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Smarter content connections: The key to digital business process innovation

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Content is at the core of every business process, so it follows that content services and process digitization are very closely linked. But the real scope for experience transformation comes when data, drawn together from multiple sources, can inform and trigger next actions—especially when it means that human involvement is strategic rather than intrinsic to a process.

AI, in the form of neural networks/machine learning technology, offers a lot of potential here, through its ability to discern connections between related data, recognize context, and complete routine processes while flagging or escalating anything which seems outside of the ordinary.

Take a seemingly basic process such as invoice approvals and processing. When organizations are dealing with the same suppliers regularly, it would be relatively straightforward to teach the systems involved to identify relevant documents, extract the relevant information and progress to payment, without requiring that each single transaction is personally checked. Train the system to recognize the invoice cycle, the amounts involved, and to cross-check the billing with materials received, and human involvement can be reserved for managing exceptions—for those occasions when dates or figures do not tally, or there is no record of goods having been ordered or received. In such cases, the AI-enabled system would spot the discrepancy and raise an alert—requiring that the responsible team member takes a closer look.

The ensuing efficiencies are self-explanatory, as work begins to flow more speedily and consistently, and companies are able to make better use of human skills.

In the automotive industry, managing promotional materials is a process that could lend itself to intelligent automation, using AI. As product or component details change along the supply chain, automated processes could help discern where a change of description or new image needs to be included in marketing materials, so that updates flow through.

Step improvements such as these demand that there is end-to-end visibility of content and data assets. They rely on the quality and currency of that data. Once there is transparency, and confidence in the reliability of data, companies can start to connect the dots—linking related information between systems to build up a holistic picture of what’s going on, perform smart analyses, and make better-informed decisions.

From little acorns…

The leap from traditional content management/business process management scenarios to those described above is significant, yet not a stretch for organizations to achieve. The first step is to be able to extract meaningful data from documents; the second is to be able to recognize and link this with other related data held in adjacent systems.

In an invoice settlement scenario, those elements are likely to include the invoice date and amount, the associated supplier account, the purchase order, and related delivery information. Once all of this information is automatically linked together, it becomes possible for trained software to cross-analyze the data to look for patterns and anomalies. So if a bill from a particular supplier comes in at a different time of the month than it typically does, carries the wrong number, or is for an unusual amount, a trained AI-based process management tool would automatically identify the discrepancy and flag potential fraud or a possible error.

It is through interconnections and smarter process management that organizations can begin to make real breakthroughs with process improvement—not merely accelerating processes, or delivering them more cost-efficiently but also creating an approach for doing things in new and better ways, and delivering new experiences. 

Creating better brand experiences

Take a major retailer, whose brand differentiation depends in part on its particular blend of products from both big-name brands and boutique suppliers or local specialists. Those smaller suppliers may be more vulnerable to cash flow problems, therefore relying on their customers to pay promptly. Yet niche suppliers are also the most likely to submit paper invoices, even handwritten paperwork, not as easily recognized by electronic accounts payable systems. This can delay authorization and processing, threatening supply and straining critical relationships (especially if word gets out that the retailer is a slow payer and needs to be chased for account settlement). A smart invoice processing system would be able to recognize all forms of invoice, and extract data reliably from them, ensuring that every supplier is treated fairly.

Once companies can extract data from invoices and combine this with information stored in other systems, or out in the wider world, they can start to make smarter buying decisions too—in line with internal KPIs. It becomes easier to compare what the organization is paying for utilities such as telecoms, with amounts paid by other similar-sized businesses, for example. This, in turn, can be used as leverage to get a better deal with the relevant supplier—or go out to the market to look for a more competitive source.

Evidence-based decision support

While large enterprises may have whole teams devoted to optimizing procurement and performing detailed business analytics, lower down the scale organizations tend to rely on "gut feel" to determine how well they are managing costs, maintaining supply, or coming through for customers. Turning everyday business documents and transactional information into strategic intelligence or trend information can be extremely powerful for these companies.

It can also help to inform bolder decisions, by providing robust evidence to support the case for change. This could apply even to decisions about which processes to automate—for instance, if analyze show the regularity and uniformity of transactions for a given supplier, suggesting that human approvals may not be needed other than in the case of exceptions.

Inspiring new services and revenue streams

"Transformation" is a bold term, but when businesses become data-enabled and data-driven, the scope for creating new experiences is considerable. It is by unlocking data that visionary companies like Lufthansa are changing the game in their industries, for example.

Rather than try to predict and fulfill every possible new experience that customers, employees and suppliers might wish to have, in their future interactions with the airline, Lufthansa has created a strategic project to provide open access to certain operational data—so that external developers can take up the baton. Instead of coming up with all the ideas itself, and having to hire all of the relevant digital expertise internally, Lufthansa is tapping into a broader ecosystem of talent who may have bigger and better ideas for innovative new apps and value-added services that will enhance people’s relationship with and experience of the company.

The decision to open up its data to developers is such a game-changer that it has opened up a whole new revenue stream for Lufthansa. Firstly, the company is charging developers for access to its data. Meanwhile, by harnessing the widest possible spectrum of ideas and talent across an extensive partner ecosystem, Lufthansa will be able to deliver new experiences to those who deal with the company—without over-stretching its own skills and resources. Using everything from real-time schedules, flight availability and pricing information to details of airport-specific lounges, opening hours and amenities, Lufthansa has challenged the developer world to think outside the box and make it easier than ever for customers to choose and get more from the airline.

Testing the potential

Yet organizations do not have to go this far to exploit the broader benefits of process transformation enabled by more connected and intelligent content and data handling. In fact, it is a much safer bet for companies to start small in their ambitions to become smarter and more data-driven businesses, experimenting with the possibilities in a discrete area of their operations. If not invoice management, that might be HR content and process management, or contracts management. Focusing in one defined area can companies determine the possibilities for smart automation and digital self-service, and build confidence—so that they are later able to try bolder transformations, such as creating new value-added services and business models (as in the case of Lufthansa).

To maximize the options, organizations should consider content/digital process management platforms that support multi-channel experiences—allowing different kinds of users to access and interact with the information and micro-services they need to, wherever they are and whatever device they are using. So, instead of a prospective business customer having to fill in a form to request a call-back from their supplier, for example, the supplier makes it possible for them to enter the request straight into an individual’s online calendar.

Making headway

Deferring process digitization is not a good idea, as organizations risk being left behind as their peers roll out new and better brand experiences, from faster and more responsive versions of traditional services, to ground-breaking new self-service apps and tools.

Our prediction is that, within 3 years, intelligent data-driven processes will take care of most standard transactions, while people’s experiences of dealing with companies (whether they are customers, employees or suppliers) will become lighter, more convenient and intuitive—via whichever channel the individual prefers (desktop, mobile app, voice, or web chat).

This doesn’t mean that organizations need to reach for the stars immediately, but rather than they should start mapping out a vision for data-driven transformation—one that can be delivered incrementally, as the business gains confidence in the possibilities and its ability to adapt the way it works.

Making sure any content management partner is on the same page is critical too. That means seeking out a software or integration provider which is pursuing its own data-driven transformation strategy and which can support the business across the duration of its journey.

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