Talent acquisition is increasingly leveraging artificial intelligence to enhance outcomes. As per Taggd’s India Skills Report 2023, 37% of organizations now use AI for screening and shortlisting candidates, while 47% plan to adopt such technologies soon. The AI based recruitment platform drives greater efficiency, objectivity, and intelligence across sourcing, screening, interviewing, assessment, and analytics.
However, simply buying AI software doesn’t guarantee successful usage or adoption across teams. Like any enterprise-wide tool, integrating recruitment AI into workflows requires concerted change management and execution strategies. Here are some tips for smooth implementation:
Get buy-in from all stakeholders
AI and automation often receive skepticism early on about job losses, bias, and dehumanization. While mostly unfounded, address these concerns through extensive sensitization across people, managers, recruiters, coordinators, and anyone involved in hiring.
Convey how AI augments human intelligence for sophisticated analysis or eliminates repetitive tasks. For example, Taggd’s bots handle high-volume communication for a better experience, while recruiters focus on value-adds like negotiation. Such positioning helps get everyone on board and be AI advocates.
Start small, test, and learn
Don’t attempt an organization-wide rollout in the very beginning, especially for recruiters who are less technology-savvy. Adopt a pilot approach by applying AI to just one or two job roles with high volumes.
Test out the impact on metrics like applications per opening, interview-to-offer rates, overall cycle time reduction, and sourcing bandwidth freed up. Fine-tune the implementation based on early feedback before scaling further. HR analytics from the software will further guide optimal utilization.
Effective change management
AI recruitment platforms bring some fundamental shifts: devolved hiring decisions, unbiased evaluations, automated workflows, and reliance on data-based insights for action. Make such mindset and behavior changes integral to adoption through training and leadership messaging.
Taggd provides dedicated change management guidance with modules on leveraging analytics, minimizing resistance, etc. Monitor user engagement metrics to continually address apprehensions through positive nudges. Make adoption easy, not an obstacle to business as usual.
Develop the required analytical acumen
Platform analytics are powerful but can overwhelm users unfamiliar with deriving intelligence from data. HR teams may need to develop skills in query building, segmenting applicants, correlating metrics, or even the basics like interpreting charts.
Undertake focused analytics training through vendor partnerships to identify performance gaps and refinement areas. Taggd offers comprehensive workshop modules tailored to recruiter and hiring manager personas. Invest time into digesting insights for maximal utilization.
Customization of Internal Processes
Every organization has unique sourcing channels, screening methods, application procedures, and interview stages that have been optimized over time. Instead of attempting to redesign these to fit AI tools, customize the software environment itself.
Taggd allows facile adjustments to skills taxonomy, job description templates, application forms, bot conversations, user access rules, and more. Such configurable components retain existing process flows while plugging in AI for enhanced outcomes.
Ongoing employee engagement
One-time training is inadequate for cementing adoption, given that users discover new system potential gradually. Schedule periodic refresher sessions by internal super-users or vendor representatives to share best practices, case studies, upgrades, etc.
Maintenance reminders, progress dashboards, and feature announcement emails further sustain engagement. Taggd also assigns designated customer success managers for continuous performance guidance and troubleshooting. Plan engagement initiatives upfront for steady usage at scale.
Seamless Integrations
From ATS and HRMS tools to background verification software and assessment platforms, recruitment functions rely on a mosaic of systems. Integrating new AI software into this existing HR tech stack is vital for unified access and end-to-end automation.
Leverage easily available APIs, connectors, and integration marketplaces offered by Taggd and other vendors to embed new data, insights, and predictive intelligence across systems. Such interoperability provides a holistic view of candidates beyond siloed applications.
Multichannel Communication
AI recruitment platforms utilize multiple avenues, like chatbots on career sites, email campaigns, adapter plugins on the ATS dashboard, mobile apps, and more, for interacting with applicants. However, streamlining bot conversations and human conversations across these channels is key.
Tools like Taggd allow creating single candidate records visible across all interfaces, providing continuity. Ensure your implementation strategy knits multichannel touchpoints together into a unified journey.
Ongoing evaluation and improvement
Setting up AI recruitment technology is not a one-time effort but a continuous optimization exercise that tracks ROI. Establish clear milestones around efficiency gains, metrics improvements, and business impact to track post-implementation.
Solicit periodic feedback from users through discussion forums or surveys to uncover areas of enhancement in training, customization, integration, etc. Platform analytics will further guide usage patterns and bottlenecks. Keep evaluating and upgrading implementation effectiveness through the AI adoption life cycle.
Ongoing model retraining
AI algorithms keep improving themselves with new data. To leverage this, recruitment models must be periodically re-trained on the latest candidates and hiring decisions from your organization.
The Taggd platform updates candidate interaction data, assessment results, and other datasets back into the model to enhance its recommendations. Actively trigger this retraining every month or quarter for the best outcomes as roles and preferences evolve.
Extensive Pilots
Run extensive controlled experiments before organization-wide rollout. Test the efficacy of AI in niche or tough-to-fill roles where traditional methods falter.
Compare key metrics like applications per requisition, offer acceptance rates, quality of hire, and recruiting costs against past baselines. Such A/B testing fortifies internal business cases and change management.
Cultural Assessment Boost
AI video interviews and chat conversations allow gauging soft skills and cultural fitment beyond just technical qualifications.
Taggd provides customized scene-setting questions for recruiters to probe candidate alignment with values like innovation, collaboration, etc. Sync responses against company ethos for a well-rounded evaluation, not just skill matching.
Conclusion
The simplicity of modern AI recruitment platforms can often hide the intricacies behind making them work successfully for an organization. Rethink AI in talent acquisition as a synergistic human-machine partnership powered by data intelligence.
With these actionable tips around change management, customization, training, and integrations, set your teams and processes for AI readiness. While tactical aspects are important, the vision and culture to become an insight-driven recruitment organization are vital to implementation success.
When planning considers nuances and provides supportive infrastructure, seamless adoption at scale is possible. It’s time to start your AI recruitment journey today towards building agile, future-ready talent acquisition for tomorrow! Reach out to Taggd experts to begin leveraging recruitment intelligence.