In this conversation, Chris Caldwell of Concentrix and Jordan Wilson of Everyday AI explore the dual nature of AI assistants and agentic AI. The core thesis is that while these tools offer unprecedented opportunities for efficiency and personalized experiences, their rapid advancement also introduces significant risks, particularly in customer interactions and cybersecurity. Hidden consequences emerge from the ease with which consumers can now orchestrate complex, high-volume demands on businesses, and the potential for malicious actors to exploit AI for sophisticated fraud and harassment. Enterprises, regardless of size, must proactively strategize for this shift, integrating AI not just for cost savings but to manage new forms of engagement and risk. This discussion is crucial for business leaders, strategists, and anyone involved in customer experience or technology adoption who needs to navigate the complex landscape of AI's immediate benefits and its long-term, often unseen, implications. By understanding these dynamics, they can gain a significant advantage in preparing for the AI-driven future of 2026 and beyond.

The Undeniable Upside and the Ugly Downside: Navigating the AI Assistant Revolution

The year is 2026. Your company is either at the forefront of leveraging AI assistants, or it's rapidly falling behind. This isn't a distant future; it's a near-term reality that demands immediate attention. In a recent conversation on the Everyday AI Podcast, Chris Caldwell, President and CEO of Concentrix, and host Jordan Wilson delved into the profound, often contradictory, impact of AI assistants and agentic AI. While the allure of enhanced productivity and personalized customer experiences is undeniable, the conversation revealed a more complex ecosystem of hidden consequences and systemic shifts that most businesses are ill-equipped to handle. The obvious benefits of AI are clear -- automation, efficiency, 24/7 service. However, what Caldwell and Wilson illuminate is how these very advantages, when amplified by agentic AI, can create new vulnerabilities and challenges that traditional business strategies simply cannot address. The true competitive advantage lies not in adopting AI, but in understanding and preparing for its full spectrum of downstream effects.

Why the Obvious Fix Makes Things Worse: The Amplified Consumer

The initial promise of AI assistants and agentic AI for businesses centers on addressing existing inefficiencies. As Chris Caldwell explained, agentic AI excels at bridging "tech deficits," automating complex tasks that previously required multiple systems and human intervention. This means faster resolutions for customers and a more streamlined operational experience for companies. For instance, a bank can now automate the intricate process of mortgage applications, allowing customers to solicit bids from numerous institutions simultaneously. This empowers consumers with unprecedented access to information and negotiation power, a clear upside.

However, this empowerment has a significant flip side. Caldwell highlighted that agentic AI doesn't just benefit businesses; it dramatically amplifies consumer power. What was once a single customer, Jordan Wilson, can now become a hundred Jordans, each with an AI assistant capable of orchestrating complex interactions. This shift fundamentally alters the dynamic of customer engagement. A simple complaint about an $8 item, previously dismissed due to the hassle of customer service, can now escalate into a persistent, multi-channel barrage orchestrated by an AI. Caldwell cautioned, "Brands, you need to think about this and think about how to interact with consumers differently because they're going to have so much more power because of agentic AI."

This isn't just about volume; it's about the sophistication of demands. In healthcare, for example, consumers can now leverage AI to research complex medical procedures, compile supporting case studies, and craft highly informed complaint letters or appeals. This elevates the baseline expectation for company responses, requiring a higher level of expertise and engagement than ever before. The immediate benefit of AI-driven efficiency for businesses is thus counterbalanced by the downstream effect of an empowered, and potentially more demanding, customer base.

The Dark Side of Delegation: Exploiting Trust and Identity

The rise of agentic AI introduces a new frontier of risks, particularly in cybersecurity and the manipulation of trust. Caldwell pointed to the alarming rise of sophisticated scams where deepfakes and voice cloning, achievable in mere seconds, can be used to impersonate individuals. An AI-powered bot can call a company, convincingly mimic a known employee like Jordan Wilson, and provide credentials to gain access. This isn't just about impersonation; it's about taking over an individual's digital identity, granting an attacker access to all their connected systems and data.

This delegation of agency to AI, while powerful, also means that if an AI assistant is compromised, the damage can be exponentially greater than a single compromised human account. The concept of a "hundred Jordans" can be turned against a company, with each AI agent acting maliciously or persistently.

Furthermore, the ease with which AI can automate communication creates new avenues for harassment and disruption. Caldwell described a scenario where a disgruntled customer, unable to resolve a minor issue, could direct their AI assistant to call a company every 15 minutes, or even task multiple AI agents to flood the company's systems. This escalates a minor dispute into a significant operational burden. The downstream effect of enabling AI to act on behalf of individuals is a heightened risk of coordinated, persistent, and sophisticated attacks that can overwhelm traditional defenses.

The 18-Month Payoff Nobody Wants to Wait For: Building Resilient Systems

The rapid advancement of AI, with new models emerging constantly, signals an acceleration in adoption. While current adoption of personal AI assistants is still relatively low, analysts predict a dramatic surge by mid-2026 to early 2027. This timeline suggests that companies have a limited window to prepare for the full impact. The temptation, Caldwell noted, is to focus on immediate gains, such as cost reduction through automation. However, this overlooks the long-term implications.

The true competitive advantage, according to the principles discussed, lies in embracing solutions that require patience and upfront investment, even if they seem difficult or unpopular in the short term. For example, implementing robust AI governance, training LLMs with accurate and secure data, and developing sophisticated identity management systems are not quick fixes. These are investments that build resilience against the downstream consequences of AI misuse.

Caldwell emphasized the need for companies to equip themselves with AI, but critically, to ensure the "right LLM model training and tuning so that they can handle this volume that is coming in and that it is accurate." This requires a strategic approach that prioritizes accuracy and security over sheer speed. The risk of companies generating "wild answers that get pinged up on the news" is a direct consequence of neglecting this foundational work. The payoff for this diligence--a robust, secure, and trustworthy AI infrastructure--will only become apparent over time, creating a durable moat against competitors who prioritize short-term gains.

Where Immediate Pain Creates Lasting Moats: Low-Tech Defenses for High-Tech Problems

In the face of sophisticated AI-driven threats, the instinct might be to develop equally advanced AI-based countermeasures. However, Caldwell highlighted a crucial insight: sometimes, the most effective solutions to high-tech problems are surprisingly low-tech. This principle of embracing discomfort for long-term gain is a recurring theme.

For instance, to combat AI-powered scams and impersonation, companies are implementing voice validation systems that use secure, known phone numbers. If an incoming call requests sensitive information, the system hangs up and redials a verified number to confirm the caller's identity. Similarly, security protocols are evolving beyond easily guessable information like mother's maiden names to more complex, multi-factor authentication methods.

This approach acknowledges that while AI can automate and amplify, it often struggles with nuanced human verification and established security practices. The "discomfort" for businesses lies in the perceived backwardness of these methods compared to cutting-edge AI. Yet, these low-tech solutions, when implemented effectively, create a significant barrier against AI-driven attacks, offering a more durable defense than purely technological solutions that can themselves be exploited. This is where patience--waiting for the right, often simpler, solution to emerge--creates a distinct advantage.

How the System Routes Around Your Solution: Agent-to-Agent Interactions

The future of AI interaction is increasingly moving towards agent-to-agent communication. Caldwell described this as a natural evolution, akin to microservices where AI agents exchange data directly, bypassing human language altogether to solve problems. This "AI talking to AI" scenario presents both opportunities and challenges. On one hand, it promises unprecedented efficiency for transactional tasks. On the other, it raises questions about oversight and control.

The system's response to this trend is not uniform. Caldwell noted a cultural divergence: businesses in Asia are more comfortable with "companion AI" that makes decisions autonomously, while North America and Europe lean towards "delegated AI," where human oversight remains crucial. This cultural difference highlights how the system adapts based on user comfort and perceived risk.

The implication for businesses is that a one-size-fits-all approach to AI delegation will fail. Companies need to understand their customer base and internal culture to determine the appropriate level of AI agency. For instance, while an AI assistant might be ideal for managing e-commerce shopping based on preferences, a sensitive customer service issue might still require human empathy. The brands that succeed will be those that can intelligently route conversations--whether human-to-human, AI-to-AI, or AI-to-human--to the most appropriate channel, ensuring that the technology serves, rather than dictates, the customer experience. This requires a deep understanding of system dynamics and human behavior, a challenge that requires more than just technological deployment.

The Human Element in an Automated World: Brand Promise and Empathy

As AI assistants become more capable, the question arises: will businesses market themselves as "human-only" to stand out? Caldwell suggested that while some brands will indeed need to deliver their promise through human interaction, the core driver for customer preference isn't necessarily human contact itself, but rather frictionless and effective service. If technology can deliver a superior experience, customers will opt for it. The desire for human interaction often stems from specific needs--empathy, complex problem-solving, or a desire for a personal connection.

The challenge for enterprises is to balance the need for AI-driven scalability with the preservation of human touch. This involves understanding where AI excels and where human intervention is indispensable. Caldwell stated, "If something can be done frictionlessly with technology, people want to do that. If they want to interact with a human, they want to do that." The brands that will thrive are those that can accurately discern these preferences and route interactions accordingly.

This requires a sophisticated understanding of brand promise. A brand built on luxury, for instance, might necessitate a higher degree of human interaction than a brand focused on speed and convenience. The danger lies in a race to the bottom, where companies implement AI solely to reduce costs, sacrificing the very elements that make a customer experience memorable and valuable. The ultimate advantage will go to those who master this delicate balance, ensuring that AI supports, rather than replaces, the core values of their brand.

Key Action Items

• Embrace and Experiment with AI Personally: Dedicate time to using various AI platforms and assistants. Understand their capabilities and limitations firsthand to build intuition and identify "wow moments" that can inform customer-facing strategies. (Immediate Action)
• Develop Robust AI Governance and Training Protocols: Invest in thorough training and tuning of LLMs for accuracy, security, and brand alignment. This is crucial for managing the volume and sophistication of AI-driven interactions. (Immediate Action, pays off in 6-12 months)
• Implement Layered Security and Identity Management: Combine advanced AI security with proven low-tech solutions like voice validation and complex multi-factor authentication to defend against AI-powered impersonation and fraud. (Immediate Action, ongoing investment)
• Strategize for Agent-to-Agent Communication: Begin planning how your organization will handle automated interactions between AI systems, considering data exchange protocols and ensuring alignment with business objectives. (This pays off in 12-18 months)
• Map Customer Journey Touchpoints for AI vs. Human Interaction: Analyze where AI can provide frictionless service and where human empathy and complex problem-solving are essential. Design systems to intelligently route these interactions. (This pays off in 6-12 months)
• Cultivate a Culture of AI Literacy and Adaptability: Educate your teams on the evolving capabilities and risks of AI. Foster an environment where experimentation is encouraged, and learning from both successes and failures is prioritized. (Ongoing Investment)
• Define and Reinforce Your Brand Promise in the AI Era: Clearly articulate what your brand stands for and how technology, including AI, will either support or detract from that promise. Ensure customer experience strategies align with this core identity. (This pays off in 12-18 months)