DeepSeek effect on enterprise is bigger than you think

Author

Marketer In The Loop
February 03, 2025

In partnership with

Hi, marketers in the loop,

A big thank you to those who joined our live community session on Friday! One of the key takeaways? We need more time for peer-to-peer discussions and networking.

It got me thinking about a paradox of our time: AI is becoming so good at generating and optimizing content that what’s truly scarce - real human connection - is more valuable than ever. I came across an insightful piece on Big Think that explores this idea: as the digital world grows louder and more automated, we’ll increasingly crave what’s rare, authentic, and deeply human.

With that in mind, stay tuned as we work on new ways to foster meaningful conversations and deeper connections in our community.

Onward,
Rei

P.S.: OpenAI launched a new AI agent on Sunday called Deep Research, and I used it to write the piece about DeepSeek below. The research quality is mind-blowing - it will save me at least 4–6 hours per project. You should give it a try, it will likely be available to Enterprise and Plus users soon.

In this issue:

  • What you should know before using DeepSeek at work

  • Easy to use agentic platforms for marketing workflows

  • Taco Bell, KFC, and Pizza Hut case study

  • And much more

Reading time: 10 minutes

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DEEP DIVE

The Hidden Risks of DeepSeek and Shadow AI in the Enterprise

The rise of “shadow AI” or “BYO AI”, where employees use unapproved AI tools for work, presents major risks. DeepSeek, a high-performing Chinese-developed AI model, has gained rapid adoption (it’s one of the most downloaded apps worldwide), but it raises unique concerns for enterprises.

In this article, we’ll explore the key risks of DeepSeek and similar unvetted AI tools, compare them to established AI platforms like ChatGPT, Gemini, Copilot, and Claude, and provide strategies for business and marketing leaders to mitigate risks while fostering innovation.

 

A Balanced Perspective on AI Governance

It’s important to note I am not against DeepSeek or AI innovation in general. My goal is to propose a balanced and thoughtful approach that weighs both risks and rewards, while highlighting why AI governance matters.

This is a rapidly evolving space. I encourage you to do your own research. Many new articles are emerging on this topic such as DeepSeek hit with ‘large-scale’ cyber-attack after AI chatbot tops app store from The Guardian.

 

7 Key Risks of DeepSeek and Shadow AI

 

1. AI Training Risks: Is Your Data Being Used?

AI providers improve their solutions using user interactions. Unless an AI tool explicitly states otherwise, it’s recommended to assume that any data input is used for training. As an example, OpenAI’s ChatGPT originally trained on user data but later provided opt-outs for business users. DeepSeek has not publicly disclosed its data retention and training policies to the same extent as OpenAI, Google and others, which raises concerns for enterprise users.

 

2. Data Security and Confidentiality Risks

AI tools process and store user inputs, meaning employees might unknowingly expose sensitive business data. For example, Samsung engineers accidentally uploaded proprietary source code to ChatGPT, leading to concerns about unintended leaks. Many major banks and enterprises have banned generative AI over similar fears."

DeepSeek’s security risks stem not only from where it stores data but also from the lack of clear enterprise safeguards against accidental data sharing. Without strict policies, employees may input confidential information into AI tools, which could expose proprietary data.

 

3. Security Vulnerabilities and Cyber Risks

All AI platforms can be exploited by prompt injections (tricking the AI into revealing restricted info) or malicious outputs (e.g., insecure code, phishing attempts). However, DeepSeek faces additional security scrutiny because it has already been targeted by large-scale cyberattacks.

Governments are taking note: Italy and Taiwan have raised security concerns about DeepSeek, while U.S. agencies, including the National Security Council, are reviewing the risks of foreign AI models. Some organizations have restricted unvetted AI tools due to security risks.

 

4. Data Residency and Global Compliance Risks

Many industries like finance, healthcare, and legal must comply with strict data residency laws (e.g., GDPR in the EU, HIPAA in the U.S.). DeepSeek currently does not provide an EU-based data hosting option, which may raise GDPR compliance concerns for businesses handling regulated data.

Comparison with other AI tools: Unlike ChatGPT, Gemini, and Claude, which offer enterprise users some degree of regional data controls, DeepSeek stores all data in China. Under China’s cybersecurity laws, data stored in the country is subject to government access, posing additional compliance risks for enterprises operating in the EU and U.S..

 

5. Hallucination and Misinformation Risks

All AI models hallucinate, meaning they generate false information that sounds plausible. Some benchmarks report that DeepSeek’s R1 model has a hallucination rate of 14.3%, though rates vary depending on tasks and datasets. Without verification, employees risk spreading misleading data in reports, marketing materials, or customer communications.

 

6. Ethical Bias and Censorship Risks

AI tools inherit biases from their training data, potentially leading to unethical or misleading outputs. DeepSeek adheres to Chinese content regulations, restricting responses on topics like Tiananmen Square and China’s political system. While other AI models also have content restrictions, DeepSeek’s censorship aligns with government policies, raising concerns about bias and completeness.

Business impact: similar to other AI tools, if employees rely on DeepSeek for any work related activities they may inadvertently produce biased or incomplete content, harming brand reputation.

 

7. Regulatory and Compliance Risks

Using unapproved AI tools may expose businesses to compliance violations. In regulated industries, using an AI like DeepSeek without governance controls can lead to breach of customer privacy, violations of data-sharing agreements, and regulatory fines and penalties.

Italy, France, and South Korea have expressed concerns about AI data privacy risks, and U.S. regulators (FTC, SEC) are warning businesses about improper AI use. While DeepSeek has not been directly named in major enforcement actions, regulatory scrutiny around AI data handling is increasing.

Additionally, DeepSeek’s terms of service pose legal uncertainties: Dispute resolution is required in China, meaning international businesses have little recourse if legal issues arise.

 

Basic Recommendations for Business and Marketing Leaders

  • Establish an AI Usage Policy: Build a cross-functional team to provide guidelines and educate employees on approved AI tools, data privacy risks, and compliance rules.

  • Use Enterprise-Grade AI Tools: Provide vetted enterprise-grade solutions like ChatGPT Enterprise, Google Gemini, or Microsoft Copilot.

  • Protect Proprietary Data from AI Training: Use AI tools that allow opt-outs from training and ensure employees don’t paste confidential data into AI tools.

  • Partner with IT, Legal and HR: Proactively discuss overall guidance and actions related to new AI tools.

Balancing Innovation and Risk in AI Adoption

The rise of new AI tools like DeepSeek brings both opportunities and risks. While AI can enhance efficiency, creativity, and automation, business and marketing leaders must ensure that AI adoption is secure, ethical, and compliant.

By proactively managing AI risks, companies can innovate responsibly without exposing themselves to data breaches, compliance violations, or reputational harm.

YOU ASK, I ANSWER

“What Are the Best (and Easiest) Platforms to Build AI Agents?”

AI agents have the potential to transform how marketing teams operate - automating repetitive tasks, analyzing customer data in real time, and ensuring consistent brand interactions. The good news? You don’t need advanced programming skills to get started. No-code and low-code platforms now make it easier than ever to explore AI agents quickly and cost-effectively.

But… What Is an AI Agent, Really?

One of the biggest challenges in discussing AI agents is the lack of standardized industry terminology. Depending on the context, an AI agent could refer to anything from a simple automated chatbot to a sophisticated system capable of multi-step reasoning, task execution, and even web browsing. Given this fluid definition, we take a broader perspective in this article - focusing on AI-powered tools that can execute tasks independently, enhance marketing workflows, and improve customer experiences. If you're looking for a deeper dive into AI agent fundamentals, check out my 'AI Agents 101' article.

Why You Should Start Now

AI agents are evolving rapidly, but waiting for a “perfect” solution means falling behind. Early adoption gives marketing teams an edge by allowing them to:

  • Build foundational AI capabilities before technology becomes mainstream.

  • Optimize workflows incrementally, rather than undergoing a massive transformation later.

  • Understand AI’s limitations firsthand and adjust strategies accordingly.

  • Stay ahead of competitors who delay implementation.

When (and When Not) to Use AI Agents

While AI agents offer significant benefits - automation at scale, data-driven insights, and reduced manual workload - they aren’t a universal solution.

When AI Agents Make Sense:
✅ Repetitive, time-consuming tasks (e.g., email categorization, social media responses)
✅ High-volume, data-driven workflows (e.g., personalization, predictive analytics)
✅ Multi-step, automated processes (e.g., lead nurturing sequences)

When to Hold Off:
❌ Tasks requiring deep domain expertise and nuanced decision-making (e.g., legal compliance, medical decisions)
❌ High-stakes creative or emotional work (e.g., brand storytelling, therapy)
❌ Environments with strict regulations or privacy concerns (e.g., finance, healthcare)

10 Key Questions to Ask Before Implementing an AI Agent

Before deploying an AI agent, assess its impact using these questions (plus this guide):

  1. What is the complexity of the task?

  2. How frequently does the task occur?

  3. What is the expected volume of data or queries?

  4. How would be the impact on the human workforce?

  5. What level of accuracy is required?

  6. Is human expertise or emotional intelligence essential?

  7. What are the privacy and security implications?

  8. What are the regulatory and compliance requirements?

  9. What is the cost-benefit analysis?

  10. What does “hiring" and “onboarding” look like? (See example below)

Solutions to Consider

1. What You Already Have in Your Tech Stack

If you're using OpenAI, you already have access to Custom GPTs and, if you're a Pro user, Operator - with Deep Research expected to roll out soon. Microsoft users can tap into Copilot and Copilot Studio, which integrate seamlessly across their ecosystem. Google users, however, may need to wait for broader access to Project Mariner or Agentspace - or apply for early access through experimental programs here and here.

These are just a few options. The best starting point? Look at the AI tools you already have access to and explore their agent-like capabilities - you might be surprised by what they can do.

2. Custom GPTs and Operator (OpenAI)

Custom GPTs make it easy to tailor AI to your specific needs, whether that’s refining your content creation process, incorporating proprietary knowledge, or optimizing workflows. Operator, OpenAI’s more advanced AI agent, can autonomously complete multi-step tasks.

3. Copilot (Microsoft)

Microsoft’s Copilot and Copilot Studio are already embedded in its ecosystem, helping businesses automate internal processes, analyze data, and manage marketing content. For organizations already using Microsoft products, this is a seamless way to integrate AI agents into existing workflows.

Agent.ai describes itself as a "marketplace and professional network for AI agents and the people who love them." Users can create, share, and discover AI agents designed for various tasks - ranging from company research to optimizing landing pages for better conversion rates.

5. Taskade

Originally a productivity and collaboration tool, Taskade now supports AI-driven automation. Users can build AI-powered workflows for task management, team communication, and custom automation needs - all in one streamlined platform.

6. Zapier

Zapier has rebranded Zapier Central to Agents, introducing AI teammates that can be equipped with company knowledge and integrated across 7,000+ apps. If you're already familiar with Zapier’s automation tools, this could be a natural next step.

7. Relevance AI

Relevance AI helps users build an "AI workforce" without coding. It offers semi-ready templates and customization tools for creating AI agents that automate tasks, analyze data, and streamline workflows.

Final Thoughts

No-code and low-code AI platforms make adoption more accessible, but implementation should be strategic. By evaluating your existing tech stack, identifying key workflows, human workforce impact, and weighing costs against benefits, you can ensure that AI agents enhance your operations - without introducing unnecessary complexity.

CASE STUDY

How Yum! Brands is Using AI to Personalize Marketing and Drive Sales

Yum! Brands, the parent company of Taco Bell, KFC, and Pizza Hut, is leveraging AI to enhance its marketing effectiveness, leading to increased consumer engagement and sales. By deploying AI-driven personalization, the company has made strides in optimizing its customer interactions and improving the return on its marketing investments.

The Challenge

With a vast global customer base, Yum! Brands needed a more precise way to engage its consumers. Traditional marketing methods often relied on broad segmentation rather than personalized messaging, which meant that some campaigns failed to fully resonate with individual customers. The company sought a scalable AI-driven solution to improve personalization and marketing efficiency.

The AI-Driven Solution

Yum! Brands has launched 40 AI-driven marketing projects across its three largest U.S. brands, optimizing digital campaigns through data-driven personalization. AI analyzes purchase history, time of day, and channel preferences to tailor messaging for upselling, retention, and customer re-engagement.

For example, Pizza Hut segments customers based on ordering behaviors - frequent buyers receive menu recommendations, while occasional buyers get event-specific promotions. Instead of generic offers, AI delivers better-timed, personalized interactions, making marketing more relevant and engaging. By shifting from a one-size-fits-all approach, Yum! Brands enhances customer experience and long-term loyalty.

New AI-Driven Opportunities on the Horizon

Yum! Brands has emphasized that its AI initiatives are designed to be scalable across multiple brands and regions. The AI systems in use allow for rapid learning and adaptation, meaning campaigns can be optimized dynamically without relying solely on traditional A/B testing.

At Taco Bell, for example, AI-powered voice ordering technology is being expanded to hundreds of U.S. drive-thru locations. This technology is designed to streamline ordering processes and improve customer experience, showcasing another dimension of how AI is transforming Yum! Brands’ customer interactions.

Real-Time Gains

Compared to traditional campaigns, AI-driven personalization has led to double-digit increases in customer engagement and conversions. The ability to optimize marketing in real-time - rather than waiting weeks for test results - has allowed Yum! Brands to capture more impulse purchases and increase overall basket sizes.

Future Directions

Encouraged by its initial successes, Yum! Brands plans to further expand its AI capabilities. This includes refining AI-driven messaging across digital marketing channels and integrating AI-powered customer interaction points, such as its drive-thru technology at Taco Bell.

The company is committed to scaling these AI initiatives across its portfolio, ensuring that automation and personalization continue to drive better consumer experiences and business performance.

Click below to learn more:

ICYMI

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