AI Strategy Steps Flashcards
(18 cards)
Where do most organization go wrong?
They get hung up on the technology itself, and they don’t link their AI efforts to actual business objectives. And if you can’t prove that your AI projects deliver value and impact the bottom line, the momentum you’ve built around AI in your organization will surely start to fade. And once that momentum disappears, it can be incredibly difficult to gain support for future AI initiatives.
What are the initial 3 steps?
Define the organization’s vision for AI.
Evaluate risks and establish AI governance.
Identify and assess AI use cases.
Why is having a data strategy important when planning for AI?
Data is the foundation of all AI systems.
If you haven’t already, start thinking about your roadmap for developing your organization’s practices around data quality, management, infrastructure, unification, governance, scalability, and so on. Ultimately, a solid data strategy maximizes the value your organization can derive from its AI investments.
Why is important to assemble strategic stakeholders?
Will need to establish an AI center of excellence (also called a council, advisory board, or committee). The goal is to assemble a multidisciplinary leadership team with expertise in technology, data, department-specific knowledge, and the needs of the business. The makeup of the committee depends on each unique organization, but generally, leadership from the following areas should be represented.
Business departments (sales, marketing, support, and so on)
Information technology
Security
Engineering
Data science
Business intelligence
Legal
Finance
Operations
One of the benefits of forming a cross-functional team is that it encourages collaboration. Individual departments are less likely to move forward with their own fragmented AI solutions without considering company-wide concerns, such as duplication of effort, integration, scalability, governance, and security. Plus, it can prevent a disconnect between the technology teams that develop AI solutions and the departments trying to achieve specific business goals.
What are the AI committee’s responsibilities?
Defining the organization’s AI vision
Securing executive buy-in
Identifying strategic barriers
Establishing AI governance
Evaluating potential use cases and building an AI roadmap
Making decisions about core AI and data capabilities
Allocating resources
Planning for scale and organizational change
Championing adoption
Reviewing outcomes and evolving the strategy
Having a centralized AI committee shouldn’t be a barrier to innovation. That’s why we recommend taking a two-pronged approach to AI strategy.
Bottom-up: Democratize access to AI, allowing employees to experiment. It encourages enthusiasm and familiarity with the technology and sparks potential use cases. You can centralize and scale the best ideas.
Top-down: Provide direction from leadership to define the overall AI strategy and transform vision into value.
What are the most important steps in Defining an AI Vison?
Establish Business Goals - quantified and measurable
When it comes to prioritizing AI use cases, it’s important to think strategically about which projects can deliver the greatest value for the business.
Align on AI Ambitions - take an incremental approach to AI transformation
Short-term: Start small with internal pilot projects that boost productivity and improve employee satisfaction. Experiment quickly by customizing out-of-the-box AI solutions that integrate easily into existing workflows. Build internal support for AI, boost adoption, and learn important lessons before deploying AI solutions to customers.
Mid-term: Implement both turnkey and custom AI solutions for employees and customers that require more effort and AI maturity. Solutions for employees might focus on front office and back office use cases. Solutions for customers might include personalization, AI agents, and more.
Long-term: Incorporate AI into R&D and product development to build AI-powered products and services. These projects require a lot of time and effort, but they can eventually result in game-changing outcomes and competitive advantage.
Define AI Governance
AI governance is a set of policies, processes, and best practices that help organizations ensure that AI systems are developed, used, and managed in a responsible and scalable way to maximize benefits and mitigate risks.
Note that if the responsibility for AI Governance falls within an existing Governance organization, it is important that that organization has the expertise to do so. It may require additional training and enhancement of existing practices to address gaps.
Principles for Responsible AI
The committee and management should take a step back and think about how to focus and commit to responsible development of AI.
Sample set of principals:
Responsible
We strive to safeguard human rights, protect the data we are trusted with, observe scientific standards and enforce policies against abuse. We expect our customers to use our AI responsibly and in compliance with their agreements with us, including our Acceptable Use Policy.
Accountable
We believe in holding ourselves accountable to our customers, partners, and society. We will seek independent feedback for continuous improvement of our practice and policies and work to mitigate harm to customers and consumers.
Transparent
We strive to ensure our customers understand the “why” behind each AI-driven recommendation and prediction so they can make informed decisions, identify unintended outcomes and mitigate harm.
Empowering
We believe AI is best utilised when paired with human ability, augmenting people, and enabling them to make better decisions. We aspire to create technology that empowers everyone to be more productive and drive greater impact within their organisations.
Inclusive
AI should improve the human condition and represent the values of all those impacted, not just the creators. We will advance diversity, promote equality, and foster equity through AI.
First step in AI Governance - Inventory Your Organization’s AI Usage
It’s hard to properly assess the risks until you know where the risks are coming from. So it’s a good idea to inventory all AI tools to understand the extent of their integration into business processes.
Identify AI technologies: List all the AI technologies currently in use, including everything from simple automation tools to complex machine learning models. Don’t overlook AI integrated into third-party services and software.
Document use cases: For each AI technology, document its specific use cases. Understanding what each AI solution does and why it’s used helps you to evaluate its impact and importance.
Map data flows: Track how data flows to and from each AI application. This includes sources of input data, what the AI modifies or analyzes, and where it sends output data.
Establish ownership: Assign ownership for each AI tool to specific individuals or teams. Knowing who is responsible for each tool ensures accountability and simplifies future audits and assessments.
Update regularly: Make the AI inventory a living document that is updated to reflect new AI deployments or changes to existing ones. This keeps the inventory relevant and useful for ongoing compliance.
What is Shadow AI?
Unauthorized usage. Also called shadow AI, unapproved AI tools present significant risk to the business.
Second Step - Evaluate AI Risks
Identify and Categorize Risk Factors
Assess the Impact and Likelihood
Prioritize Risks
Third Step - Develop Risk Mitigation Strategies
examples of safeguards for different types of risk with mitigation strategies
Technical and Security:
Security policies and protocols
Anomaly detection systems and fallback options
Secure AI infrastructure, tenancy, and hosting
Cybersecurity red-teaming
Data and Privacy:
Access controls
Data anonymization and encryption techniques
Regular data audits
Data misuse policies
Data quality standards
Data cleaning and validation processes
Ethical and Safety:
Responsible AI principles:
Acceptable use policies
Ethical red-teaming
Bias assessment and mitigation tooling
Model benchmarking
Model transparency, such as explainability and citations
Watermarks for AI-generated content
Feedback mechanisms
Audit logs
Operational:
Risk assessments
Incident response plans
Change management
Documentation and company-wide education
Metrics and monitoring
Internal ethics reviews for new AI products and features
Compliance and Legal:
Compliance protocols and training
Legal consultations and contracts
Describe a “right” use case in the context of an organization
The right use cases are the ones that align with your organization’s goals and deliver actual value.
Name some sources for Use Case ideas
Internal crowdsourcing: Encourage employees to experiment and submit suggestions. You can host hackathons and workshops, conduct surveys, set up an idea exchange site, or establish communities (such as Slack channels) to promote education and knowledge sharing.
Business process review: Conduct an in-depth analysis of all your business processes to learn where there are inefficiencies, pain points, and areas where automation and AI can have a significant impact.
User research: Involve the user research team in the ideation stage to make sure that potential AI use cases solve real problems for users.
Data analysis: Your data experts can evaluate patterns and work their data magic to find ideas for AI use cases.
Market trends and research: Use market research to find gaps and opportunities, anticipate changing buyer behavior, and study market risks. Conduct a competitive analysis to understand what competitors are doing with AI so you can assess the strengths and weaknesses of your own AI roadmap.
What is a impact-effort matrix?
There are many ways to prioritize projects, but one simple method is an impact-effort matrix that compares business value to ease of implementation.
Quick wins: Use cases in the upper right quadrant are high-value and low-effort. If you’re relatively new to AI, start here when selecting pilot projects.
Low-hanging fruit: Use cases in the lower right quadrant are low-value and low-effort. Tackle them when more important projects are complete or while waiting for obstacles to be resolved.
Big bets: Use cases in the upper left quadrant can lead to major transformation, but they require a lot of effort and therefore more risk.
Money pit: Use cases in the lower left quadrant are low-value and high-effort, so they aren’t worth pursuing.
The Importance of Data Readiness
data for the project must be accurate, available, accessible, and securely governed.
Many projects get stuck in data readiness because teams are trying to chase perfection. Instead, work with your team to identify reasonable goals for data readiness. You can use the Build stage to identify and address any gaps in your data that affect the AI output.
Data Inventory Key Considerations
Identify what data you need in your project.
Identify where the data is stored.
Answer some questions about your data:
Is the data type structured, unstructured, or semi-structured? (Learn more about data classification in Data Fundamentals for AI.)
How often is your data refreshed?
Is the data updated in real-time, hourly, daily, monthly, or static?
How can the data be accessed?
Have governance standards been implemented for the data?
What are some data considerations that can cause challenges in your project?
Challenges to data
Includes quality issues, integration hurdles, gaps in the data, and sometimes even outdated data infrastructure.
