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How AI Transforms Talent Management

Powering critical processes across business functions, AI is now transforming how organizations acquire, develop, and manage their most valuable asset – people.
AI-assisted talent management solutions are already helping companies make better hiring decisions that deliver measurable business performance lift. Done right, AI-powered processes can ensure bias-free recruiting and promotion practices. And facing a tight hiring market, AI-driven talent acquisition strategies offer new ways for companies to overcome the ever-shrinking pools of skilled job candidates.

Better Hiring Decisions

AI-powered success models are the most accurate predictors of new-hire job performance. Tailored to your organization and job success factors, these predictive models are up to 4x more reliable than traditional screening methods.

Organizations that use Cangrade’s AI-assisted hiring assessments are seeing results that include:

Higher Sales ● Elevated customer service scores ● Better safety records ● Lower turnover

AI Success Models

Cangrade’s AI engine analyzes current employee personality attributes and performance data to determine what drives success for each role in the organization.
This process generates a unique Success Model for each role and organization – which then powers Cangrade's Pre-hire Assessments and Talent Management Tools.

Bias-Free Guarantee

Despite organizations' best efforts, most hiring and promotion decisions are still burdened with bias. While data-driven AI models may also be biased, they can be more rigorously controlled through proven scientific methods.

Cangrade’s Bias-Free Guarantee means that every single item, scale, and model we provide has no statistically significant adverse impact on any legally protected demographic group. Going the extra mile to ensure equal opportunities for all, we are continually adding demographic identifiers to our big data sets to extend our bias-free guarantee to additional groups.

Overcoming the Skills Shortage

Hiring managers in almost every field today are facing an acute shortage of experienced and skilled workers, a shortage that is only expected to grow over time. The only way to fill the gap between supply and demand is by expanding recruiting pools with high-potential candidates that can be trained for the job.

AI success models are designed to pinpoint candidates with a combination of personality traits and soft-skills that indicates a great potential for training – those that are likely to excel at a new position and enjoy a high degree of job satisfaction.

Whitepaper: Using the Power of AI to Find Sales Winners

AI + HR
The Winning Combination

AI is not intended to replace your HR professionals and their valuable expertise. Smart HR organizations use AI solutions to augment their capabilities and provide scale and speed. Using AI-powered pre-hire assessments is a prime example – allowing recruiters to focus their time and skills on the candidates with the highest probability of success.

AI guidance can also be helpful in optimizing employees’ career paths. It is a powerful tool for talent-based promotion decisions that are bias-free and avoid the pitfalls of the Peter Principle – promoting people beyond their level of competence.

Beyond Hiring – AI-assisted Talent Management

AI can help HR leaders modernize talent management processes that extend beyond hiring. It can inform decisions about talent development – suggesting specific areas of training and enrichment that would empower an employee to be a top performer.

AI guidance can also be helpful in optimizing employees’ career paths. It is a powerful tool for talent-based promotion decisions that are bias-free and avoid the pitfalls of the Peter Principle – promoting people beyond their level of competence.

What to Look for in an AI Talent Management Solution

  • Accuracy: Is the solution proven to reliably predict future job performance?
  • Customization: Can the models be easily tailored to the characteristics of your organization and job requirements?
  • Bias-free: Are the models proven to offer equal opportunities to all groups?
  • Self-learning: Does the solution become more accurate as more data is collected?
  • Integration:Can it augment your existing processes and systems?