Episode 102: AI Adoption: How to Make AI Work for Your Business

Kevin Dean05/08/2025

The rise of AI has sparked excitement across industries, but in reality, many businesses are struggling to move from experimentation to real impact. AI isn’t failing; rather, companies are failing to implement it strategically. In a recent conversation on #shifthappens, Kevin Dean, President and CEO of ManoByte, shares how organizations can harness AI’s power and make it a driving force for growth.

Why AI Adoption Fails and How to Fix It

AI projects often don’t fail due to AI's limitations, but rather because of poor planning, lack of alignment, and weak data foundations. Many companies fall into the "AI tourism" trap — launching flashy pilots that fail to connect with business value.

Kevin emphasizes that before diving into technology, businesses must first set real goals. This goes beyond assigning AI to a department or buying access to a large language model (LLM). It requires aligning AI initiatives with strategic objectives and ensuring operational maturity to support them.

Here are several critical factors that separate successful AI initiatives from disappointing ones:

Stop Chasing AI and Make It Work: AI can’t succeed without a clear purpose. Too often, organizations dive in without first defining the business problem they aim to solve. This leads to wasted effort and misaligned priorities. AI works when it’s aligned with business needs, integrated into workflows, and backed by strong data discipline. Without this foundation, even the most advanced models will fall short. To move beyond experimentation, organizations must ask:

• What are the top pain points or inefficiencies we want to solve with AI?
• Can AI support better decisions or faster outcomes in these areas?
• Are we measuring results with metrics tied to business goals?

IT’s Role is Changing — Keep Up: AI is redefining what it means to work in IT. As Kevin puts it, IT leaders must become tech strategists. Instead of focusing solely on systems management, the next generation of IT leaders will be expected to drive business outcomes, shape digital strategy, and guide responsible AI adoption. These roles will bridge business needs with technical capabilities, focusing more on:

• Establishing governance and guardrails for ethical AI use
• Partnering with business units to drive AI-aligned strategy
• Balancing innovation with cybersecurity and compliance

IT leaders who embrace these responsibilities will lead their organizations into an AI-powered future.

Clean Up Your Data or Risk Bad AI: Bad data is one of the biggest silent killers of AI projects. Even the most sophisticated algorithms can’t generate valuable insights from poor data. AI requires structured, clean, and context-rich data to function effectively. Without it, output becomes biased or unpredictable, and AI’s potential turns into a liability. Kevin stresses that businesses must prioritize data governance to ensure data is accurate, up-to-date, and free from biases that could distort AI-driven decisions.

AI Won’t Stay Smart Unless You Keep Improving

AI is not a one-time implementation. It’s a dynamic process requiring continuous refinement. AI drift – when models stop performing well because conditions have changed – is a real threat. It isn’t a “set it and forget it” technology. Success requires ongoing monitoring, refinement, and optimization.

To maintain AI’s value, organizations must:

• Retrain AI models to address new business challenges
• Optimize data pipelines for improved AI accuracy
• Monitor performance to prevent AI drift

Companies that approach AI as a one-time investment will quickly fall behind. Sustained impact comes from treating AI as a dynamic capability, not a static solution.

The SHARK Framework

To help businesses navigate AI transformation, Kevin introduces the SHARK framework:

• Strategize: Identify real business problems. What’s worth solving with AI?
• Hybridize: Blend business and tech roles. Break down silos between IT, operations, and leadership.
• Activate: Build the right data pipelines and governance for responsible AI.
• Revolutionize: Don’t just improve — reimagine how work gets done with AI.
• Kaizen: Commit to continuous improvement. Monitor, adapt, and evolve your models.

Kevin summarizes the SHARK mindset by emphasizing the importance of constant movement and adaptation. It’s a reminder that transformation isn’t static. Just like sharks are always in motion, companies should stay proactive, continuously evolving, and making strategic moves to stay ahead. This mindset drives their approach to helping businesses adopt AI and stay competitive.

AI Use Cases Driving Tangible Results

It’s easy to assume agentic AI is still in its infancy, but Kevin shares an example that shows how it’s already delivering tangible value, especially when used to solve human-centric problems.

In one project, his team developed a digital agent to help families navigate complex decisions around senior care. The agent wasn’t just informative, it was emotionally intelligent. “The digital agent uses tone detection to determine how they're interacting and changes the persona of the person interacting with them to either be more direct or softer,” he adds.

This AI-powered system could understand emotional cues, personalize recommendations, and adapt its tone based on stress levels. The result? A solution that feels more like a human advisor than a bot.

That’s the kind of AI that makes a difference — solving real pain points, scaling human expertise, and creating measurable impact.

The Future of Work Will Change, Not Disappear

AI often sparks fear of job displacement, but Kevin argues it will transform, not eliminate, jobs. As AI advances, new roles and opportunities will emerge. Professionals should focus on developing AI literacy to understand its potential rather than fearing it.

The workforce of the future will be AI-augmented, not AI-replaced. Employees should embrace adaptability and use AI to enhance their work, ensuring they remain relevant in a rapidly evolving job market.

The Path Forward: AI as a Competitive Advantage

AI adoption must move beyond hype to strategic execution. Businesses that align AI with their strategic goals, build strong data foundations, and commit to continuous improvement will gain a competitive edge.

“Stop treating AI as an experiment,” Kevin says. “Think about your business strategy. It starts with executive buy-in, clear business objectives, and a strong data foundation. I think that's number one. The second is to think like a shark. Stay hungry, keep moving, keep evolving, and do what you can to stay on top of your game. That's how you're going to dominate.”

AI, when applied strategically, can drive business forward. However, like any tool, it requires careful planning, continuous adaptation, and alignment with core business objectives. For successful AI adoption and implementation, organizations should start small, scale smart, and always stay agile.

Episode Resources

#shifthappens Research: AI & Information Management Report

#shifthappens Podcast: The Transformative Power of AI Agents

#shifthappens Podcast: Moving Beyond AI Hype to Responsible Implementation

Kevin Dean on LinkedIn

ManoByte website

Dux Raymond Sy on LinkedIn

Mario Carvajal on LinkedIn