Driving ROI with AI: It’s About Strategy

By Justin Bundick

Artificial Intelligence is reshaping whole industries, yet businesses struggle to convert AI investments into measurable outcomes.

Why? Because it’s a strategic challenge more than a technical one. AI must be treated as a business capability. To that end, here are six AI strategies to drive your ROI.

1. Engage the Business

AI must be grounded in business value. Plant it squarely in the middle of the problems that matter most. Embed AI teams within business units using your preferred methods (project-based consulting, long-term partnerships, fully integrated pods, etc.). But make sure three things are happening organization-wide:

a) Invest in AI literacy at all levels. Train, educate, inform. Get everyone on board. Courses, certifications, town halls. When stakeholders understand AI, they’re far more likely to embrace solutions.

b) Design for humans. AI models should be built with the end user in mind. Intuitive interfaces, clear outputs, and seamless integration with existing processes.

c) Manage change. Deploying AI is a transformative proposition. It impacts every aspect of the organization, from roles to processes to authority. And have a plan to communicate the heck out of everything. If you don’t communicate effectively, you’re not managing anything, including change.

2. Build The Teams

AI is a team sport, not something “tech” or “data science” does. Build multi-disciplinary teams with clearly defined roles. Build multi-disciplinary teams with clearly defined roles, including:

• Product Managers – Align AI initiatives with business strategy and customer needs

• Project Managers – Coordinate timelines, resources, and cross-functional execution

• Data Scientists – Develop models using statistical and machine learning techniques

• Data Engineers – Build and maintain data pipelines and infrastructure

• Machine Learning Engineers – Optimize and deploy models into production environments

• DevOps Engineers – Ensure scalable, secure, and reliable model operations

• UX/UI Engineers – Design intuitive interfaces and user experiences for AI solutions

An organization with this structure is built for speed, quality, and relevance.

3. Standardize the Process

This fundamental sounds trite but isn’t. AI thrives on experimentation, but scaling it requires discipline. Establish a standardized development lifecycle—from problem definition and exploratory analysis to prototyping, piloting, and production—to help teams move quickly while maintaining quality. But make sure to include two critical pieces:

a) Governance structures: Steering councils, review boards, even tiger teams. There are lots of ways to do this, but governance is essential. Never treat it as an afterthought (or risk kissing your ROI goodbye).

b) Performance monitoring: Include traditional machine learning metrics but go well beyond. Generative and agentic AI systems introduce new contextual dimensions –prompts, memory, interaction flows, and more. Traditional metrics will always matter, but contextual frameworks require their own special scrutiny. AI-focused organizations must treat context as a first-class asset.

4. Invest in the Future

And in this case, the future means flexibility and scale. AI demands robust infrastructure and data capabilities. Invest in platforms that support the full lifecycle of AI. Pour resources into:

• Data lakes and streaming platforms for capturing transactional and IoT sensor data

• Cloud infrastructure for flexible (and cost-effective) compute power

• Integrated platforms that encompass the lifecycle of the whole computing spectrum – traditional machine learning, GenAI, and agentic AI products. These platforms should support streaming-based architectures that signal inference invocation and allow for real-time responsiveness across use cases. (In layman’s terms: these platforms should be able to receive signals from a data stream and, in real time, trigger AI models to run).

There’s a lot about the AI-driven future that remains unknown, but your infrastructure doesn’t have to be caught off guard.

5. Model, Model, and Model

AI is not a one-and-done effort. We code. We monitor. We evolve. It requires continuous R&D and disciplined lifecycle management:

• Build a repository of proven modeling techniques—from supervised learning to reinforcement learning—and embed them into standardized development playbooks. And always be sandboxing new techniques. After all, every proven technique was once experimental (and traditional AI is still evolving).

• Generative and agentic AI systems require the care and feeding of foundational large language models: curating training data, fine-tuning for domain specificity, prompt engineering strategies, and monitoring emergent behaviors. Continuously evaluate them as they are deployed in dynamic, real-world environments where safety and performance are paramount.

The dual focus on both traditional and frontier AI ensures responsible innovation that supports operational excellence.

6. Govern Your Way to Innovation

As AI capabilities scale, so do the risks. Robust governance equates to responsible use, legal compliance, and ethical integrity. Include pillars such as:

• Acceptable Use Policies – Clearly defined guardrails for employee interaction with AI tools, acknowledged and enforced across the organization.

• Usage Guidelines – Processes to evaluate AI features embedded in commonly used software, ensuring alignment with enterprise standards.

• Contractual Safeguards – Supplier agreements that address AI-specific concerns such as personal data protection, model training restrictions, and intellectual property rights.

• Risk Management – Proactive identification of (and mitigation strategies for) functional, legal, social, financial, and reputational risks, ranging from poor model accuracy to privacy breaches and regulatory exposure.

Governance is never easy. In some ways, it’s the hardest thing of all. But without good governance, the ability to innovate confidently evaporates, and with it your customers, employees, and brand reputation.

Final Thoughts
Artificial Intelligence is not a plug-and-play solution you can buy off the shelf in anticipation of immediate results (despite what many a vendor will tell you). It’s a strategic capability that must be nurtured through thoughtful investment in people, processes, and technology. Organizations that treat AI as a business enabler (rather than a technical experiment) are best positioned to unlock its full potential – to build human-centered AI that matters.