How to best utilize an evolving tool.
By Nicole Needles
ARTIFICIAL INTELLIGENCE (AI) may be the most discussed and misunderstood technology shaping today’s business landscape. For material handling professionals, the opportunities are significant, but so are the misconceptions. Crystal Washington, author, futurist and speaker at MHEDA’s 2026 Convention, says the industry’s next competitive edge won’t come from adopting AI the fastest, but from adopting it the smartest.
“AI is only a tool,” Washington said. “It’s just a hammer. You need the education and the knowledge of the system that you’re looking to fix to be able to use that hammer.” In a wide-ranging conversation, Washington shared practical uses of AI today, common pitfalls, what the next three to five years may hold and why transparency and culture will ultimately set thriving companies apart from the rest.
AI as a “Second Brain” – Not a Replacement
When it comes to immediate, practical applications, Washington says office-side professionals, such as sales, marketing and business management, have some of the clearest opportunities.
“You’re going to look at AI a great deal for creating text,” she said, pointing to marketing content, sales messaging and training resources. But the real power comes from using AI collaboratively rather than as an instant-output machine.
“It really acts as almost a second brain that you can go back and forth with,” she said. “The trick is to use it more to go back and forth than to always just have it create something immediately.”
She emphasized that AI’s strength lies in accelerating creativity, customizing learning modules and quickly updating training materials in response to regulatory changes or client requirements.
“Again, the technology doesn’t replace any of those office positions,” she said. “It can almost put a battery on the back of those people to help them get more done or get things done more creatively.”
Addressing the Job-replacement Myth
One of the most common concerns in the industry is whether AI will eliminate jobs. Washington doesn’t shy away from that fear, and she says much of it is driven by hype.
“A lot of the concern comes from the CEOs of AI companies saying that AI will replace jobs,” she said. “Their goal is to try to get the buy-in from consumers and build up the expectation so that investors will invest more.”
The reality, she said, is far less dramatic. “The technology is not strong enough to replace very many jobs. Certainly, it can’t replace any jobs that are not repetitive,” Washington said. Positions requiring client interaction, problem-solving or variable workflows remain safely beyond the reach of full automation.
She pointed to recent headlines, such as fintech company Klarna’s claim that AI helped replace thousands of customer service workers, as cautionary tales.
“Just months later, they had to walk that back because it was a complete nightmare,” she said. “They were pulling people from other departments to replace the customer service area.”
Where AI can make a difference is in supplementing staffing in hard-to-fill roles or supporting warehouse robotics.
“AI plus robotics might be able to replace some warehouse positions, depending on how the warehouse is set up,” she said. “But then the question becomes, are there opportunities for retraining?”
Adopting AI Strategically
Washington cautions against viewing AI adoption as a binary choice between those who adapt and those who are left behind. In her view, moving too fast can be just as harmful as moving too slow. “It’s dangerous to jump on something immediately without ensuring it’s a good fit,” she said. “And it’s dangerous refusing to research if something’s a good fit.”
The organizations most likely to use AI successfully are those that deeply understand their own business. “They understand what their clients and customers need. They understand the culture of their organization,” Washington said. “Then we use all this information to identify if there are specific types of AI that help address some of the challenges they’ve already identified.”
In other words: Identify the pain point first, then adopt the technology, not the other way around.
The Data That Matters Most
For companies looking to leverage AI, data is the foundation. Washington suggests focusing on information streams that enable prediction. “If you could predict what would happen next, that’s the kind of data you want to start to capture,” she said.
That includes:
- Sensor and environmental data, such as temperature, vibration and other conditions tied to stored or moved materials.
- Customer behavior patterns, which can improve forecasting and service personalization.
- Predictive maintenance indicators help prevent downtime.
- Employee-related data that can flag early signs of turnover risk.
- Route, timing and workflow data that reveal efficiency opportunities.
Culture First, Technology Second
If there’s one mistake Washington sees repeatedly, it’s leaders pushing AI from the top down without involving the broader team. “One of the most common pitfalls I see is it being dictated from the C-suite,” she said. “Team members might be scared or resentful. Who wants to train something they believe is going to take their job?”
Washington urges that buy-in must happen before implementation. To make this happen, it requires active listening from all sides of the issue. “Let them know: ‘We’re thinking about adopting some technologies around AI to help with some of the biggest issues. What are some of the issues you think it can help us with? What should we be mindful of?” Washington said.
To further aid buy-in, she encourages companies to start with the tools employees already use rather than piling on entirely new platforms.
“When existing technologies you already use start to integrate new types of AI, you’re not reinventing the wheel,” she said.
The Foggy but Transformative Years Ahead
Predicting the near future of AI is challenging, in part because the technology faces limitations in the availability of training data. “They are running out of good, quality data to train their large language models on,” she said. “That will result in them almost eating their own sludge.”
Still, she sees several clear trends emerging:
- AI will be more seamlessly integrated into everyday business tools.
- Cybersecurity threats will rise sharply, from voice-spoofing fraud to hyper-realistic fake invoices.
- Organizations will differentiate themselves through service, with some leaning into high-touch, high-human models and others adopting more automation-heavy operations.
- Education and data protection will become core competencies, not optional add-ons.
“The need to educate our team members on how to spot scams and schemes is going to increase tenfold,” she said.
AI Is Not a Strategy, It’s a Tool
Perhaps Washington’s most important message is that AI should never be treated as its own standalone initiative.
“You don’t need a social media strategy; you need a marketing strategy,” she said. “The phone is one tool for sales. AI is one tool that can help in business operations, marketing, sales – many parts of the business – but in and of itself, it is only a tool.”
She also called on business leaders to advocate for thoughtful guardrails.
“We need to give serious thought to how we’re going to influence politicians, business leaders and peers on setting boundaries around AI,” she said. “Technology should exist to serve humanity, not the other way around.”
As the material handling industry navigates rapid change, Washington’s advice offers a grounding perspective: Stay curious, stay cautious and stay human. In the end, the companies that will thrive aren’t those that adopt the most AI. They’re the ones who adopt it with purpose.
Crystal Washington is a futurist, author and speaker who helps organizations become “future-proof.” She will be speaking on how to spot business trends in advance at MHEDA’s 2026 Convention.
Article Takeaways
1. AI Is a Tool, Not a Strategy. – Companies should treat AI as a supportive tool that enhances existing operations, not as a standalone solution or business strategy.
2. Culture Determines AI Success. – Successful adoption depends on involving employees early, building trust and aligning AI tools with real organizational needs.
3. Prepare for Both Opportunity and Risk. – AI will bring greater efficiency and integration, but rising cybersecurity threats and data challenges require proactive education and safeguards.
