or customers would pay one invoice with two payments, or vice-versa. By using machine learning algorithms to analyze the patterns in the large body of data about historical invoices, the company was able to raise the automatic matching rate from 70% to more than 94% — representing a massive potential savings in time and effort.

Human Resources. The biggest barrier to improvements is often fear of change. Algorithms can help automatically identify the best training courses for each individual, based on their interests, previous training, and the training taken by others with similar profiles. It makes training more proactive, helping change innovation culture.

And those are just some examples among many hundreds or thousands that exist across the enterprise. For example, McKinsey believes that up to 70% of current financial processes can be automated, and Gartner believes that this year alone, half a billion people will save two hours a day thanks to AI-powered tools — equivalent to more than 200,000 years of increased productivity!

This type of enhanced automation works best with large quantities of high-quality data — and in most organizations, the best-quality data available is already stored in your corporate systems, the result of your long-term investments in applications and infrastructure.

And machine learning is most effective when the decisions to be taken are well-defined and frequent, that require complex inputs but have a clear output that can be executed inside an existing business process. For example, repeatable decisions such as “is this transaction likely to be fraudulent?” or “what product should we try to cross-sell this customer at the checkout counter?” are ideal opportunities for machine learning automation.

Machine learning can help cut costs, improve customer satisfaction, and even enhance cultural change within the organization. For example, algorithms can help analyze all of the training available to employees and proactively promote the courses that are mostly likely to appeal to an individual, based on their interests, prior training, and profile.

These applications of machine learning have another big advantage: they can be implemented with relatively little disruption to the business, since it involves optimizations to repetitive, inefficient parts of existing processes rather than difficult cultural change.

The other big efficiency opportunity concerns new “natural language” interfaces powered by machine learning. These chat bot and voice technologies allow convenient, scalable, self-service access to services and data. It can be both for customers: “is this pair of shoes available in red?” and internal users: “what is the budget vs actuals for my department this month?” By lowering the barriers to accessing data and enabling new types of users, it provides a greater return on your existing systems and investments.

Transform business opportunities

Companies everywhere are faced with digital transformation challenges and opportunities. Increasingly, the exploration of new business models isn’t a question of choice, but one of survival. It’s clear that there are now new opportunities to rethink businesses and entire industries from scratch, using methodologies such as Design Thinking to reconsider how new technologies can transform the end-to-end customer experience.

Organizations can use data to move from selling products to providing integrated digital services. Brazilian agricultural machinery provider Stara, for example, has moved from selling tractors to providing integrated digital farming systems. Sensors on the tractors can ensure each individual plant gets exactly the fertilizer it needs. This means less fertilizer is required, making farming more profitable. That’s good news for farmers, good news for Start, and good news for the environment because it makes farming more sustainable. It’s a great example of how new technology can not only provide better business, but help the world be a better place.

It’s not just about innovation — it’s about new ways of innovating. Because new business models are by definition uncertain, organizations have to learn new approaches. Companies have to be able to adopt more agile, prototype-driven innovation than in the past.

The cloud is an ideal environment from this type of experimentation. All the functionality you may need, such as access to streaming sensor data, deep machine learning algorithms, text mining capabilities, sophisticated mapping, or support for personalized mobile interfaces is all already available on cloud platforms, only an API call away.

In addition, cloud platforms give smaller organizations the opportunity to tap into powerful functionality that up until now has been restricted to only the biggest companies who could afford all the underlying infrastructure.

Organizations can quickly create and test prototypes, interactively with prospective users, without the costs and delays associated with on-premise projects. And once the prototypes have been validated, it’s possible to scale fast without detailed upfront estimates of possible demand.

Vestas, for example, is the world’s leading provider of wind turbines. They used the Design Thinking methodology to identify high-opportunity areas for innovation. They had been using a very manual process to gather data about when to expensively move cranes between their more than 300 constructions sites. They used prototyping to create a mobile application to be used by the crane managers in the field, in just three weeks, and are hoping to save more than eight million euros this year in more effective use of their assets.

Incremental improvements or radical change? Both!

They bottom line: there’s never been a better opportunity to leverage your existing infrastructure investments. Organizations can use it to optimize and simplify existing processes, while actively experimenting with the new business processes of the future.

To hear more from me about A New Dawn for the Intelligent Enterprise keynote, register TODAY for the Intelligent Transformation Virtual Summit on May 22!





One response to “A New Dawn for The Intelligent Enterprise?”

  1. […] This article originally appeared on Digital Business & Business Analytics. […]