Artificial Intelligence, deep learning, machine learning — whatever you’re doing if you don’t understand it — learn it. Because otherwise, you’re going to be a dinosaur …

—Mark Cuban, American billionaire entrepreneur [1]

Artificial Intelligence (AI) in SAFe

Definition: Artificial Intelligence (AI) is a term used to describe a wide range of smart machines capable of performing tasks that typically required human intelligence. AI can be applied at all levels of SAFe to build intelligent customer solutions, automate value stream activities, and improve customer insights.

It is a technology that can revolutionize solutions developed by SAFe organizations and has the potential to dramatically influence the operational and business models of enterprises as well as increase individual and team productivity.

(If you are new to AI, consider reading the Introduction to Artificial Intelligence extended guidance article.)

Why AI?

AI opens limitless possibilities. It can extend existing solutions and make them more valuable and scalable. More importantly, it opens a frontier for entirely new solutions and capabilities that benefit the customer and the business in new ways.

Every day, more and more aspects of our personal lives are aided and supported by AI. Applying for a loan, flying on an airplane, shopping in an online store, and scheduling a doctor’s visit are just a few examples of how this technology powers many of our daily activities. Enterprises use AI to understand their customers better and create better products and services. Banks use AI to detect fraud and money laundering. Governments provide services to their citizens using AI. Even military and national security systems have incorporated this disruptive technology.

‘Conventional’ software solutions are good at addressing well-understood problems. Such solutions execute a limited number of scenarios based on a predefined list of rules. However, many organizational workflows and customer scenarios involve parameters that can’t be accounted for via conventional, preprogrammed means. AI addresses many of these most complicated scenarios and turns them into viable business opportunities. The ultimate goal is to create innovative business value ahead of the competition.

Becoming an AI Company

Fully embracing AI transforms enterprises into AI-centric organizations [2]. It’s not just about adopting AI technologies; it’s about embedding AI into the organizational fabric, pivoting to a data-driven operational model with a robust digital infrastructure, and fostering a culture of continuous learning and cross-functional collaboration. This approach integrates AI across business functions, driving innovation and agility and enabling rapid adaptation to market shifts and customer needs.

SAFe can help organizations achieve these goals and realize the benefits of AI. Figure 1 shows the three dimensions SAFe enterprises need to embrace AI as a core competency. Each of these is explained in further detail below.

Figure 1. The three dimensions of AI competency in SAFe

Augmenting the Workforce with AI

Leveraging generative AI in the practice of SAFe is particularly transformative, unlocking new efficiencies and capabilities in various roles. Generative AI accelerates processes, enhances decision-making, reduces toil, and enables teams to adapt swiftly to changing market demands. Here are just a few examples of how generative AI can help many of the most common roles in SAFe.

Scrum Masters/Team Coaches (SM/TC) can use AI to analyze flow data to identify and predict potential bottlenecks and delays and suggest improvements, leading to more effective planning, delivery, and retrospectives.
Product Owners can use AI to suggest enhancements to user stories and acceptance criteria, ensuring the team constantly works on the most valuable backlog items.
Release Train Engineers (RTE) can leverage AI’s real-time data analysis and predictive modeling to identify and address workflow inefficiencies, align capacity with demand, and streamline value delivery.
Lean Portfolio Management can leverage historical performance data and market trends to identify strategic themes and opportunities, enabling more informed decisions on epic prioritization. Additionally, AI-driven analytics can aid in establishing dynamic, data-driven lean budgets and ‘smart’ KPIs, ensuring resources are allocated efficiently and aligned with business objectives, thereby enforcing lean budget guardrails more effectively.
Software engineers realize significant benefits from generative AI ‘copilot’ development tools that assist in coding, automated testing, and code review processes. These tools can suggest optimal coding practices, detect bugs early, and even propose fixes, streamlining the development cycle.

Building AI-Enabled Solutions

Building, operating, and scaling AI-enabled solutions requires AI-enabled people, processes, and technology, focusing on achieving strategic business outcomes.

Enhancing Agile Teams with AI

The journey to developing AI solutions starts with people. Existing Agile Teams and Agile Release Trains (ARTs) within SAFe organizations must integrate specialized roles such as AI specialists, data scientists, and machine learning engineers. These professionals bring indispensable skills in technologies and knowledge domains for building AI product features. These skills may be centralized and deployed to ARTs in the early stages as needed (Figure 2).

Figure 2. Typical stages of organizing for AI development

Furthermore, upskilling current development teams is critical. By creating a baseline of AI knowledge across all teams combined with AI-focused roles or teams within ARTs, organizations can ensure efficient AI integration into existing solutions, fostering an environment where AI and traditional software development synergize to drive innovation. Figure 2 shows the goal of evolving quickly from centralized AI pilot teams to fully integrated AI competency in each ART.

Incorporating AI-Specific Processes into SAFe

Developing AI-enabled solutions requires a unique combination of new and existing processes. In the same way that DevOps focuses on creating alignment and shared goals for building, deploying, and maintaining software, we need a similar approach for our Data, our Models, and our AI technologies. The most relevant of these process models for AI include:

DataOps focuses on managing and utilizing data in a way that is efficient and compliant, ensuring data quality and accessibility.
MLOps, or Machine Learning Operations, involves the practical aspects of deploying and maintaining machine learning models in production environments, ensuring they operate smoothly and reliably.
ModelOps enables organizations to manage and scale AI models effectively, ensuring they remain accurate and efficient over time.
AIOps leverages artificial intelligence to automate and optimize IT operations, integrating various tools into a unified platform for proactive monitoring, quick issue resolution, and continuous improvement. (Figure 3).

Figure 3. AI processes integrated into the Continuous Delivery Pipeline

Integrating these processes into the existing Continuous Delivery Pipeline is crucial for AI development. This integration allows for a smooth blend of AI and traditional software development methodologies. It ensures continuous delivery, adaptation, and improvement in a path optimized for AI-enabled solutions.

Selecting AI Tools and Platforms

Technology selection is pivotal in AI integration. Selecting tools that complement the organization’s existing infrastructure and AI objectives is essential. It’s not just about adopting the most advanced AI technologies; it’s about finding the right fit – tools that offer the desired AI capabilities while seamlessly integrating with current systems and practices. This careful selection ensures a harmonious blend of AI and existing technologies, paving the way for innovation without disrupting established workflows.

Achieving Responsible AI

Even with all the new opportunities AI is creating for organizations, the risks of this emerging technology are very real. In many cases, they are unlike anything experienced in the past. Examples of actual incidents from using AI include instances of bias, fictitious data, confidential information leaks, and lawsuits over copyright infringement.

Despite the tremendous potential and frenzied enthusiasm over AI, many organizations cite risks illustrated by incidents like these as their top reason for delays in moving AI capabilities out of the pilot stage and into full production.

The effort by organizations to create policies and practices to mitigate the unique risks inherent in using artificial intelligence is increasingly being described as adopting Responsible AI. Responsible AI solutions are (Figure 4):

Figure 4. The aspects of responsible AI

Patterns are emerging for implementing Responsible AI, specifically for enterprises using SAFe. SAFe includes many roles and practices that provide a solid foundation for establishing Responsible AI. These synergies, as well as guidance for how to begin creating a Responsible AI program, are further described in the Responsible AI article.

Applying AI to Achieve Better Business Results

Generally, an organization’s opportunities for leveraging AI lie in four areas (Figure 5):

Figure 5. The four primary ways to apply AI in an organization

Increasingly Intelligent Customer Solutions. Enterprises are embedding AI into many products and services we access daily. Common examples include facial recognition on smartphones, personalized product recommendations, and automating time-consuming tasks. In addition, many AI-powered functions are now directly embedded in our mobile and desktop computer software applications.
Improved Operational and Development Value Stream Flow. AI solutions can be applied to both Operational and Development Value Streams. Inventory and demand management, financial forecasting, and chatbot solutions for customer support are examples of how AI can power operational workflows. Companies can also use AI to reduce development time, increase code quality, analyze production data, identify and prioritize product features, and facilitate effective testing within development value streams.
Deeper Customer Insights. AI can help organizations identify new business opportunities, learn more about the customer, and extract market insights that create new offerings on a broader scale. In this latter case, AI supports launching new initiatives that were otherwise undiscoverable via the BAVS (the Business Agility Value Stream).
Improved Workforce Productivity. Generative AI capabilities added to off-the-shelf software products reduce toil, improve quality, fuel innovation, and increase productivity for everyone in the enterprise, including all SAFe roles.

Making AI Investment Decisions

Many AI initiatives fail to deliver the better results promised when advocates propose investments in this technology. This failure to meet expectations is often caused by poor decision-making about how and why AI will be used. Organizations often want to ‘do AI’ because ‘everyone else does,’ without understanding the effort required to operationalize and scale this technology, its impact on the organization, or even if it will produce the intended benefits.

SAFe organizations already have powerful tools to make better decisions on the appropriate use of AI. A few of these are highlighted in Figure 6.

Figure 6. AI decision-making framework enabled by SAFe

Alignment with strategy ensures that AI initiatives pursue beneficial outcomes for the business. Aligning the AI roadmap with the Portfolio strategy is an essential step in this direction. Some AI initiatives may require more financial support; others may need to be repurposed or canceled based on a progressively assessed economic viability. Productively managing the spending requires Lean Budgeting. Using the Portfolio Kanban System and a Lean Business Case helps establish better alignment with strategy. Additionally, PI Planning provides the foundation for recurrent alignment of AI strategy with the actual implementation.
Customer centricity ensures that AI initiatives solve real customer problems. For example, intrinsic AI capabilities (such as image recognition or natural language processing) must be appropriately integrated into a favorable customer scenario. Explicitly defining the customer problem is an important step and hugely benefits from applying Design Thinking to AI capabilities.
Continuous exploration paves the pathway to a successful AI-powered solution. Solution development always contains a significant degree of uncertainty. In the case of AI, however, the level of uncertainty is exceptionally high in creating the right solution and implementing it correctly. This is where the SAFe Lean Startup Cycle is very useful. Creating a clear business hypothesis, building an AI MVP, and validating it against suitable measures are at the core of successful exploration.
Empirical milestones guide the development of a successful AI solution. AI capabilities must be continuously integrated with the rest of the solution throughout the incremental development process. Solution increments are used to elicit important customer feedback.

まとめ

Succeeding with AI is critical to surviving and thriving in the Digital Age. While most organizations are investing in AI, many find it challenging to operationalize and scale their AI-enabled solutions. Operationalizing AI involves implementing policies, processes, and automation to ensure AI is used and deployed responsibly. These actions reduce the risks unique to AI and unlock individual productivity gains and differentiated AI-powered product features.

SAFe provides various roles and practices that enable the successful development and delivery of AI-enabled solutions. It requires organizing the teams and trains to incorporate AI capabilities, establishing essential processes for AI development, and building architectures that enable AI features in products. Organizations must also invest in building an AI-augmented workforce and establishing practices for responsible AI. With these dimensions in place, enterprises can use AI to seize critical business opportunities and accelerate toward becoming an AI company.

詳しく学ぶ

[1] Cuban, Mark. Upfront Summit, 2017.

[2] Iansiti, Marco, and Karim R. Lakhani. Competing in the Age of AI: Strategy and Leadership When Algorithms and Networks Run the World. Harvard Business Review Press, 2020.

The Business Opportunity of AI, November 2023. IDC and Microsoft.

Last Update: 8 April 2024