How to Build an AI-Ready Team: A Comprehensive Guide for Business Leade

Introduction: The AI Imperative

Artificial intelligence is no longer a futuristic concept—it’s a present-day business necessity. Understanding how artificial intelligence is transforming the world helps contextualize this shift. According to McKinsey’s 2025 State of AI report, more than two-thirds of organizations now use AI in multiple business functions, with half deploying it across three or more areas. Yet here’s the uncomfortable truth: while 89% of executives acknowledge their workforce needs improved AI skills, only 6% have begun meaningful upskilling initiatives.

This gap between aspiration and action represents both a challenge and an opportunity. Organizations that invest in building AI-ready teams today will gain significant competitive advantages, while those that delay risk being left behind in an increasingly automated marketplace.

At Savvycom, we’ve spent over 16 years helping enterprises navigate digital transformation, working with more than 200 clients across APAC, North America, and Europe. In November 2025, we appointed Mr. Tue Nguyen as our Chief AI Officer (CAIO) and launched SavvyAgent—our internal AI assistant—marking our commitment to an AI-driven future. This article distills our experience into actionable guidance for building teams capable of thriving in the AI era.

Understanding What “AI-Ready” Actually Means

Before diving into strategies, let’s clarify what an AI-ready team looks like. It’s not simply about hiring data scientists or purchasing AI tools. An AI-ready team possesses three interconnected capabilities:

1. AI Literacy Across All Levels

Every team member—from marketing executives to customer service representatives—should understand AI’s basic principles, limitations, and ethical considerations. This doesn’t mean everyone needs to code machine learning algorithms, but they should grasp concepts like how AI models learn from data, what biases can emerge, and when AI solutions are appropriate versus when human judgment is essential.

2. Specialized Technical Expertise

Your organization needs individuals who can actually build, deploy, and maintain AI systems. This includes data engineers, ML engineers, AI architects, and prompt engineers. The IBM Global AI Adoption Index 2023 found that 33% of large enterprises identify limited AI skills as their primary barrier to implementation—a bottleneck that only strategic hiring and training can address.

3. Adaptive Mindset and Continuous Learning Culture

AI technology evolves rapidly. Teams that succeeded with yesterday’s tools may struggle with tomorrow’s innovations. Building an AI-ready team means fostering psychological safety for experimentation, establishing learning pathways, and rewarding curiosity alongside results.

The Strategic Framework: Four Pillars of AI Team Building

Based on our experience supporting enterprises through digital transformation, we’ve identified four essential pillars for building AI-ready teams:

Pillar 1: Assessment and Gap Analysis

You cannot build what you don’t understand. Before diving into assessment, ensure your leadership team aligns on the goals of artificial intelligence for your organization. Then start by conducting a comprehensive skills audit:

• Map current capabilities: What AI-adjacent skills already exist? Data analysis, programming, statistical thinking?
• Identify business objectives: Which processes would benefit most from AI augmentation?
• Pinpoint gaps: Where does the gap between current capabilities and future needs lie?
• Prioritize by impact: Which skills will generate the highest ROI when developed?

Tools like skills matrices, competency frameworks, and 360-degree feedback can structure this assessment. The goal is creating a clear picture of where you are versus where you need to be.

Pillar 2: Strategic Hiring and Team Composition

Not every organization needs an army of data scientists. The right team composition depends on your AI maturity and business objectives. Consider these role categories:

Core Technical Roles:

• AI/ML Engineers: Build and optimize machine learning models
• Data Engineers: Design infrastructure for data collection, storage, and processing
• Data Scientists: Analyze patterns and derive actionable insights
• MLOps Engineers: Manage deployment, monitoring, and maintenance of AI systems

Hybrid Roles:

• AI Product Managers: Bridge technical capabilities with business needs
• AI Ethics Officers: Ensure responsible AI development and deployment
• Prompt Engineers: Optimize interactions with large language models

When hiring proves challenging—and given the global AI talent shortage, it often will—consider partnering with specialized software development companies that can provide dedicated teams with proven AI expertise. Experience in managing software development teams with cross-functional AI capabilities is essential when selecting such partners. This approach allows you to scale capabilities quickly while building internal competencies.

Pillar 3: Upskilling and Reskilling Programs

Hiring alone won’t solve the AI skills gap. Your existing workforce represents untapped potential—people who already understand your business, culture, and customers. A joint Gallup-Amazon survey found that 71% of workers who improved through upskilling reported increased job satisfaction, while 68% expressed willingness to retrain for future career success.

Effective upskilling programs share several characteristics:

1. Personalization: AI-powered learning platforms can tailor training to individual needs, learning styles, and career goals.
2. Practicality: Training should connect to real work challenges. Abstract theory without application rarely sticks.
3. Progression: Create clear pathways from basic AI literacy to advanced specialization, with milestones and certifications along the way.
4. Protected time: Learning cannot happen in spare moments. Allocate dedicated time for skill development.

At Savvycom, our Edison Technology Academy has trained over 1,000 technology professionals annually, partnering with leading institutions like RMIT University and the University of Sydney. This investment in human capital development has been essential to maintaining our technical excellence.

Pillar 4: Culture and Leadership Alignment

McKinsey’s research reveals that AI high performers are three times more likely than peers to have senior leaders who demonstrate ownership and commitment to AI initiatives. Culture flows from leadership—if executives treat AI as a side project rather than a strategic priority, teams will follow suit.

Building an AI-positive culture requires:

• Executive sponsorship: Leaders must visibly champion AI initiatives, allocate resources, and role-model AI tool usage.
• Psychological safety: Teams need permission to experiment, fail, and learn without fear of punishment.
• Cross-functional collaboration: AI projects require diverse perspectives—break down silos between technical and business teams.
• Clear communication: Address employee concerns about job displacement honestly while emphasizing AI as augmentation, not replacement.

The Human Skills That AI Cannot Replace

While technical AI skills receive significant attention, the World Economic Forum’s research emphasizes that human skills—communication, critical thinking, emotional intelligence, and leadership—remain in high demand. These capabilities complement AI rather than compete with it.

AI excels at processing data, identifying patterns, and automating repetitive tasks. However, it struggles with judgment calls, cultural awareness, ethical reasoning, and creative problem-solving in ambiguous situations. Building an AI-ready team means developing both technical competencies and these irreplaceable human capabilities.

Consider how this plays out in practice: when evaluating a marketing automation product overview, an AI system might analyze customer data and predict churn risk with high accuracy. But deciding how to intervene with at-risk customers—what message to send, how to personalize the approach, when human contact is warranted—requires emotional intelligence, ethical judgment, and strategic thinking that AI cannot provide.

Organizations should therefore invest in personal development training that focuses on:

• Critical evaluation skills: The ability to assess AI outputs, identify potential biases, and know when to override algorithmic recommendations
• Communication and collaboration: Working effectively across teams to translate AI insights into business action
• Adaptability and learning agility: The capacity to continuously update skills as AI technologies evolve
• Ethical reasoning: Understanding the societal implications of AI deployment and making responsible choices

Practical Implementation: A 90-Day Roadmap

Theory must translate to action. Here’s a practical roadmap for initiating your AI team-building journey:

1. Foundation

• Conduct skills assessment across departments
• Identify 2-3 high-impact AI use cases aligned with business objectives
• Appoint an AI champion or committee to drive initiatives
• Audit existing data infrastructure and quality
• Research training programs and potential technology partners

2. Activation

• Launch AI literacy program for all employees
• Begin specialized training for identified high-potential employees
• Initiate pilot project with clear success metrics
• Establish governance frameworks for AI ethics and data usage
• Create feedback mechanisms to capture learnings

3. Acceleration

• Evaluate pilot results and iterate
• Scale successful initiatives to additional departments
• Formalize AI career pathways and incentive structures
• Assess need for additional hiring or external partnerships
• Develop long-term AI strategy roadmap

Common Pitfalls to Avoid

Based on our experience supporting digital transformation initiatives, here are mistakes we’ve seen organizations make—and how to avoid them:

Pitfall 1: Tool-First Thinking

Purchasing AI software before understanding business needs or building team capabilities leads to expensive shelfware. Strategy and skills must precede technology acquisition.

Pitfall 2: Ignoring Change Management

AI adoption is fundamentally about changing how people work. Technical implementation without attention to human factors—fear, resistance, workflow disruption—results in failed projects regardless of how good the technology is.

Pitfall 3: Underestimating Data Requirements

AI systems require quality data. Organizations often discover their data is fragmented, inconsistent, or incomplete only after beginning AI projects. Data infrastructure investment should parallel team development.

Pitfall 4: Treating AI as IT’s Responsibility Alone

AI impacts every function—marketing, operations, finance, HR. Siloing AI within IT prevents cross-functional innovation and limits value creation.

Real-World Application: Lessons from the Field

Theory is valuable, but practical application reveals the nuances. Here are insights from organizations that have successfully built AI-ready teams:

The Importance of Quick Wins

Organizations that sustain AI momentum typically start with projects that deliver visible results within 90 days. These quick wins build organizational confidence, demonstrate ROI to skeptical stakeholders, and create internal champions who advocate for broader AI adoption. A marketing team automating email personalization, for example, can show measurable engagement improvements quickly—building support for more ambitious initiatives.

Cross-Functional Teams Outperform Siloed Ones

The most successful AI implementations involve diverse teams combining technical expertise with domain knowledge. A data scientist working in isolation may build a technically elegant model that solves the wrong problem. When business analysts, operations managers, and technical specialists collaborate from project inception, solutions are both feasible and valuable.

Governance Cannot Be an Afterthought

AI systems make decisions that affect customers, employees, and communities. Organizations that establish governance frameworks early—addressing data privacy, algorithmic fairness, transparency requirements, and accountability structures—avoid costly retrofitting later. Moreover, proactive governance builds trust with stakeholders increasingly concerned about responsible AI use.

The Role of External Partnerships

Building AI capabilities entirely in-house isn’t always feasible or efficient. Strategic partnerships can accelerate your journey in several ways:

• Dedicated development teams: Access experienced AI engineers without lengthy recruitment processes
• Knowledge transfer: Learn from partners’ expertise while building internal capabilities
• Scalable resources: Expand or contract team size based on project needs
• Risk mitigation: Leverage proven methodologies and avoid common implementation mistakes

When evaluating partners, look for demonstrated AI expertise, industry-specific experience, and a track record of successful project delivery. The right partner functions as an extension of your team, not just a vendor.

Conclusion: The Time to Act Is Now

As Harvard’s Christina Inge observed: “Your job will not be taken by AI. It will be taken by a person who knows how to use AI.” This insight applies equally to organizations. Companies won’t be disrupted by AI itself, but by competitors who harness AI more effectively.

Building an AI-ready team is not a one-time project but an ongoing commitment. It requires strategic investment in people, processes, and partnerships. The organizations that begin this journey today—systematically assessing capabilities, hiring strategically, upskilling continuously, and fostering AI-positive cultures—will be positioned to lead in an increasingly intelligent future.

The question isn’t whether your organization needs AI capabilities. It’s whether you’ll build them proactively or scramble to catch up later. The choice, and the competitive advantage that follows, is yours.

Ready to Build Your AI-Ready Team?

Savvycom helps enterprises accelerate their AI transformation journey through dedicated development teams, custom AI solutions, and digital transformation consulting. With over 700 technology professionals, 16 years of experience, and a newly established AI leadership under our Chief AI Officer, we’re equipped to support your organization’s AI ambitions.

Contact us to discuss how we can help you build the team and capabilities needed for success in the AI era.

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About Savvycom

Savvycom is a leading multinational digital transformation company headquartered in Vietnam, with offices across 7 countries including the USA, Australia, Thailand, South Korea, Japan, and Singapore. Founded in 2009 by CEO Ms. Van Dang, Savvycom has served over 200 clients globally, delivering 500+ successful projects in BFSI, Healthcare, Retail, and Technology sectors. Our services include AI/ML solutions, custom software development, dedicated development teams, and digital transformation consulting.