There is a wealth of evolving business and IT strategies, disciplines, and technologies, but many of these concepts are technology-driven and lack a unifying vision. For example, digital transformation has taken on an almost entirely IT-centric perspective. The cognitive enterprise, on the other hand, offers a business-driven vision for organizations where technology is merely a means to an end. This Advisor explores some common scenarios that manifest within a cognitive enterprise.

Walking through business scenarios highlights how an organization can leverage the business intelligence embedded in the business knowledgebase with cognitive computing technologies to assess various situations and, where applicable, shift the enterprise from human-led activities to machine-assisted or machine-led activities. These scenarios should not be viewed as an all-or-nothing decision. As a business knowledgebase is deployed, human-led work becomes machine assisted. Over time, automation in the form of virtual stakeholders takes over more and more of the work, to the point, in some cases, that the work shifts from human-led to machine-led.

Supplier Optimization

The following scenario leverages certain business architecture representations in the business knowledge­base, connected to simulation technologies to dynamically identify when a vehicle transport operator should be contracted, assigned to an in-house stakeholder, or replaced with automation in a driverless vehicle.

• Viewing a business ecosystem from the outside-in and the inside-out highlights all third-party and internal points of engagement, when viewed through value streams and stakeholder mappings.

• Stakeholder engagement includes all customer, partner, and internally directed human resources where a given stakeholder may be a partner in one scenario and an internally directed human resource in another scenario.

• Coupling these stakeholder mappings with cognitive computing technologies enables what-if simulations to optimize partner-related, stakeholder engagement.

• Consider a “transport operator” engaged in an Execute Route value stream for a trucking company, where the operator may be (1) an internal human resource, (2) a contractor, or (3) an automated transport controller (i.e., a driverless vehicle).

• Running what-if scenarios across multiple routes, conveyor types, shipment types, policy (legal) constraints, incidents, and events — all stored in the knowledgebase — enables the organization to assess cost, safety, legal, and other considerations as input to supplier utilization versus in-house or automation options.

Product Portfolio Optimization

The product portfolio optimization scenario described below highlights how the cognitive enterprise can incrementally automate roles and tasks performed by product management teams. (As defined here, a product is a named combination of goods and services offered to customers.)

• Product planning is often done by multiple managers across business units with little insight into other planned or deployed products, dependencies, and related automation deployments.

• The business knowledgebase, applying a technique called product mapping, has the ability to identify the enabling capabilities and supporting technologies required to ensure the viability and quality of those products.

• A “virtual product manager” examines a cross-section of product, capability, business unit, stakeholder, information, and technology impacts to determine where existing or planned products are unique, overlap, or conflict.

• The virtual product manager extends this analysis across multiple brands, product lines, business units, and technology deployments and recommends new products, cross-product consolidation and alignment, and product retirements.

• Virtualizing product management roles enables the expedited elimination of nonviable product proposals while decreasing the time it takes to move new product ideas through to market deployment.

Program and Project Optimization

In spite of all the work around project and program management and deployment methodologies, challenged and failed projects remain the norm. Consider, for example, that successful projects are only achieved less than 30% of the time based on 25 years of data. The culprit is often inadequate scope and impact analysis based on a lack of business transparency. The “virtual program manager” leverages the business knowledgebase to:

• Ensure that impacts of strategic objectives are incorporated into program and project definition

• Assist human planning efforts to expose business ecosystem impacts of proposed programs, highlighting cross-program impacts or conflicts

• Validate that a fully coordinated set of programs is deployed so teams can define and stay within their lanes

• Ensure that cross-program coordination is not left to trial and error, expediting program and project startup and execution

IT Architecture Portfolio Optimization

Experience to date highlights two important factors that contribute to weaknesses in IT architectures when it comes to delivering capability automation for their organizations. The number of technology deployments per capability is very high, where, for example, agreement management is replicated and fragmented across hundreds of technology deployments. And many capabilities lack automation entirely.

These factors increase what is termed “business/IT alignment debt.” Business/IT alignment debt differs from traditional technical debt because it is measured from a business perspective. Business/IT alignment debt increases as the ability of IT architecture to ensure the effectiveness of business capabilities decreases.

A cognitive enterprise leverages the business knowledgebase, linked to IT architecture, to highlight degrees and impacts of business/IT alignment debt, recommend actions to reduce this debt, and streamline the time it takes to deliver and execute business strategy. The approach allows organizations to maximize the value of IT investments while concurrently shrinking their IT portfolios, replacing traditional or legacy IT architectures with cognitive computing technologies.

One last point related to IT architecture portfolio optimization and the incremental move toward cognitive technologies involves security. A well-articulated capability map incorporates security controls into every action against a given business object. These security-related capabilities, which include access constraints as well as authorization and authentication management, provide insights into where security can be systemically integrated into cognitive computing or any target state automations.

[For more from the author on this topic, see “The Cognitive Enterprise: Envisioning the Business of the Future.”]