Towardsanintelligententerpriseecosystem

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1. Introduction

Today an enterprise is evolving into a complex economic, social and business environment, co existing with

suppliers, producers, competitors, other stakeholders and customers. Global trends of production and

distribution in one hand, business dependence on technology evolution on the other (Internet of things,

Cloud and Smart Systems) are leading into an era where people, machines, devices, sensors, and businesses

must all be connected and able to interact with each other.

A new paradigm of doing business is necessary, an Intelligent Enterprise Ecosystem that will offer an

operational and profitable symbiotic relationship between an enterprise, technology, its environment and

knowledge generated by (and influencing) this relationship.

2. Assessing Alignment

For a successful Intelligent Enterprise Ecosystem the alignment of business, technology and knowledge is

sine qua non. The history of theory-building around the concept of alignment is still young and has only

been going on approximately 15 years. The most widespread and accepted framework of alignment (even if

does not include knowledge and by extension analytics as an integral part of business) was proposed by

Henderson and Venkatraman in 1993.

This theoretical construct, also known as the strategic alignment model (SAM), describes the phenomenon

along two dimensions. The dimension of strategic fit differentiates between external focus, directed towards

the business environment, and internal focus, directed towards administrative structures.

The other dimension of functional integration separates business and IT. Altogether, the model defines four

domains that have to be harmonized in order to achieve alignment.

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3. Overcoming Problems in Alignment Implementation

While trying to align technology with the business, many enterprises experience a quite fuzzy target. With

what ‘business’ should technology and knowledge models align? According to the ‘Strategic Alignment

Model’, a first answer should be with the business strategy. In practice business strategy is not often a clear

target following a linear model allowing a blueprint with guidelines.

An enterprise must be able to be responsive to developments in its environment. The company strategy is

therefore not a destiny that is ever reached. In reality strategy provides a trip, not an Ithaca. On the second

level of the Strategic Alignment Model, the alignment is aimed at the business processes and organization.

The organization provides only limited information about the business requirements. It is focused on

hierarchical structure, but not on information content. An additional problem is that in many enterprises the

organization structure is not very stable. Departments and job titles change frequently. The business

processes in the other hand tend to be more stable. In the development of technology applications they

provide an important basis for the analysis of the information requirements. A problem however is that there

are multiple views of the business processes, all with different goals and different content.

As a result of this, most IT Departments development projects will build their own process models

according to their own modeling conventions. And every Business Intelligence project will be standing on

some business requirements and on IT capabilities, with no global strategy.



4. A New Business/Intelligence Alignment Model

Based on 20 years of experience, I propose a new model that incorporates SAM model into a larger,

including knowledge creation and management, knowledge generated especially from the web. This new

model is defined in two axes : Content Base Axe and Process Based Axe.

4.1 Content-Based Axe

Competition goes beyond established industry rivals to include four other competitive forces as well:

customers, suppliers, potential entrants, and substitute products. This market-based view of strategy is

interested in the resources businesses have and treats their behavior as a “black box”. Competitive strategy

determines how the organization gains an advantage over its rivals within chosen market positions. Content

Based Axe is critical as it is the one generating profits for an enterprise

4.2 Process-Based Axe

The alignment models corresponding to the process stream of strategic management focus on the dynamism

of business–technology alignment, the co-evolutionary development of both strategy and IT strategies and

on the social dimension of alignment. These models highlight the importance of the process in which

internal politics, organizational culture, managerial cognition and skills help achieve and maintain high

alignment.

4.3 Strategy As Practice

Targeting efficiency above all, the strategy-as-practice approach understands practice as being “closer” to

reality and delivering a “more accurate” description of the real world phenomena than formal theories

populated by multivariate analyses of firm or industry level factors. Sometimes is also necessary to start

with a strategy focusing to partial business objectives such as loyalty and acquisition, stock management

optimization, etc... Based on this consideration I propose the following methodology.



5. Intelligent Enterprise Ecosystem. Methodology

Directing Intelligence in Enterprise Ecosystem methodology is deployed through the following ontological

values that constitute 5 hierarchical levels :

Business Blueprint. The ability to anticipate unpredictable internal and external changes : ÷ Business

strategy axes description, KPIs, governance, operational models. ÷ Identification of change drivers and their

impact on key business processes. ÷ Implementation planning through existing or new information systems

and data analytics.

Open Architecture. The guide and reference for collaboration of people involved at all levels, for present

and future projects. ÷ Architectural planning of applications and systems to be deployed, their interactions

and their relationships to the core business processes of the enterprise.

Shared Values and Communication. The common language inside an enterprise that permits an effective

communication between business and technology people. ÷ Capturing critical business information, without

being tied to specific technologies. ÷ Modeling of the logical software and hardware environment that is

required to support the deployment of new strategies.

Network of People, Systems and Knowledge. Shared knowledge makes people from different departments

to work together, using the same information. ÷ Building innovative, interconnected, collaborative and

evolutionary analytics that share and exchange data, content and services within the ecosystem.

Enterprise Open to World. Interconnection of an enterprise with the world through all devices ÷ Provision

of the connectivity infrastructure and integration of individual applications and devices into the Enterprise or

Community Ecosystem, based on interoperability standards.



6. Why artificial intelligence

Incorporating the contribution of game theory and decision-making processes, gradually an autonomous

body of research was created : Artificial Intelligence, which exceeds computer science, as it is the only one

that allows the approach and study of complex adaptive systems, such as human behavior.

At the borders of economics, computer science, psychology, sociology, semantics and logic, artificial

intelligence was based on heuristic search, the selective trial and error research.

The development of this science has made possible to have approximate representations of real situations,

more accurate than those generated by operations research algorithms. Being able to face any situation that

could be represented symbolically (verbally or mathematically through diagrams),

Artificial Intelligence allows to extend the use of computers to more complex problems and less structured,

including the most sophisticated forms of reasoning, unique privilege to human crisis till this moment.

Complex problems and unstructured data as the chaotic information flow provided by the web (among other

things).

But there is another "because" for artificial intelligence usage apart their ability to make sense of the mess of

available data. Their ability to be trained, be “educated” by each enterprise to select, analyze and present

useful information to serve business strategy. Their ability to evolve in parallel with each enterprise.


Towardsanintelligententerpriseecosystem

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