The Digital Transformation (DX) to AI Transformation (AIX) Journey Map

An Executive's Guide to Technology Modernization

In today's rapidly changing business environment, executive teams face a significant challenge: how to transition from improving data systems to implementing advanced computing technologies. As organizations work on enhancing their data management, new computational methods emerge that demand attention and strategic planning.

The 4 Stages of Technology Transformation

1. Systems: Building the Foundation to access Data via Systems of Record (SoR)

2. Business Intelligence (BI): Gaining Insights from Data

3. Machine Learning (ML): Forecasting & Predicting Outcomes

4. Artificial Intelligence (AI): Automating Decision-Making & Building Data Products

Key Insight: Stages 1 and 2 focus on data management improvements, while Stages 3 and 4 represent advancements in computational capabilities. The most significant return on technology investments often comes from the transition from Stage 3 to Stage 4.

Stage 1: Systems - Building the Foundation to access Data

"And you know something is happening but you don't know what it is / Do you, Mr. Jones?" – Bob Dylan

Key focus: Implementing and integrating critical data systems across your organization.

Understanding Core Data Systems AKA "Systems of Record" (SoRs)

A System of Record (SoR) is the primary source of information for a specific type of data in an organization. These systems are crucial for managing distinct data sets such as:

• Customer information
• Product details
• Sales data
• Financial records
• Production data
• Supply Chain data
• Operational data

SoRs can be classified by main business processes:

1. How a company creates and markets products

2. How it chooses, sells, and replenishes products

3. How it evaluates and reports performance

For a detailed breakdown of core data systems by business process, refer to SoR chart below.

Technical Debt & Data Debt

System inefficiencies arise from missing, outdated, underused, or disconnected data systems AKA "Technical Debt." This can result from:

• Lack of company-wide technology standards leading to unofficial software adoption
• Mergers and acquisitions
• Older systems built before better options were available

Data quality issues, caused by system inefficiencies, AKA "Data Debt" can be even more problematic. They primarily affect two major areas:

1. Product information (often first encountered by manufacturing companies)
2. Customer data (often first encountered by service-oriented companies)

The burden of manual work, corrections, duplicate efforts, and inaccurate information often signals to leadership that significant system improvements are needed.

Prioritize Critical Data Debt

Most CIOs admit their organizations today are buried in years of tech debt. However, you only need to pay down the tech debt that causes critical data debt, because it represents the main obstacle to getting from digital transformation (DX) to AI transformation (AIX).

The key to improving data systems involves:

1. Implement a comprehensive data integration system AKA "Integration Layer" that connects various software applications globally.
2. Establish a central data storage system AKA "Enterprise Data Warehouse" (EDW) that works with regional data storage solutions.

This approach ensures smooth information flow across the organization while maintaining both local relevance and overall consistency.

Industry Observation: Research firm Gartner reports that technology spending averages 3% to 6% of revenue, with variations by industry. Financial services and technology companies may spend up to 10%, while manufacturing and retail typically spend 1% to 2%. However, inconsistent technology investment can lead to varying system capabilities over time.

Key Steps:

• Key Upgrades & Integration: Implement and integrate systems for unified, up-to-date information across departments.
• Scale & Globalize IT: Expand IT infrastructure for reliable global operations.
• Analytics-Centric Architecture: Build IT architecture prioritizing efficient data analytics.
• Clear Data Debt Blockers: Resolve underlying issues hindering data accuracy and availability.
• Robust Integration Layer: Implement ESB and iPaaS for seamless data flow and system integration.
• Global EDW Architecture: Establish global data warehouse fed by regional data lakes for local and global coherence.

Challenge: How to effectively improve technology capabilities while addressing system inefficiencies and data quality issues? The journey from basic to advanced capabilities requires careful planning and execution.

Stage 2: Business Intelligence (BI): Gaining Insights from Data

"Act on the basis of facts, not on the basis of feelings." – Jack Welch

Key focus: Transforming raw data into useful business insights.

At this stage, organizations face a crucial decision: trust data or trust intuition? This transition can be challenging, especially when past successes are attributed to gut-feel decisions made before comprehensive data was available.

Key Steps:

- Establish reliable data collection and analysis processes

- Create an efficient data analysis workflow

- Develop important performance indicators, reports, and visual data summaries

- Implement methods to understand what happened in the past

Challenge: How do you create a data-informed culture while respecting institutional knowledge?

?Stage 2 - Business Intelligence (BI)

“Act on the basis of facts, not on the basis of feelings.” – Jack Welch

So you have well-defined, global, systems of record generating all kinds of information. Can you harness and use it? To be able to do that you must first be able to collect it in an automated and efficient manner.

The main challenges at the BI stage are often a combination of people and process versus technology. At some point in its evolution, provided that an organization has systems of record that are providing streams of useful data, it has to make a key decision - trust our data or trust our feelings? This is harder than it seems because a lot of institutional folklore can be tied up in good gut feel decisions made in the past before data was available. The folks who made these decisions might be revered in the organization as magicians because they made magic happen. Now, we have data science which often finds itself at odds with magic.

Building upon the foundational systems established in Stage 1, Stage 2 focuses on transforming collected data into actionable insights. The transition from having robust systems to leveraging them for business intelligence is crucial. In this stage, organizations start to harness the power of their integrated systems to drive decision-making processes.

?Stage 2 Milestones

Robust Data Collection & Analysis: Establishing automated processes for collecting and analyzing data from various systems of record, ensuring that the data is accurate, complete, and timely. This involves deploying ETL (extract, transform, load) tools to automate data extraction, transformation, and loading into a centralized data warehouse. Example: Implementing ETL processes to consolidate data from multiple sources.

Business Intelligence (BI) Pipeline: Creating a streamlined BI pipeline that transforms raw data into meaningful insights through a series of data processing and analysis steps. This includes setting up data warehouses, data marts, and BI tools for reporting and visualization. Example: Developing a data warehouse and BI tools to generate reports and dashboards.

Core KPIs, Reports, Dashboards: Developing and implementing key performance indicators (KPIs), reports, and dashboards that provide actionable insights into business performance and support data-driven decision-making. This involves defining metrics that align with business objectives and creating interactive dashboards for real-time monitoring. Example: Setting up a sales dashboard that tracks monthly sales performance against targets.

Descriptive Analytics (“What”): Utilizing descriptive analytics to understand historical data and identify patterns and trends, providing a solid foundation for further analysis and decision-making. This involves using statistical techniques and data visualization tools to analyze past performance and identify key trends. Example: Analyzing historical sales data to identify seasonality trends and customer buying patterns.

?Stage 2 Challenge

“Got data - now what?”: Addressing the challenge of transforming collected data into actionable insights and ensuring that data-driven decision-making becomes an integral part of the organizational culture. This involves fostering a data-driven mindset among employees and providing training on BI tools and techniques. Example: Training staff to use BI tools effectively and incorporate data insights into their daily decision-making processes.

?Stage 3 - Machine Learning (ML)

“For the listener, who listens in the snow / and, nothing himself, beholds nothing that is not there and the nothing that is.” – Wallace Stevens

Stage 3 represents the shift from traditional data analysis to the implementation of advanced machine learning techniques. This stage leverages the robust data infrastructure established in Stage 2 to create predictive and diagnostic models that drive efficiency and innovation.

Having established a strong foundation in BI, the organization can now transition to incorporating machine learning. Stage 3 builds on the descriptive analytics of Stage 2 by introducing predictive and diagnostic capabilities, allowing for more sophisticated data-driven decision-making.

?Stage 3 Milestones

Internal Data Products: Developing internal data products such as predictive models, recommendation systems, and optimization algorithms that leverage machine learning to improve business processes and outcomes. This includes creating models for demand forecasting, customer segmentation, and predictive maintenance. Example: Developing a recommendation system to personalize customer experiences and increase sales.

Machine Learning (ML) Pipeline: Establishing a robust ML pipeline that automates the process of data collection, model training, evaluation, and deployment, ensuring that machine learning models are continuously updated and refined. This involves setting up data pipelines, model versioning, and automated deployment frameworks. Example: Building a pipeline that automatically retrains models with new data and deploys updates seamlessly.

Data-Driven Efficiency: Achieving significant improvements in operational efficiency by leveraging machine learning to automate repetitive tasks, optimize resource allocation, and enhance decision-making processes. This includes deploying machine learning models in production to support real-time decision-making. Example: Implementing an ML-driven inventory management system to optimize stock levels and reduce costs.

Diagnostic Analytics (“Why”): Utilizing diagnostic analytics to understand the underlying reasons behind observed patterns and trends, enabling the identification of root causes and the development of targeted interventions. This involves using machine learning models to analyze complex datasets and uncover hidden relationships. Example: Using ML models to analyze customer churn data and identify key factors contributing to customer attrition.

?Stage 3 Challenge

“What is the maximum ROI for minimum R&D?”: Balancing the need for research and development (R&D) investment in machine learning with the goal of maximizing return on investment (ROI), ensuring that ML initiatives are cost-effective and deliver tangible business value. This involves prioritizing ML projects based on their potential impact and feasibility. Example: Prioritizing ML projects that have a clear path to ROI and measurable business impact.

?Stage 4 - Artificial Intelligence (AI)

“Some [people] see things as they are and ask why. Others dream things that never were and ask why not.” – George Bernard Shaw

Stage 4 signifies the culmination of the AI transformation journey, where organizations leverage advanced AI capabilities to create intelligent systems that drive innovation and competitive advantage.

Stage 4 builds upon the predictive and diagnostic capabilities developed in Stage 3. By integrating AI into the core operations, organizations can move from understanding and predicting outcomes to automating decision-making processes and creating smart, autonomous systems.

?Stage 4 Milestones

External Data Products: Developing and commercializing AI-powered products and services that provide value to external customers and create new revenue streams for the company. This involves creating AI-driven applications and platforms that address customer needs and market demands. Example: Developing an AI-powered virtual assistant that helps customers with complex technical support queries.

Smart Automations: Implementing advanced AI-driven automation solutions that enhance operational efficiency, reduce costs, and improve overall business performance. This includes deploying robotic process automation (RPA) and AI-powered workflow automation. Example: Implementing an AI-driven quality control system in a manufacturing process to detect defects in real-time.

Predictive Analytics (“When”): Utilizing predictive analytics to forecast future trends and outcomes, enabling proactive decision-making and strategic planning. This involves using AI models to predict market trends, customer behavior, and operational risks. Example: Using predictive models to forecast demand and adjust supply chain operations accordingly.

Prescriptive Analytics (“If/then”): Leveraging prescriptive analytics to provide actionable recommendations based on predictive insights, helping the organization to identify the best course of action in various scenarios. This involves using AI algorithms to simulate different scenarios and recommend optimal decisions. Example: Implementing a prescriptive analytics solution to optimize pricing strategies based on market conditions and customer preferences.

?Stage 4 Challenge

“Where’s our AI moat?”: Establishing a competitive advantage through AI capabilities, creating a sustainable "moat" that differentiates the company from its competitors and ensures long-term success. This involves continuously innovating and improving AI solutions to maintain a competitive edge. Example: Investing in cutting-edge AI research and development to stay ahead of industry trends and create unique, high-value AI products and services.

?Stage 4 May Not be For Everybody

Not every company needs to, or can, reach Stage 4. Many organizations might find that their optimal operational efficiency and market competitiveness are achieved in Stage 3, focusing on internal improvements through machine learning rather than full-fledged AI products. Sometimes the best strategy for an organization is to have a really solid Stage 3-grade platform that enables their customers to get to Stage 4. For instance, a SaaS company providing robust predictive analytics tools and machine learning models to its clients can empower those clients to develop their AI capabilities while the company itself maintains a focus on perfecting its ML offerings. Example: A SaaS provider might offer advanced predictive analytics and machine learning tools that allow their customers to create AI-driven solutions tailored to their specific needs, without the SaaS provider needing to become an AI-centric company themselves. This approach allows the SaaS company to capitalize on their ML expertise while enabling their customers to reach AI transformation.

"Does using Generative AI like ChatGPT Make us an AI company?"

Typically not, in the same way that using a web browser and having internet access doesn't make you an internet company though some companies in the early days of internet mass adoption did apply this liberal definition to themselves. Today, nearly every company is an “internet company.” Nevertheless, adopting new productivity tools into daily tasks at a company does make staff more efficient, competitive and stay abreast of technology trends in a hands-on way. These are all positive things. It’s quite possible that we would see workers start many of their tasks “AI-first” and then subsequently edit or refine the output from the AI tool. Certain writing-oriented professions have started adopting LLMs in first drafts of contracts or marketing copy and many coders have started using co-pilots and LLMs to create their first version of code for a software project. Often, this AI-generated code is good enough to run on a production system with minimal refinement. Some of these trends might result in certain companies identifying as AI-centric sooner than others, but widespread AI adoption is still in its infancy.

?Stages Can Overlap

Transformation is a dynamic process, whether digital or AI transformation. Various regions or subsidiaries in an organization might be at different stages on the journey map. Additionally, different departments might progress at varying speeds, creating a multifaceted landscape of capabilities within the same company. This overlap can be beneficial as it fosters a cross-pollination of ideas and practices, ultimately accelerating overall transformation. The ultimate goal is to get every global and regional division in the company to the level of the highest-performing one, ensuring uniform excellence across the organization.

Does Everyone Need to Get to Stage 4?

Not every company needs to, or can, reach Stage 4. Many organizations might find that their optimal operational efficiency and market competitiveness are achieved in Stage 3, focusing on internal improvements through machine learning rather than full-fledged AI products. Sometimes the best strategy for an organization is to have a really solid Stage 3-grade platform that enables their customers to get to Stage 4. For instance, a manufacturing company providing robust predictive maintenance tools and machine learning models to its clients can empower those clients to develop their AI capabilities while the company itself maintains a focus on perfecting its ML offerings. Example: A manufacturing firm might offer advanced predictive maintenance and machine learning tools that allow their customers to create AI-driven solutions tailored to their specific needs, without the manufacturing firm needing to become an AI-centric company themselves. It's essential to recognize that the journey to AI transformation is not one-size-fits-all. Each company must assess its unique needs, resources, and strategic goals to determine the most appropriate stage to aim for, ensuring that the pursuit of AI transformation is aligned with realistic expectations and sustainable growth.

Key Takeaways for Executive Teams

1. Return on Investment: The transition from Stage 3 to Stage 4 often provides the most significant returns on technology investments made during earlier stages.

2. Varied Progress: Different departments or regions may advance at different speeds. This variation can encourage the sharing of ideas and practices, accelerating overall progress.

3. Optimal Stage Varies: Peak efficiency might be achieved at Stage 3 for some organizations. Some companies may find their strength in providing robust Stage 3 capabilities that enable their customers to reach Stage 4.

4. Tools ≠ Transformation: Using advanced software tools doesn't automatically make you a technology leader, but it can significantly improve productivity and keep staff current with technology trends.

5. Customized Approach: Assess your company's specific needs, resources, and goals to determine the most appropriate stage to aim for. The pursuit of advanced computing should align with realistic expectations and sustainable growth.

As an executive team, your role is to guide your organization through this transformation journey. By understanding these stages and their challenges, you can make informed decisions that will position your company for future success.

Remember, the goal isn't just to reach the final stage – it's to create sustainable growth and maintain a competitive edge in a technology-driven world. Where does your company stand on this journey, and what's your next move to maximize the potential of your technology investments?

Ariel Jalali is CEO of Paragon Tech , an CIO-led advisory firm helping mid to upper mid-market companies through their journey from digital transformation to AI transformation. He is a world expert in applied AI/ML, lecturer and author.