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The Cognitive Enterprise: Market Marking Business Platforms (2 of 4)

Aug 20, 2020

Part 2 of this 4 part series will describe the importance of Business Platforms in The Cognitive Enterprise.

  1. The Cognitive Enterprise (1 of 4)
  2. Market Making Business Platforms (2 of 4)
  3. Intelligent Workflows (3 of 4)
  4. Enterprise Experience and Humanity (4 of 4)

Definition of a business platform

There are generally three types of platform:

  • Internal Strategic / Enabling Platforms - Embedding differentiated workflows for the next competitive advantage, and more cost effective and flexible front, middle, and back office processes
  • Industry Platforms - Upon which multiple industry players can benefit from new digital, cognitive, and cloud capabilities while driving operational efficiency.
  • Cross-Industry Platforms - Straddle industries by managing essential or value-added processes for a wider ecosystem of partners.

Internal platforms break the silos inside an organisation, creating new economies of scale to leverage skills, intelligent workflows, and data for new value.

External platforms break the barriers between organisations and industries. The platforms grow and connect complementary products and services in a way that makes experiences more holistic for customers.

In Singapore for example, the government has the Networked Trade Platform that connects among others, insurers for Marine cargo, to the shippers, and suppliers, customs. to manage documents digitally between all parties, etc. This can then be integrated with the insurer's own platforms for a smooth customer experience and enhanced data capture, as well as any other functionality that the insurer has such as payment gateways.


Platforms combine business and technology drivers

How fast and how smoothly can you onboard a new participant to your platform? How quickly can you respond to a new customer requirement? Can you assemble and reassemble new infrastructure and interfaces, new workflows and teams faster than the competition? 

The business platform must be made up of highly interoperable system components and infrastructure built using cloud, AI, and other exponential technologies. Microservices and APIs should be configured with ease to create shared value in the form of new products and services, across an ecosystem of partners.

As-a-service components enable agile business configurations. Real-time and external IoT data, coupled with AI and machine learning, enable the organisation to simultaneously sense changes in customer expectations and environmental conditions.


Action Areas

There are three Action Areas which are seen as critical to becoming cognitive:

  1. Double down on "Big Bets" - Make platform choices (it's a "Big Bet" because it requires large investment) and align the organisation, assets, resources, and investments to rapidly scale and sustain the continued evolution of the platform.
  2. Create a new business blueprint - Embed governance in a more open and transparent business architecture to enable rules, more informed decision making, and allow for the rapid reconfiguration of organisational components to create a new target operating model.
  3. Orchestrate compelling change - Establish control towers to monitor early warning indicators, implement change in real time, and develop iterative and proactive change management.

1) Double Down on "Big Bets"

Strengthen the core business and expand opportunities

Business platforms transform organisations but betting on the right one can be an existential matter. Depending on the strategy, it may be important to be able to build a partner network over initial market share. The platform’s ecosystem might make it a better bet than its product portfolio.

Platforms are being used to improve organisations’ data science and forecasting ability in conjunction with ecosystem partners. Use of IoT across supply chains is allowing the big companies who were outflanked by the nimble digital start-ups, to make a comeback. Their own historical big data, existing ecosystem partners, and their physical assets are allowing them to orchestrate change.

By extending the platform, organisations will increase their offerings and services, not just from themselves, but also through ecosystem partners also focusing on strengthening their own core, thus providing a quality, holistic experience. With enough data, customers can be provided with a curated experience, increasing the value for the customer.

Think of car sales; a platform could offer customers use of a car for a set period, and in doing so also sell pay-as-you-drive insurance, possibly also tied into safe driving habits captured through telemetry data.

It’s not just about sales

Providing a great customer experience and product is great but the advantage to the organisation is the mass of data that comes from the wide variety and constant interactions that the customer has with platform. From this, ecosystem partners will gain insights and use them to gain further advantage.

Re-engage the core

A lot of the data within the incumbents is ‘dirty’ and this heterogeneous data (high variability and formats) prevents real insights from being made. However, the data exists the incumbents have an advantage from so much proprietary data and domain expertise.

Incumbents own most of the world’s physical assets and should look at the value that can be derived from IoT-connected, hybrid cloud networks to build their data advantage. Allied with this is also building a culture of data, and building the skills that enable them to use this data.

Companies have a choice of the previously mentioned platform types:

  • Two internal types - Strategic Enabling Platforms, which embed workflows to create competitive advantage, and Internal Enabling Platforms to strip out cost.
  • Two external platform types - Industry Platforms where multiple players collaborate to create new capabilities, and cross-industry platforms that straddle industries.

The kind of platform to build starts with a consideration of strategic capabilities including:

  • Unique proprietary data. The ability to curate data so that it’s AI or other exponential technology-ready, as well as accessing new data sources. 
  • Deep expertise. Technology expertise is in short supply, as well as the softer skills needed for agility and collaboration.
  • Differentiated workflows. The workflows that support the organisation’s capacity to scale with speed and stay open to change.
  • Exponential technology applications. The combination of technologies that differentiate and automate intelligent workflows and create new customer, partner, and employee experiences.

It’s been hard for manufacturers to create direct-to-customer sales channels as this is not their core strength. Those who have tried alienated their trusted sales channels in their attempt to create direct-to-consumer relationships. The trust with the customer is likely to be with the retailed, not to mention their skills in selling.

Assuming that the core is strengthened by the platform, and traditional measures like efficiencies can be seen, how else can value be measured? After all, the benefit from a well-chosen platform with open APIs lies with the network effects and partnerships that are built.

A new platform investment is a major outlay and organisations will need to calibrate the appropriate measures. 


2) Create a new business blueprint

Establish an open and transparent business design

As businesses bet on new platforms organisational change is inevitable and most future operating models end up being a hybrid, meaning that the organisation will operate on a platform for some of its business. Some may have separate platforms for customer-facing or back-end activities. As new platforms are built, the future state and legacy operating models may well be running side by side.

On multi-party platforms, the assets and capabilities of other organisations must be considered. According to IBM, research suggests that most ecosystems will grow in size and complexity, and the most successful platforms average about 40 ecosystem partners, compared to an average of 27 for others. They also include partners from more industries and more regions.

To future-proof the platform, business architectures need to be built “open” to enable ecosystem connectivity. This includes frictionless entry for new participants - including the functions and departments within an organisation. Open technical standards break down the biggest barriers to entry. Regarding pricing, will the platform owner charge participation usage fees or earn its revenues from advertising or other services that help participants manage their business?

Set the rules and governance to create value

The challenge of having rules and governance is be able to support but not stifle the organisation. Exhaustive rules and governance act as a choke on progress and lock organisations into doing things a certain way.

To avoid this, organisations set what are called ‘Big Rules’. Often seen, it might look like this:

  • The parent company will set the core mission
  • The local conditions and market will determine the customer service model
  • The core back-office processes will be standardised
  • IT architecture will leverage on open source

Protect the data and compliance

Rules governing data will be at a more detailed level. Data is an organisation’s key point of differentiation and therefore policies must exist which explicitly guide how data is shared. Permissions must be set as to who can see what and under what conditions. Let’s not forget regulatory rules too, for example an invoicing system that allows partners to reverse bill, and for associated rules for those partners reside in multiple jurisdictions.

Governance on who can use a platform is also necessary. When it is open, the participants must meet a certain standard to meet security, or other regulation / quality controls.

Automated decision making

Exponential technologies enable organisations to gain insights that were never previously possible, allowing for better, timelier decisions. Sensors, wearables, and robots are generating masses of from a multitude of streams. Facial expressions can be captured to monitor feelings towards a product, for example in a virtual showroom.

If AI is involved, patterns can made from all this data, providing insights that can then be translated into action.

As the organisation becomes more data driven, the slow, large scale consensus that was traditionally needed for decision making reduces. Automation can remove some decision making altogether, bring to light that need human intervention and those that are possibly too complicated and still need consensus.

Straight Through Processing (STP) is a goal of many platforms and is rooted in this decision-making ability. As well as smarter decisions, headcount can be streamlined and geared towards the more complex decision making needed at the escalation points defined in the intelligent workflows.


3) Orchestrate compelling change

Change management for the Cognitive Enterprise

Change management has often been added on to a project, if there’s time. Yet a desirable ‘to-be’ state has always been described in the various project artifacts. So if that is the case, why has change management sometimes felt like an option?

Researchers estimate that 50-70 percent of transformation projects are not rated as successful. Tasler, Nick. 2017 “Stop Using the Excuse "Organizational Change Is Hard". Clearly, change management needs to improve.

Such rapid change across an organisation at the same time needs to balance dynamism with stability. To change to a platform, that balance must extend across the entire ecosystem and the bigger the ecosystem, the more who will be impacted if change fails.

Skills upgrade

The biggest change to an organisation is not to the systems and processes, it’s the skills of the people. Technology is constantly changing and there will always be gaps as organisations continually reposition themselves. These gaps can usually be quickly filled. Harder to fill are the gaps across the entire workforce.

Leadership must have a skills agenda and perpetually review where skills are needed and how they might be acquired.

Learning on the job is a big part of development, with training programs to back it up. Team composition is one way to pass skills on, project rotation, short term assignments to different teams, and coaching. New areas such as virtual reality simulations and gamification hold great potential and are already used in the military.

Change management of the future will align the adoption of platforms with exponential technologies, and the organisation changes that are required to exploit them. For example, data analysis cannot be held because IT is a choke point as more and more unique requests for answers to business queries are made. A culture of analytics must be pervasive and citizen data scientists developed for faster insights, requiring a large scale change program across the organisation.

Robotics (bots) can be applied many areas, not just to speed up data entry. Automated regression testing is often neglected, and human skill is wasted in repeating the same tests. Inevitably boredom sets in and regression bugs often go missed. 

Control Towers

Control towers act as a central hub with next generation technology at the base, monitoring of the workflows at mid-levels, and at the top of the tower, for senior leadership, visibility into the success of its strategic (platform) objectives.

Control towers work best with agile working, using the constant feedback, and outcome measurement to facilitate rapid decision making and adaptation. Activity is monitored in real time to ensure data gets to where it is needed.


Read more:

Intelligent Workflows (3 of 4)


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