What can I outsource using AI assistants?
We live in an age of abundant automation. Tasks that once required hours of human effort can now be accomplished in seconds using artificial intelligence. As AI capabilities advance, even more activities are becoming candidates for automation.
This opens up exciting possibilities for outsourcing work to intelligent assistants. But it also raises questions – what exactly can AI do, and when does it make sense to delegate responsibilities to a digital helper?
In this post, we’ll explore the expanding potential of AI outsourcing. We’ll look at the capabilities of today’s assistants, as well as their limitations. And we’ll consider when AI delegation can boost productivity, versus when a human touch remains essential.
The promise and limits of current AI
AI has made astonishing progress in recent years. Systems can now hold conversations, generate articles, translate languages, and even create art, music, and games.
But while AI is advancing rapidly, current systems still have major limitations:
- Narrow focus: Most AI today is narrow or specialized – great at specific tasks, but unable to generalize. For example, GPT-3 excels at human-like text, but cannot cleanly summarize a scientific paper.
- Brittle understanding: AI often relies on pattern matching, not genuine comprehension. So performance drops sharply with even small variations from the training data.
- Lack of common sense: AI systems completely lack the basic human understanding of the world that allows us to adapt and reason through novel situations.
- Inability to learn: Unlike humans, today’s AI cannot step back, critique its own work, and improve. Without a feedback loop for self-correction, the systems’ capabilities remain confined.
For the foreseeable future, these limits mean AI requires careful oversight by humans. But within constrained domains, AI systems are already capable of delivering meaningful productivity gains.
When to outsource to AI – and when to stay human
Given its strengths and weaknesses, here are some guidelines on when delegating tasks to AI makes sense:
Good candidates for AI outsourcing
- Data processing and analysis – Systems like IBM Watson can rapidly gather, clean, and analyze large datasets. This saves hours of human effort.
- Content creation – AI can generate draft blog posts, social media captions, emails, landing pages, etc. This can save time compared to writing content from scratch.
- Data entry – AI can help populate databases, spreadsheets, forms, etc by analyzing data and patterns. This automates repetitive data tasks.
- Customer service – Chatbots powered by generative AI can handle common customer queries, route issues to the right agents, and escalate complex conversations.
- Research – AI tools can dig through sources to compile research reports, summarize long documents, and find relevant statistics or insights.
- Design – AI image generation systems like DALL-E can create logos, posters, ads, app icons, etc based on text prompts. This provides endless design options.
- Translation – AI-powered services can translate materials like websites, documents, transcripts into different languages. This expands reach.
- Transcribing audio – Tools like Trint quickly transcribe recordings with greater accuracy than possible manually.
- Scheduling and calendar management – AI systems like X.ai’s Amy excel at coordinating schedules, booking meetings, and adjusting plans based on conflicts and priorities.
Areas where human judgment remains essential
- Strategy and planning – Only humans possess the advanced reasoning skills needed for high-level strategic thinking and decision-making.
- Writing original research – AI can synthesize existing information but cannot yet conduct primary research or advance original theories.
- Engineering and design – Systems lack the flexibility and contextual understanding needed for complex engineering and design projects.
- Relationship building – Human interaction and empathy are still required to establish trust and nurture partnerships.
- Evaluating AI output – Humans must supervise AI systems to correct mistakes and prevent potential harm from bias.
- Making judgment calls – With limited common sense, AI cannot weigh complex situational factors to make nuanced decisions.
By considering where AI excels versus areas where human oversight remains critical, we can strategically outsource the right tasks to amplify productivity.
Maximizing the power of AI outsourcing
To take full advantage of delegating work to AI assistants:
- Start small – Outsource in a limited domain where AI is proven, then expand scope based on performance.
- Set clear guidelines – Provide enough structure and examples for the system to work within its capabilities.
- Maintain human oversight – Review output and correct errors to improve system training over time.
- Allow feedback loops – Let the AI learn from human corrections to continuously enhance its work.
- Watch for bias – Since AI systems inherit biases from data, check for potentially unfair impacts.
- Don’t over-automate – Reserve tasks requiring empathy, creativity, and complex reasoning for human employees.
- Focus humans on higher-value work – Automate lower-level work so staff can focus on strategy and relationships.
With the right approach, AI delegation can significantly drive productivity by letting people concentrate on work only humans can do. But maintaining human oversight ensures AI complements staff capacity rather than replacing jobs.
AI assistant use cases
AI systems are gaining capabilities across a diverse array of use cases. Here are some examples of how businesses are productively applying AI outsourcing today:
Journalism
- Automated reporting – AI can generate draft articles from data sets and transcripts faster than human journalists. Writers then refine and enhance the copy.
- Personalized news – Systems like Persado analyze reader preferences to customize pitches and article recommendations for each subscriber.
- Fact-checking – Tools that can rapidly compare claims against multiple sources to automate part of the verification process.
Customer service
- Basic query handling – Chatbots address common questions and routing, freeing staff to resolve more complex issues.
- Sentiment analysis – AI evaluates customer tone across channels to detect rising dissatisfaction and priorities for improvement.
- Predictive intelligence – By processing past conversations and outcomes, systems can recommend the best responses and solutions to agents.
Finance
- Trade execution – Algorithms execute financial transactions in microsecond timescales impossible for humans.
- Contract review – AI speeds the laborious process of assessing and approving contracts and loan applications.
- Fraud detection – Machine learning models identify patterns and anomalies to detect potential financial crimes.
Human resources
- Resume screening – AI can rapidly filter applicant pools for the most relevant candidates based on job criteria.
- Interview scheduling – Virtual assistants independently handle the cumbersome back-and-forth of coordinating interviews.
- Candidate matching – Algorithms assess candidate skills against open positions to surface the best fits.
Legal
- Document review – AI drastically reduces the time needed to sort and validate relevant documents for case research.
- Contract generation – Systems can create customized contracts by analyzing precedents and prompting lawyers for key variable inputs.
- Predictive forecasting – Algorithms analyze past rulings and case law to forecast potential outcomes of legal proceedings.
Marketing
- SEO optimization – AI tools can analyze web content and suggest improvements for better search engine rankings.
- Ad creation – AI copywriters can generate targeted ad language tailored to products, key demographics, etc. Humans refine and approve.
- Sentiment monitoring – Systems can track brand mentions and analyze consumer sentiment from social media and reviews.
IT/Engineering
- Programming – AI code generation tools can write code for simple apps/programs based on prompts. This speeds up coding for basic projects.
- Data mining – AI systems can search through large datasets to identify patterns, anomalies, and insights difficult for humans to find.
- Forecasting – AI can make data-based predictions about future business or economic trends to support planning.
Across sectors, these examples demonstrate the growing scope of how AI delegation can augment human capabilities and accelerate work. But thoughtfully understanding the strengths and limits of AI remains essential to productively applying it.
Key takeaways on outsourcing to AI
Automating work through AI assistants holds tremendous promise. But the realization of that potential requires maintaining realistic expectations of current systems’ abilities.
Key lessons to keep in mind:
- Focus AI on repetitive and data-heavy tasks – This is where machines excel while avoiding creative and strategic work best suited to humans.
- Start small, then expand scope – Target a limited domain to first prove value before increasing AI responsibility.
- Maintain human oversight – Review and correct system output to improve accuracy over time.
- Allow AI feedback loops – Let systems learn from mistakes by incorporating human corrections.
- Watch for bias – Scrutinize results for potentially unfair impacts from tainted training data.
- Keep humans in the loop – Even as AI capabilities grow, people should remain integral to processes for empathy, ethics, and complex reasoning.
- Elevate human work – Automate lower-level work so staff can focus on relationship building, strategy, and critical thinking where humans shine.
Used thoughtfully, AI delegation creates the capacity for more meaningful and creative work. But striking the right balance between human and artificial intelligence remains critical for maximizing productivity gains.
By clearly understanding the capabilities and limits of AI, we can selectively outsource the right responsibilities. This amplifies human abilities while maintaining the human touch where it matters most.
