Return to page’s Strategy and Value Creation Program 

While Data is considered as the OIL of the 21st century, Artificial Intelligence is considered as the force behind the next industrial revolution. The business opportunities predicted are tremendous with Gartner, Deloitte, McKinsey, Accenture pointing out all the revenue growth opportunities across industries and geographies. 

It is estimated that AI Achievers can attribute about 30% of their revenue growth to their efforts in AI. The optimization and differentiation of AI achievers in comparison to their peers has a tremendous impact on their respective industry developments. Examples are AT&T in the area of telecommunication, Commonwealth Bank of Australia in banking and many more.

However, the AI Journey to value creation gets estimated to be about 9 years by Accenture. Furthermore, the journey is not linear. According to research by McKinsey, the value gets created on the last 20% of it. That means that the first 80% will be a journey of trial and error with no or low Return on Investment.

alt alt’s Subject Matter Expert Program for AI Strategy & Value Creation

The goal of our SME - AI Strategy & Value Creation Program is the comprehensive support of our customers’ AI and Business leaders to develop a strategy and a roadmap for AI transformation encompassing their business vision, investments, KPIs, technology capabilities, and the timeline for AI adoption. We will help to develop an advanced analytical acumen to craft future AI initiatives. This program is customized to our customers’ organizational needs and aligns with their AI maturity to help their organization on its path of AI value creation. 

The elements that are driving the success of AI initiatives are related to AI strategy, business understanding and alignment, AI transformation, AI technology and democratization of the value of AI across the organization.

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Designing and Executing an AI Strategy

Understand critical elements of successful AI Strategies and how to execute them

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Understanding Business Needs for AI Application

Builds valuable and low risk use case portfolios that support different business units

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Leading AI Business Transformation

Cooperate with business stakeholders driving their business outcomes and support digital transformation

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AI Analytics and Technology Implementation

Implement analytical and technological requirements step by step dependent on your AI journey step

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Designing and Implementing AI Applications

Design and implement business applications that provide value and fit in business processes

According to Michael Porter, “the essence of strategy is choosing what not to do.” Whatever is left, is going to provide the path forward and will allow a clear focus on value creation. However, designing and executing a thorough AI strategy is a complex topic and includes the integration of the AI strategy in the business model of the organization by aligning its elements to the enterprise’s business strategy. Therefore, it is critical to create all of its elements, such as AI vision, AI mission, an AI value creation approach, the right AI capabilities, and a support system to back AI operations across the organization. 


Enterprise Business Strategy

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AI Vision

How is AI supporting the winning aspiration of the enterprise? 

What is the aspiration of AI within the enterprise?

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AI Mission

Where is AI helping to support enterprise focus areas? 

e.g. to reduce risk, improve revenue, cut costs

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AI Value Creation

How is AI going to create business value through its capabilities of disaggregated decision making?

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AI Capabilities

What is necessary to execute the first three steps?

AI Skills & Experiece, AI Analytics & Data, AI Technology, AI Transformation

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AI Support

How is AI supposed to get integrated and managed?

Creating AI Strategy, Communicating AI Strategy, AI Investments

The understanding of business needs requires comprehension of business and AI opportunities, feasibility, and the evaluation of change management efforts. The assessment of use cases and the creation of a use case portfolio are the outcome to create a value path for the AI unit. One of the biggest challenges of AI in organizations is the misunderstanding and fear of the consequences of decision automation. 

Business Values

Business Value can be measured in financial outcomes, risk, customer experience, and many more...


Feasibility includes skills, data, technology, and is directly related to project complexity.

Change Management

Change Management related to need to adjust current operations based on disruption with AI.

Change Management Change Management

AI transformation requires change management experience, a strategy and resources to build trust to shape the new world of AI driven decision making. Helping to understand how AI can create value and to leverage its benefits will drive AI adoption across the enterprise.


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Every time we make a decision it's an opportunity to use machine learning—what customers to market to, what products to offer them, what terms accompany the relationship, what rewards to offer, what spending limits to put in place, how to identify fraud, and so forth." 

Rob Alexander, CIO, Capital One



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Some people call this artificial intelligence, but the reality is this technology will enhance us. So, instead of artificial intelligence, I think we'll augment our intelligence." 

Rob Alexander, CIO, CapitaaGinni Rometty, CEO & President, IBM l One



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Al at its heart, Al is a computer programming that learns and adapts. It can't solve every problem, but its potential to improve our lives is profound." 

Sundar Pichai, CEO, Google

As one of the leaders in AI, has a lot of experience in executing the AI implementation in enterprise architectures and providing insights in technology requirements for value creation along your AI journey.


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The democratization of AI requires the access of business stakeholder to the benefits of AI. This is done through well designed applications, which are implemented in business process to create value across the organization.  Value is created from AI when the business user consumes models to assist or make decisions.

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Customizing an Enterprise’s SME - AI Strategy & Value Creation Program

The path to AI value creation is complex, every enterprise has reached a specific level of AI maturity and requires focus on the gaps on its path to AI value creation. Therefore, we are evaluating together with our customer the stage of their AI Journey and assess the gaps of the elements that are necessary to create value. 

Every step of the AI Journey is important for the next one. The milestones are a representation of AI maturity and the outcome AI can achieve. The AI Achievers in industries such as finance, insurance, automotive and others had to go through each of the steps on their way to create value as well.

Phases of AI Maturity Phases of AI Maturity

Some enterprises have an exceptionally good technical foundation/analytical but are missing the business orientation while others have a particularly good strategy but struggle with their analytical and technical skill level to scale AI across an organization. The gap assessment to find missing elements for AI value creation in combination with the evaluation of a company’s level of AI maturity will allow an organization to focus in order to get to the next step of AI maturity.

A 3 Year AI Journey

According to Accenture, the average AI Journey takes about 9 years. Our SME program is intended to reduce that journey by 50-70%. The goal of the SME Program is to coach and enable our customers to create value within three years.

Phases of AI Maturity Phases of AI Maturity

Within the first year of the SME Program we will assess all gaps for value creation in detail and help to create and align an AI strategy to an enterprise’s business strategy. We will help provide knowledge transfer for all missing elements to create light house projects that can provide proof of value creation. The second year will allow our customers to scale their efforts across verticals and to standardize processes and frameworks that help to create value. In year three our expert team will become bystanders and only mentor the AI initiative to run all operations for value creation.’s experience in AI Transformation has more than a decade of experience working with numerous Fortune 2000 companies on their AI Journey. We have been partnering with their senior and executive management but more importantly with their AI operations and business stakeholders to learn from their trial and error of their AI Journeys. 

The outcome of those partnerships has been the technological enablement of AI Transformation. We are proud to have become one of the leading software providers in the field of AI and ML with the help of our customers. We learned about the struggles of AI model development and deployment and moved from H2O-3 as a great open source product to train individual models to simplify machine learning and developed Driverless AI. We developed MLOps to help our customers to easily deploy and monitor AI models and created Wave (a low code application environment) to augment and automize decision making for business stakeholders.

The second outcome of our partnership with our customers has been the learning of how to drive AI adoption and how to create value with AI. We learned about the milestones of our customers’ AI Journeys and how they reached those milestones. It is the experience in execution that differentiates’s subject matter team from our peers in consulting.

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