Overcoming the AI Talent Scarcity Problem Requires a New Approach for Enterprises

In today’s market, we see three major challenges standing in the way of enterprises seeking to leverage AI for a competitive edge:

1. Talent with AI expertise is essential to building and using AI within the enterprise.
2. AI talent is tough to come by. 
3. AI talent is costly. 

In fact, 67% of IT leaders named the AI skills shortage as the number one barrier to deploying AI, according to Rackspace Technology. Yet recent AI-related job postings from July are 450% YoY, highlighting that this hasn’t stopped them from trying.  

To further counter this shortage, enterprises are shifting talent within traditional technology and data roles to AI-related priorities, implementing AI point solutions to replace customer service representatives, and leveraging the flexibility of freelancers in a move that is shoring up the gig economy. But none of these have delivered the outcomes enterprises are looking for. 

Instead, the clearest path to ROI may lie in a strategic partner that merges the best of human and artificial intelligence within a single end-to-end technology solution. Let’s dive in. 

Why AI Expertise is Essential

An effective enterprise AI solution can’t simply be patched together. To understand why, let’s look at the varying degrees of AI adoption within an enterprise. 

Many models depict enterprise AI adoption across three levels, with AI integration deepening within an enterprise’s operations at each step. Egon Zehnder is one such organization.

Here’s our take, including what adoption looks like at each stage: 

Level 1: AI is introduced in an organization

• A team, or teams, onboard a point solution like an AI-powered SaaS tool.
• AI is added as a feature to a product or service.
• A core AI solution is not centralized within the organization’s operations.

Goals: Automate and increase the efficiency of repetitive low-complexity tasks, or improve the customer experience. 

Level 2: Business processes become centered in AI 

• A foundation LLM is customized with fine-tuning to support one or more business functions.
• Data science teams begin to shift focus toward maximizing the AI model’s value.
• AI becomes a core product feature.

Goals: Increase the efficiency and quality of an entire business function’s output and optimize value-creation for a key product.

Level 3: An organization is fully AI-enabled 

• An LLM or multiple LLMs working in concert deeply supports a core business process and has an impact across the entire organization.
• Humans in an organization work seamlessly in tandem with the AI and the rest of their tech stack.
• Existing business processes are redesigned to be AI-enabled; new business processes are structured around AI.

Goals: Engage on every opportunity, maximize business productivity and outcomes, and widen the gap with competitors.

Off-the-shelf AI tools tend to cater to enterprises at Level 1. But as enterprises scale to Levels 2 and 3, costs and complexity begin to mount.

That’s because, at each level of adoption, the expertise and resources required scale exponentially. Based on our experience working with the leading AI model development firms and enterprises deploying AI, here is our assessment of the organizational requirements for Levels 2 and 3:

Level 2 Requirements

At this stage, an AI model is integrated within an enterprise’s operations at a larger scope. The stakes for having an accurate and robust solution are higher, so enterprises need: 

• Skilled AI trainers to produce preference data for Supervised Fine-Tuning and perform model evaluations.
• Trained experts to monitor data quality and manage the AI trainer workforce.
• Tech infrastructure and data scientists to manage data securely and efficiently.
• Properly structured and enriched proprietary data.
• A red team to uncover hallucinations and build feedback loops that steer a model toward aligned behavior.
• Process mining and experience in shaping business processes around an AI model and human intelligence.
• Tech infrastructure that moves data cleanly between AI, people, and the organization’s tech stack.

Use case example: A healthcare firm utilizing a conversational AI tool that integrates with EMRs and is fine-tuned on preference data from domain experts. 

Level 3 Requirements

When enterprises reach this level of adoption, they are shaping business-critical processes around one or more AI models working in concert. They need: 

• All of the requirements of Level 2.
• A technology platform capable of orchestrating one or more AI models, software integrations, and humans in the loop to maximize efficiency and value.
• Research scientists with expertise in measuring.

Use case example: A financial institution deploying multiple generative AI models to improve fraud detection response accuracy and speed.

For a business restructuring its operations around AI, the requirements listed above necessitate either a monumental shift in internal resources or a recruiting effort of similar cost and magnitude. Both routes threaten ROI due to their expense and complexity. 

Why Choose a Strategic Partnership

The strongest alternative is a partner experienced in AI model training and deployment. While many claim to transform enterprises with AI, few offer an end-to-end solution — this is where Invisible steps in.

Invisible works alongside global enterprises, enabling them to leverage the full potential of AI through our process orchestration platform: a potent blend of a global team of experts, cutting-edge automation capability, and AI. 

This platform enables:

• Customizing AI models for specialized enterprise use cases.
• The orchestration of multiple models and humans in the loop to deliver powerful outcomes through large-scale business operations.

Invisible’s value proposition lies in our deep relationships with the world’s leading AI model development firms, for whom we provide human-in-the-loop data training that delivers more accurate and helpful models. 

This unique position grants our enterprise partners a head start in their AI journey with access to the latest thinking in AI and deep expertise in the most powerful models, allowing us to craft solutions that maximize model efficiency, while negating the need for additional recruitment, team shifts, or tech investments, ensuring businesses are AI-enabled and ROI-focused.

Case Study: Strategic Partnership with a Big 4 Retailer

A retail giant required a precise, high-throughput solution for enhancing product descriptions for its online marketplace. Cluttered third-party data obscured listings from search engines, leaving millions in revenue on the table. 

Invisible crafted a dual-AI solution combined with human expertise in which one AI model enriched data from scraped content, while another identified and flagged errors. Errors were then refined by human experts.

The outcome? Invisible enriched a vast product range swiftly, surpassing other solution providers. The retailer witnessed a staggering 9x ROI within a month.

Ready to harness AI's power for your enterprise? Discover how Invisible can be your strategic partner here.