AiThority Interview with Michael Kwok, Vice President, IBM Bob, and Canada Lab Director

With every business looking to become AI-native, what are some of the AI adoption and deployment fundamentals that organizations need to keep in mind? Michael Kwok, Vice President, IBM Bob, and Canada Lab Director shares a few proven tips in this chat with AiThority:

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Hi Michael, tell us about your journey through the tech world and your time at IBM.

I’ve spent more than 20 years in the technology industry, working across software engineering, product management, and business leadership. Throughout my career, I’ve been passionate about helping organizations use technology to solve complex problems and create tangible business value.

Today, I serve as Vice President of IBM Bob and Director of the IBM Canada Lab. I have the opportunity to lead teams focused on applying AI and automation to software development while supporting one of Canada’s largest software development organizations.

One of the reasons I’ve stayed at IBM is its ability to continually reinvent itself. I’ve seen the industry evolve through multiple technology shifts, but the rise of AI is one of the most transformative. What excites me most is helping clients move beyond experimentation and use AI to improve productivity, accelerate innovation, and drive measurable business outcomes.

Also Read: AiThority Interview with Gou Rao, co-founder and CEO at NeuBird AI

Tell us about some of the latest IBM BOB enhancements and how they benefit end users?

IBM recently expanded IBM Bob with three major areas of focus: multi-agent capabilities, built-in AI cost analytics, and specialized modernization workflows for enterprise environments.

The new multi-agent architecture allows Bob to break complex projects down into specialized tasks that can be executed by multiple AI agents working together. This allows developers and engineering leaders to spend less time managing workflows and more time focused on higher-value engineering decisions.

We also introduced Bobalytics, which provides visibility into how AI is being used, the value it’s creating, and the costs associated with it. As organizations continue trying to scale AI, many struggle to understand how it’s driving value and where costs are accumulating. Bobalytics makes AI investments more transparent so organizations can optimize AI across individuals and teams.

In addition, IBM Bob now includes pre-built modernization workflows for environments such as Java, IBM Z, and IBM i. These workflows help enterprises modernize legacy applications more consistently, reducing risk while accelerating transformation initiatives. With faster delivery, improved governance, and better visibility into AI spend, enterprises can have more confidence in using AI for large-scale modernization efforts.

What other fundamentals would you give business heads and organizations to help mitigate rising AI usage costs specifically?

The biggest mistake organizations make is treating AI costs as a technology problem when it’s really an operating model problem.

First, organizations need visibility into how AI is being used and where value is being created. Without clear insight into metrics like usage patterns, productivity gains, and spending trends, leaders are effectively managing AI investments in the dark. This is why capabilities like Bobalytics are becoming increasingly important. They help organizations:

• Drive adoption by identifying successful usage patterns and replicating them across teams and code repositories.
• Prove business value by correlating AI usage with productivity improvements, modernization outcomes, and business results.
• Optimize costs through granular visibility into spend patterns, license utilization, and resource allocation.

Second, organizations should focus on matching the right model to the right task. Not every request requires the largest or most expensive model. Companies that can intelligently route workloads based on complexity can often significantly reduce costs while maintaining quality.

Finally, governance should be embedded from the beginning. When organizations introduce AI without clear standards, accountability, and oversight, they often create hidden costs through rework, security reviews, compliance issues, and duplicated efforts across teams. Organizations need to treat governance as an enabler of scale.

It’s being said that AI development costs will exceed human developer costs, what should organizations do today to prevent future issues as they build AI-human teams?

I don’t believe the future is a choice between AI and developers. Future high-performing teams will be the ones where humans and AI work together effectively. The winners won’t be the companies with the most AI. They’ll be the companies that create the most effective AI-human operating model.

Organizations should start by defining which tasks are best handled by AI and which require human judgment. AI is exceptionally good at accelerating repetitive work, documentation, code generation, analysis, and modernization activities. Humans remain essential for architecture decisions, risk management, governance, customer understanding, creativity, and accountability.

Companies should also invest in role evolution. The most valuable engineers in the coming years will be those who can orchestrate AI systems, validate outputs, govern quality, and connect technical decisions to business outcomes. Companies should be training for those skills today.

It’s also important to establish transparent accountability. Every organization needs clarity on who owns a decision when AI contributes to the outcome. Human oversight, auditability, and governance become increasingly important as AI-generated work scales across the enterprise.

Can you give us a quick peek into some of IBM’s near future AI focus areas?

Our priority is to help businesses make the transition from AI experimentation to AI execution at scale. We’ll continue to help enterprises use agentic AI in ways that are secure, governable, and tied to measurable business outcomes. Organizations are moving beyond asking, “Can AI do this?” and are now asking, “How do we scale AI responsibly across thousands of employees and business processes?” Enterprises will see value from AI when they have orchestrated AI systems that can work across workflows, applications, and teams.

With this in mind, we’re investing in what organizations will need to manage AI as a core part of their business: governance, cost optimization, orchestration and observability. As AI adoption grows, leaders need the same level of visibility and control over AI operations that they expect from cloud, cybersecurity, and financial systems. Trust, transparency, and accountability will become competitive differentiators.

At the same time, we continue to believe the future is hybrid and open. For example, enterprises want flexibility to use the right model for the right workload, whether that model is open source, proprietary, small, large, cloud-based, or deployed closer to where their data resides. Helping customers operate in complex, hybrid environments remains a major focus.

What aspects of the current state of AI most interest and worry you?

What most excites me is that we’re seeing AI move from being a productivity tool to becoming an operational capability. For years, technology primarily helped people work faster. Today, AI has the potential to help organizations work differently. We’re seeing opportunities to augment decision-making, accelerate innovation cycles, improve customer experiences, and unlock expertise that was previously trapped in systems, documents, and organizational silos.

What worries me is not the technology itself but the gap between adoption and governance.

Many organizations are deploying AI faster than they are building the frameworks necessary to manage it. Questions around accountability, security, compliance, model transparency, and cost management are becoming increasingly important. As AI becomes more autonomous, governance can no longer be an afterthought.

What five thoughts on the future of business and AI would you leave us with before we wrap up?

1. Every business will become an AI-native business.

Just as digital transformation became a business imperative, AI will become embedded in nearly every function, from software development and operations to finance, customer service, supply chains, and HR. The question won’t be whether you use AI, but rather how effectively you use it.

2. Competitive advantage will come from execution, not access.

AI models are becoming increasingly accessible. What will differentiate organizations is their ability to combine AI with proprietary data, institutional expertise, governance, and business processes to create unique value.

3. Human expertise will become more essential, not less.

The most successful organizations will use AI to elevate human potential, not replace it. Judgment, creativity, leadership, empathy, domain expertise, and accountability will remain essential business capabilities.

4. Trust will be a strategic asset.

More and more, we’ll see customers, employees, regulators, and partners evaluate organizations based on how responsibly they deploy AI. Companies that prioritize transparency, security, governance, and ethical use will build stronger and more durable relationships with stakeholders.

5. The winners will focus on outcomes.

The market is moving beyond AI demonstrations and pilot projects. Business leaders are increasingly asking a simple question: What measurable value does this create? The organizations that can connect AI investments to productivity, growth, innovation, and customer outcomes will be the ones that lead the next decade.

Also Read: ​​AI and The Future of Work: Artificial Intelligence Is Expanding Organizational Intelligence Beyond Human Limits

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