What Today’s Generative AI Can and Can’t Do For Your Business
Generative AI is seemingly everywhere today, transforming industries and processes. But how much of your business’s "thinking" should generative AI be handling? Can it go beyond language-based tasks to manage complex decision-making and strategic planning?
Recent research in neuroscience, highlighted in a fascinating podcast from the Santa Fe Institute, offers an intriguing perspective: language and thought are not the same in our brains, and this insight could apply to AI as well. The current large language models (LLMs) like ChatGPT are incredibly powerful at handling text — but do they think?
What Current Language Models Do Best
The current LLMs are designed to process vast amounts of text, learning linguistic patterns to generate responses, answer questions, and make connections between words.
They shine when it comes to language-focused tasks.
Generating content: Need a blog post draft, social media captions, or product descriptions? LLMs can rapidly produce text from simple prompts, saving you time.
Answering common questions: Businesses often use LLMs in chatbots to manage customer inquiries, providing instant responses to frequently asked questions.
Summarizing data: LLMs can digest lengthy documents into concise summaries, letting you get key points without the full read.
Translating text and analyzing sentiment: LLMs can translate text across languages or detect tone, helping businesses understand customer sentiment globally.
These are language-based tasks where AI excels, primarily because they involve recognizing and generating patterns — not understanding at a deep level.
Where AI Falls Short: Cognitive-Heavy Tasks
While the current LLMs are impressive, they aren't replacements, at least not yet, for human brains in complex thinking tasks that require deeper understanding.
Complex decision-making: While current LLMs can provide plausible responses, they lack true understanding and can easily go astray, forming nonsensical conclusions. Therefore, for critical business decisions, human insight remains indispensable.
Strategic planning and abstract reasoning: Humans adapt and think abstractly based on experience; the current LLMs do not. They excel at brainstorming but can’t create strategy independently.
Understanding cause and effect: The current LLMs aren't designed for understanding causal relationships. For example, they might generate a logical-sounding explanation without truly understanding the underlying causes.
A Supporting Role, Not a Replacement
The current LLMs shine in a supporting role, helping automate simple language-based tasks.
Humans are then freed up for more complex work.
Customer service: Chatbots can handle common queries, but real people are needed for nuanced or emotional situations. Human oversight can reduce bias that might have been introduced from the LLM’s training data.
Content drafting: AI drafts, while helpful, benefit from human editing for accuracy and context. This review can also ensure that the tone, sentiment, and perspective truly reflect the intended message.
Brainstorming and summarizing: AI can assist in ideation or summarizing, and with newer models like those incorporating reinforcement learning or agentic behaviors, AI can even suggest actionable next steps. However, human oversight is still essential to ensure the quality and feasibility of these actions.
Play to AI’s Strengths in Language, Not Thought
By focusing on their strengths, businesses can use generative AI effectively while staying aware of its current strengths and weaknesses. AI can enhance efficiency, freeing up time while keeping human judgment at the forefront for nuanced and critical decisions.
AI is a powerful tool, best used to complement human thinking rather than replace it. Using AI for language tasks while retaining human control over complex thought ensures the best of both worlds: enhanced productivity alongside deep insight.
So, how will you use AI to support — not substitute — the unique thinking abilities of your team?
Here are some actionable steps to help you get started.
Identify suitable tasks: Determine which repetitive language-based tasks can be automated with AI, such as responding to common inquiries or drafting initial versions of documents. Schedule a call with us, tell us what you do, and we will prepare some ideas.
Experiment with limited integration: Start using AI in a limited capacity, monitor the outcomes, and refine its use based on your observations.
Involve human oversight: Ensure that AI outputs are always reviewed by human team members, particularly when tasks involve nuanced decision-making or customer interactions.
Encourage collaboration: Use AI as a brainstorming partner to generate ideas, which can then be refined and brought to fruition by your team.
Provide training: Educate your team on how best to work alongside AI tools to enhance their efficiency and effectiveness.
Cake Studio AI is here to help you explore how AI can support your business. We offer guidance on how to integrate AI into your processes effectively, ensuring your team stays productive and in control. Reach out to learn more about how we can support you in implementing these steps.
