• AI is strongest for repetitive, structured, high-volume work where errors are easy to catch.
• Human teams are still needed for judgment, accountability, creativity, trust, customer nuance, leadership, and operational ownership.
• The practical choice is not AI or hiring. It is automate, augment, hire locally, or build an AI-enabled offshore team.
• Automating a broken process usually scales the mess. Clear roles, clean data, governance, and escalation paths come first.
• Offshore teams become more valuable when AI removes repetitive drag and people still own execution.

Your CEO does not want another headcount request. They want to know why AI cannot handle the work instead.

That is the real pressure behind the AI vs hiring question. Leaders like you are being asked to deliver more without adding people.

And you can’t really put all the blame on executives. AI tools look cheaper (at first glance), faster, and easier to justify than another full-time hire. That’s how AI companies sell themselves as well, the promised land they want us all to dream about.

But many operators are already seeing the limitations: yes, AI can help with tasks, but it cannot own outcomes and it cannot produce truly creative and paradigm-shifting work. 

Of course, AI is far from useless. But, it’s not magic labor, either.

The real question is not whether you should use AI or hire people. The better question is: what should AI handle, and what still needs a human owner?

The Real Question Is Not “AI or Hiring?”

The Better Question Is “What Type of Work Are We Solving For?”

“AI vs hiring” is actually too broad, lacking nuance. Because the answer is never binary, not either/or.

A single role often contains several types of work. Some tasks can be automated. Some can be accelerated by AI. But most still require a person who can make decisions, handle exceptions, and be accountable for the result.

That is where many AI debates fall short. They compare AI with a job title (making people worry that AI is going after all our jobs) instead of comparing AI with the actual work inside the job.

Take customer support. The role may include repetitive password questions, order status checks, complaint handling, escalation judgment, customer reassurance, and process feedback. AI may handle the first two well. It may assist with the next two. But it cannot own the whole function.

The same conclusion shows up in a recent McKinsey research: generative AI and other technologies could that absorb 60 to 70 percent of employees’ time today, but that does not mean 60 to 70 percent of roles can safely disappear. 

It’s clear that the unit of analysis is the work activity, not the person.

So before asking, “Can AI replace this person?”, ask this:

Which parts of this work are repetitive tasks, and which parts require ownership?

Automate Tasks That Are Repetitive, Structured, and Low-Risk

Good Candidates for Automation

AI is useful when the work is clear, repetitive, and easy to verify. It can move quickly through information, organize messy inputs, and produce first-pass outputs that save time.

Based on my experience (this is not an exhaustive list; just based on my personal day-to-day), AI helps with:

• Initial research
• Brainstorming and ideation
• Workflow creation
• Information gathering
• Meeting note organization
• Transcript analysis
• Knowledge base creation
• Extracting patterns from internal discussions
• Drafting summaries
• Repetitive, low-judgment tasks
• Large-volume data analysis

These are the areas where AI shines. It removes the slow, tedious work that drains human operators. It helps a team get from blank page to first draft, from raw notes to structured insight, and from scattered documents to usable knowledge.

The Decision Rule

Automate when the work is:

• High-frequency
• Rules-based
• Easy to check
• Low-risk if corrected
• Built on clean data
• Not dependent on trust or judgment
• Not central to a customer relationship

That last point is important. AI can help with customer-facing work, but it should not automatically own work that affects trust, retention, or sensitive decisions.

, 91̽��’ CEO, compares AI fluency to Excel fluency: “If you hire an accountant nowadays who doesn’t know how to use Excel well, it’s useless. In the future, or already, if you hire anybody who doesn’t know the basics of AI, it becomes an impediment.”

That is the right way to look at things. AI is becoming a baseline operating skill. It should make capable people faster and sharper; not as a replacement for those capable people.

Do Not Automate Work That Requires Ownership

AI Can Execute Prompts, but It Cannot Own Outcomes

AI can produce output, really good output. 

But it cannot be accountable for whether that output was right, useful, ethical, timely, or commercially sound.

If an AI tool gives a customer the wrong answer, misses a compliance issue, generates inaccurate analysis, or escalates the wrong case too late, someone still has to answer for it. That someone is not the tool.

This is why frameworks like the exist. They help organizations incorporate trustworthiness into the design, development, use, and evaluation of AI systems.

In other words, AI needs governance. It needs owners. It needs escalation rules. It needs people who know when the answer is technically plausible but operationally wrong.

Operators on Reddit describe the same concern. AI is fast, but , especially in client work and high-consequence environments.

The Work That Still Needs Humans

Human-led work includes:

• High-level strategic thinking
• Creative direction
• Leadership
• Managing people
• Gut-based decision-making (last time I checked, AI doesn’t have a gut)
• Customer escalation
• Complex sales conversations
• Talent evaluation
• Cross-functional coordination
• Process improvement
• Unique storytelling

This is where trust is built or lost.

AI can summarize a customer complaint, but a human decides whether that customer needs a refund, a workaround, an apology, a technical escalation, or a hard boundary.

AI can draft a hiring scorecard, but a human decides whether a candidate can actually work inside the company’s culture, communication rhythm, and expectations.

AI can assist judgment, but cannot replace human judgment.

The Human Premium Is Rising

When Average Output Becomes Easy, Original Human Work Becomes More Valuable

AI has made average output easier to produce. That makes transformative, groundbreaking human work more valuable.

The internet is already flooded with AI slop. Most readers can spot it quickly: the predictable structure, the recycled phrasing, the tidy but empty advice, the absence of lived experience. In marketing, writing, design, strategy, leadership, and client-facing work, that creates a trust problem.

This is where human work has the most advantage. People bring taste, context, restraint, lived experience, and original judgment. 

Taste is the biggest one for me. Taste is something AI can never replicate, not ever. I truly believe that.

And as AI makes average output easier to produce, the premium on real human judgment and taste rises.

So, the value shifts from “Can we produce more?” to “Can we produce something worth trusting?”

The Same Pattern Applies Beyond Content

This is not only a writing issue. It applies to coding, design, video, and even music.

AI can produce fast drafts, code, images, and summaries. But speed often introduces bloat, generic choices, or hidden problems. The best human operators bring taste, structure, simplicity, and context.

Software development shows this clearly. Vibe coding can build quickly. But there is still a place for developers who write simple, organized, maintainable code that does not create future headaches for everyone else.

Humans can elevate a discipline to craftsmanship and craftsmanship to art.

The same applies to customer support, finance operations, marketing, recruiting, and back-office work. The more AI produces, the more valuable humans become when they can filter, judge, improve, simplify, and own the result.

Use This Framework: Automate, Augment, Hire, or Offshore

The Four-Bucket Decision Model

The important move is to separate tasks from roles. Do not automate a role just because part of it is repetitive. Do not hire a full-time person just because one task is painful. Diagnose the work first.

A Simple Evaluation Table

Use this table before buying another AI tool or opening another role.

Where Human Teams Still Win

Operational Continuity for AI-Driven Companies

Spot Ship, one of our clients, is a great example here because it’s an AI-powered tool for ship brokers. And even an AI-driven company still needed human operators to keep work moving accurately and professionally.

Henry Waterfield, Founder and COO of Spot Ship, said: “We wanted to increase our productivity levels without compromising professionalism. We found exactly that with 91̽��. The quality of their endorsements and quick turnaround time for hiring are impressive!”

Spot Ship saved an average of 89% of the cost per role while increasing productivity, and a remote team member was promoted within a year due to his impact.

So yes, AI companies still need people. AI systems rely on human operational continuity, clean data, exception handling, judgment, and professionalism.

Why Offshore Teams Fit This Model

Offshore teams reduce local hiring pressure. AI reduces repetitive drag. Together, they give companies more capacity without pushing all work into automation.

The key is not low-cost labor. The key is structured ownership at a sustainable cost.

AI-enabled offshore teams do not let you outsource ownership. They let you scale it, by pairing capable operators with sharper tools and a structure built to absorb pressure rather than crack under it.

How 91̽�� Makes AI-Enabled Remote Teams Work

Hiring the Right People, Not Just Filling Seats

AI-enabled teams still start with the right people. The hiring system is what makes the model work.

Before sourcing, the role needs to be clear. The company needs to separate tasks from outcomes. Screening should test skills, communication, and ownership, not just availability. AI can assist parts of that process, but final judgment should stay human.

This is why “warm body” hiring fails. If the problem is unclear, another person only inherits the confusion. If the role is clear, the right person can use AI to produce more without losing accountability.

For readers who want to understand how a remote team gets built, hired, onboarded, and supported, the 91̽�� process from role definition through ongoing support lays it out step by step.

Hypercare Turns Hiring Into Integration

A human team only works if onboarding, feedback, reporting lines, expectations, and accountability are clear.

AI does not solve poor onboarding. Offshore teams need context, communication rhythm, role clarity, and early feedback. Without that, the company risks blaming the person when the real issue is the system around the person.

That process connects directly to the AI vs hiring question. If you decide the work needs humans, the next challenge is making those humans successful. The Hypercare Framework is built around that integration period.

Where to Start If You Are Unsure

Do not begin with a tool or a hire. The best way is to begin with a work audit.

Use this sequence:

1. List the work your team does every week.
2. Separate repetitive tasks from judgment-heavy responsibilities.
3. Identify which work is high-risk, customer-facing, or hard to verify.
4. Automate low-risk repetition first.
5. Assign humans to work that needs judgment, communication, and accountability.
6. Consider offshore teams when you need ongoing ownership at a sustainable cost.
7. Add AI tools to help those teams move faster without removing human oversight.

If cost is part of the decision, you can benchmark common remote roles in the Philippines salary guide.

Build the Team Around the Work

Some work should be automated. Some work should be AI-assisted. Some work still needs local leadership. Some work is ideal for an offshore team that uses AI while still owning execution.If you are deciding what to automate, what to keep human-led, and where offshore talent fits, 91̽�� can help you map the work before you add headcount or buy another tool.

The post AI vs. Human Teams: What to Automate and What to Hire For appeared first on 91̽��.

This article explains what Atlas is, how it works, and what it means for U.S. leaders hiring offshore talent and remote professionals building careers in AI-enabled teams.

What you will get here: practical definitions, honest limitations, a pilot plan you can use if this applies to your work, a role-by-role view of what changes and what stays the same, and a 30-day upskilling plan for professionals who need to stay ahead of displacement.

The position I’m taking: Atlas is powerful in theory. In practice, for someone who already uses LLMs heavily, it adds friction instead of removing it. . The enterprise readiness is not there yet. This is technology you should understand and pilot carefully, but it is not the transformation moment some are claiming it to be. Yet.

Key Takeaways

• An AI-Native Browser, Not Just a Chatbot: ChatGPT Atlas is a new, AI-powered browser from OpenAI that integrates a context-aware sidebar (which can read the page you are on), browser “Memories” for recall, and an “Agent Mode” capable of performing multi-step actions on the web on your behalf.
• Significant Enterprise Readiness and Security Concerns: Despite its potential, Atlas is in early access and is not yet enterprise-ready. It currently lacks critical governance features like SOC 2 or ISO attestations, SIEM/eDiscovery feeds, and advanced access controls, and it presents real security risks like indirect prompt injection.
• The Strategic Shift is to “Leverage Arbitrage”: Atlas and similar tools are changing the offshore talent equation. The focus is shifting from “labor arbitrage” (who is cheapest) to “leverage arbitrage” (who is best at using AI tools to multiply their output and speed), making AI fluency a critical skill.
• A Tool to Pilot Carefully, Not to Fully Deploy: The article’s core position is one of cautious skepticism. While the technology is powerful in theory, for many experienced AI users (“power users”), it currently adds friction instead of removing it. The recommendation is to pilot the tool carefully with non-sensitive data, not to implement it for production-level work.

What ChatGPT Atlas Is, and How It Works

Atlas is an AI-native browser currently available to consumers, with early-access pathways for Business and Enterprise workspaces. It is available for macOS today. Other platforms are on the roadmap. Agent mode, the feature that lets the AI take over and actually do things, is gated to paid ChatGPT plans. For availability, access, and pilot instructions, see OpenAI’s product guidance: .

There are three pillars.

ChatGPT Sidebar, context-aware on any page. You can ask for a summary of a long vendor page, rewrite copy in a form field, or generate a QA checklist without leaving the tab. The sidebar sees what you see. It reads the page, and you don’t have to copy-paste anymore. This is useful if you are not already using ChatGPT, Claude, or Gemini in a dedicated workflow. If you are, the sidebar is redundant.

Browser Memories, optional personalization and recall. You opt in. Once you do, Atlas remembers recent research sessions. It can pick up where you left off on a client RFP three days ago. The memory is persistent, which creates value for some users and privacy concerns for others.

Agent Mode, multi-step actions. This is the feature that gets the attention. You tell the agent to research three suppliers, fill a comparison sheet, and draft a summary. You watch the steps unfold in the browser as it clicks, types, and compiles. In demonstrations, this looks seamless. In daily use, it is slower than doing the work yourself if you already know what you are doing. The friction of waiting for an agent to navigate pages, click buttons, and compile results is higher than the friction of just doing it manually when you have an optimized workflow.

Data Privacy Defaults and User Controls

Atlas follows a privacy-by-default stance, which means your browsing content is not used for model training unless you opt in. Individuals can clear web data, manage Memories, and toggle model training on or off. Business and Enterprise users retain user-level privacy controls, but you should confirm workspace-level coverage in the OpenAI article above.

Atlas also includes:

• Memory management: view, archive, or delete memories in Settings. Clearing browsing history removes associated memories.
• Site-specific visibility: a simple address-bar toggle prevents ChatGPT from accessing content on sensitive pages. Use this for online banking, for example.
• Incognito behavior: functions as a temporary logout from the ChatGPT account, so activity is not saved to history or used to create memories.

These controls exist, but the fundamental question remains: what happens to your data when an AI is watching and acting on your behalf? What does it do with the websites and pages it sees while browsing on your behalf? What if you are entering credit card information? What if you are browsing a sensitive page, or inside your bank account?

OpenAI says these features can be turned off and memory can be erased. But there are doubts. There should be doubts.

Security Posture and Enterprise Readiness, What Leaders Must Know

The primary risk is indirect prompt injection. Malicious instructions hidden on a webpage can cause an agent to misbehave when it reads the page. An example: the agent might attempt to pull data from another tab without your knowledge. OpenAI mitigates this with hardcoded limits. Today, the agent cannot execute local code, download files, install extensions, or access your local file system. It is designed to disengage on sensitive sites.

But security vulnerabilities in agentic browsers are not theoretical. Perplexity’s Comet browser had a vulnerability that allowed malicious actors to hide instructions in web content. This could be used to extract data from emails or calendars, download malicious files, even attempt to purchase things on your behalf. That is the risk profile.

Here is the reality of early access. OpenAI states that Atlas for Business and Enterprise is early access. Atlas is not currently in scope for SOC 2 or ISO attestations. It has no SIEM or eDiscovery feeds. It lacks Atlas-specific RBAC, SSO enforcement, or audit exports. This means you treat regulated, confidential, or production data as out of scope for now.

There is also the shadow IT risk. Because Atlas is available to consumers, an offshore team member can use a personal paid account to access full Agent Mode on client systems. No visibility. No logging. No governance. This is why you establish a clear policy for agentic browsers now, even if you are not piloting. Set expectations on what is allowed, what is not, and how to report incidents.

From Labor Arbitrage to Leverage Arbitrage

Traditional offshoring leaned on wage differentials. You hired someone in Manila because they cost less than someone in San Francisco, and the work got done at a lower price point. That was labor arbitrage, and it worked for a long time.

Atlas and similar AI browsers change the equation to leverage arbitrage. The winning teams will be those that extract the most output, accuracy, and speed from AI. Not the lowest hourly rate. The question is no longer who is cheapest. The question is who can make the AI work hardest.

A simple framework:

• Automation. Offload repeatable, rules-based tasks the agent can safely execute.
• Augmentation. Pair talent with Atlas to accelerate research, drafting, analysis, and QA.
• Oversight. Elevate roles toward prompt design, exception handling, and quality review.

The shift is happening in theory. In practice, many power users who already have optimized workflows with Custom GPTs, Claude Projects, Gemini Gems, and dedicated browsers for specific types of work will find that Atlas adds steps instead of removing them. The value proposition is clearer for teams that have not yet built these systems.

Where Atlas Fits in Offshore Work, Role-Based Impacts

Below are common functions, typical tasks that move first, the expected automation level, the human oversight needed, the ROI lever to watch, and the key risk to manage. These projections assume reliable execution, which is not guaranteed in daily use.

Customer Support

• Tasks: ticket triage, knowledge base lookups, post-call summaries. These are tasks where specialized, production-ready tools are already common. Unlike the general-purpose agent in Atlas, many dedicated  are already mature in handling these specific support workflows.
• Automation level: medium for triage and summaries, low for complex cases.
• Oversight: agent prompts, escalation criteria, compliance checks.
• ROI lever: average handle time, first-contact resolution, QA score.
• Key risk: enforcing data boundaries and redaction in transcripts.

Finance Operations

• Tasks: invoice matching, vendor research, policy lookup, report prep.
• Automation level: low to medium. The agent assists with prep work and checks.
• Oversight: segregation of duties, approval workflows, sampling for accuracy.
• ROI lever: cycle time, errors found pre-close, time to variance explain.
• Key risk: never expose PII or financial systems to agent mode. Keep Atlas out of production ledgers.

Marketing Operations

• Tasks: brief synthesis, content drafts, UTM audits, on-page QA.
• Automation level: medium for synthesis and QA, low for final creative.
• Oversight: brand guardrails, fact-checking, campaign sign-off.
• ROI lever: time to first draft, error rate on tracking, campaign velocity.
• Key risk: hallucinated facts. Require source-attached outputs.

Sales Operations

• Tasks: account research, contact cleanup, call note summaries, competitor matrix drafts.
• Automation level: medium for research and notes, low for judgment work.
• Oversight: CRM governance, PII hygiene, opportunity review.
• ROI lever: prep time cut, meeting quality, pipeline throughput.
• Key risk: do not allow agent actions in live CRMs.

Data and Research

• Tasks: literature scans, data dictionary lookups, first-pass EDA notes.
• Automation level: medium for collection and summarization, low for analysis.
• Oversight: sampling, reproducibility, citation audits.
• ROI lever: time to insight, documented sources, error catch rate.
• Key risk: prompt-injection exposure on unknown sites.

Engineering Support

• Tasks: ticket grooming, doc generation, release note drafting, dependency license checks.
• Automation level: medium for drafts, low for code or config changes.
• Oversight: code review norms, SBOM governance, security sign-off.
• ROI lever: time saved on non-coding work, doc completeness.
• Key risk: never grant agent access to prod systems.

30-Day Upskilling Plan for Remote Professionals

The goal is simple: move from individual contributor to AI agent manager. That is the role now, whether or not Atlas becomes part of your daily workflow.

Week 1: Sidebar Mastery and Habits

Replace copy-paste with sidebar prompts on real tasks. Build a daily prompt log with input, steps, and outcomes. This is muscle memory work. The sidebar has to become automatic, even if you ultimately decide it is slower than your existing workflow.

Week 2: Memory Hygiene and Safe Patterns

Enable memories only for low-risk research tasks. Practice site-specific visibility toggles on sensitive pages. You are learning where the boundaries are. You are also learning whether this feature adds value or just creates another system to manage.

Week 3: Agent Chains and QA Workflows

Design 2 to 3 multi-step agent flows for your role. Add a QA checklist that includes sources and a human sign-off. You are building systems now, not just completing tasks. You are also testing whether watching an agent work is faster than doing the work yourself.

Week 4: Portfolio Proof and Client-Facing ROI

Package three before-and-after examples with time saved and quality gains. Present a simple ROI sheet to your manager or client. You are making the case that you are worth keeping because you know how to evaluate AI tools critically, not just adopt them blindly.

Governance Guardrails for Pilots

Set written rules of engagement before any pilot. Keep scope small, data non-sensitive, and success criteria clear. Minimum controls:

• Access: enable Atlas only for a pilot group. Do not use with regulated, confidential, or production data.
• Identity and permissions: no Atlas-specific SSO or RBAC today. Use device management to control distribution. See OpenAI’s guidance on managed preferences in the article above.
• Agent controls: allow agent mode on low-risk workflows only. Disable when browsing sensitive accounts.
• Data and logs: since there are no Atlas audit exports yet, require prompt logs and manual notes. Prohibit storing secrets in prompts.
• Security: train users to recognize prompt-injection patterns. Maintain an allowlist of approved sites for agent actions.

These are not suggestions. These are minimum requirements for a controlled test. You skip one, you expose yourself.

Proceed with Caution

Atlas is safe to try in a controlled pilot. It is not ready for full enterprise deployment. It is also not a replacement for the workflows you have already built if those workflows are working.

For power users who already run Custom GPTs, Claude Projects, Gemini Gems, and dedicated browsers for specific types of work, Atlas may add more friction than value. The agent mode is remarkable in demonstrations. In daily use, it is often slower and less reliable than doing the work yourself.

For teams that have not yet optimized their AI workflows, Atlas may offer a faster path to automation. For offshore teams managing high-volume, low-complexity tasks, the agent mode has potential. But the privacy concerns are real. The security vulnerabilities are documented, and the enterprise controls are not there yet.

It is early days. The technology will improve. The question is whether it will improve fast enough to justify the added complexity, and whether the value it provides will exceed the value you can already extract from the tools you are using.

Proceed with caution. Measure results. Formalize governance. Do not assume this is the future just because it is new.

Need help designing a safe pilot or building an AI-enabled offshore team? 91̽�� is an AI-first company at the forefront of these technologies. Talk to us.

Frequently Asked Questions

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Key Takeaways

• AI Augments Human Agents, It Does Not Replace Them: The primary role of Artificial Intelligence in customer service is to augment and enhance the capabilities of human agents. AI automates routine tasks, which frees up human professionals to focus on more complex, empathetic, and high-value customer interactions.
• Leads to Significant and Measurable Performance Improvements: The integration of AI has resulted in dramatic and quantifiable improvements in key customer service metrics. These include a 71% increase in agent efficiency, a reduction in average live chat wait times to just 23 seconds, and a 57% increase in overall customer happiness.
• Creates a Demand for New, AI-Capable Human Skills: To effectively leverage these new technologies, companies must hire and train agents with a new set of skills. Beyond traditional customer service abilities, businesses now need to screen for capabilities in data analytics, data security, prompt engineering, and conversation design.
• Offshoring is a Strategic Way to Access AI-Skilled Talent: As the demand for AI-capable agents grows, offshoring to a tech-savvy talent hub like the Philippines offers a reliable and cost-effective solution. It allows companies to access a large pool of professionals who are adapting to an AI-reliant future, all while achieving significant cost savings.

What Is AI for Customer Service?

Artificial intelligence has advanced quickly in recent years, and the customer service space has received its share of AI-enhanced tools. However, opinions across the industry have been unanimous: AI is not ready to take over the responsibilities of human agents.  from Forbes says that experts agree customer service positions will only be augmented and automated but not replaced. In addition, see AI amplifying human intelligence and not replacing it.

Related:

• AI vs. Human Teams: What to Automate and What to Hire For
• Hire a Customer Experience Specialist Who Create Lasting Loyalty

How AI Can Power Your Customer Service

1. 71% More Agent Efficiency and Productivity

In focus, AI or automation frees human agents to by as much as 71%. A person can focus on more important tasks while leaving basic responsibilities to AI. Moreover, contact center artificial intelligence can assist human agents through insightful support. Tools can implement sentiment or intent analysis to evaluate customer interactions and accordingly de-escalate or improve situations. Bernard Marr, a , said that “AI-powered tools can analyze customer interactions, extract valuable insights, and assist agents in real-time. The technology can free up time for more human, empathetic interactions.” AI helps agents provide a human touch to interactions and empathically help customers with their concerns, leading to higher client satisfaction.

2. Wait Times of 23 Seconds

Customers value their time, and they expect companies to do the same. This expectation places importance on wait and response times. With the assistance of call center automation and AI, wait times have decreased to a record lowest point since the inception of the . The average live chat wait time was 23 seconds in 2023. Average response times also fell in 2023 to 46 seconds.

At the same time, AI-powered chatbots monitor conversations, supplying live agents with context-relevant answers. Human agents need not search for type answers anymore. With this, customers may feel that they and their time are valued.

3. Average Handling Times of 9 Minutes

Average handle times reflect how fast customers receive resolutions to their concerns. Low AHT means faster resolutions. However, AHT can also reflect the quality of solutions customers receive. With this, a balanced AHT must be achieved by contact teams.

AI has improved AHT in 2023 with a 19% drop. This has placed average handle times at 9 minutes and 36 seconds. Comm100 evaluated this trend as an improvement due to AI-assistance. Additionally, the trend reflected an assurance that quality was upheld despite the drop in AHT.

4. Increase Customer Happiness by 57%

AI tools can perform sentiment or intent analysis. These solutions parse huge volumes of data across various channels and mediums. AI can then provide you with accurate information on trends and customer preferences. You can use these insights to further optimize your customer service, resulting in higher customer satisfaction. In fact, AI call centers in the UK with remaining human teams have already reported .

Analytical tools powered by AI assist you in enhancing leadership decision-making. You can steer your company toward better shores because you have deeply insightful information on your market. You can create better products or services, while catering to customer concerns.

Augment Your Team with AI-Experienced Agents

1. Hire AI-Capable Agents

With all the ways can improve your customer service, you will need people who can steer AI toward your organizational goals and overall company success. AI telemarketing tools will only be as effective as the people who wield them. To this end, you can build up your company’s AI capability by hiring experienced or knowledgeable agents.

2. Screen for AI Skills

You must screen for AI skills in candidate applications. Skills to watch out for include: 

• Data analytics
• Data security
• Natural language processing
• Conversation design
• Prompt engineering

3. Evaluate AI Tool Capability

In addition to AI skills, applicants must also be well-versed in . These AI tools are existing devices that can already perform tasks and work. Only seamless integration will be required. Candidates must know how to use common AI tools such as:

• ChatGPT
• Custify
• Fin 

4. AI Questions to Ask

To determine the level of experience an applicant has in AI call center technology, you may ask the following questions:

1. Have you used AI in customer service?
2. What is your experience using AI in customer service?
3. What AI tools for customer service have you used?
4. How have you used AI to resolve or respond to customers?

5. Offshore AI-Skilled Agents

Now, hiring AI-experienced customer service talent is no easy feat, given current labor shortages all over the world. For this reason, you may consider offshoring human agents. Offshoring is a reliable solution to acquire capable workers for considerable cost savings. Offshoring to the Philippines also makes sense because it is the BPO capital of the world. You can offshore agents who are already AI-capable. Remote workers in the Philippines are technologically savvy, and many Filipino professionals are keeping pace with AI advancements. You can access and onboard such talent onto your team while saving on labor costs. Check out this offshoring salary calculator to see how much you can save.

Related: Hire a Customer Service Supervisor Who Builds Loyalty That Lasts

Establish AI Capability with AI-Skilled Agents

Human agents will continue to have a place in customer service even with AI in BPO centers. To become AI-capable, however, you need AI-experienced people in your team. Find AI-capable employees by offshoring and onboarding AI-skilled Philippine talent through the help of an offshoring partner. With the right people and AI skills, you may see expected boosts to customer service performance while minimizing risks. AI is here to stay for good, and your company will only benefit from AI-enhanced customer service.

Frequently Asked Questions

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