AI in the workplace: 10 key use cases, benefits, and challenges

Widespread automation can spark uncertainty and concern due to skill gaps and potential job losses, causing skepticism and resistance to AI adoption among your staff. Indeed, Mercer points out that according to executives, only 45% of their employees could adapt to a job market reforged by AI. Still, the prospect of simply replacing workers with new, AI-ready staff is inviting but also naïve in a job market notoriously affected by talent shortage.

The WEF stressed the importance of proactively investing in staff retraining and upskilling to reallocate the affected workforce to new positions opened through AI-driven innovations. Specifically, the focus should move from manual skills to technological, cognitive, and emotional competencies. Role transitions can be complemented by external partnerships with experts in relevant fields, including AI and data science. The rapid spread of remote work models fostered by collaboration tools and virtual workplaces can help in this regard.

Some enterprises struggle to harmonize AI tools with existing operations and workflows, which leads to further inefficiencies and bottlenecks instead of synergies and performance gains. For example, an ecommerce company can enhance its marketing strategies with AI-driven user segmentation and targeted ads to boost sales while disregarding order management, inventory, and other functions to fulfill the consequent increase in orders.

Your operating model should evolve to accommodate AI software. Consider establishing centers of excellence and appoint the most skilled professionals to coordinate workflow adjustments. You can also opt for a progressive adoption, allowing your staff to get familiar with new technologies, collecting feedback from initial deployments, and gradually updating legacy processes. Another way to balance out different workflows is through BPM best practices and platforms, such as Pega and Odoo.

AI model training is a complex procedure, and relying on low-quality, inconsistent datasets offering an incomplete vision over key trends can prevent algorithms from identifying patterns across data points. This, in turn, can lead to biased analyses and inaccurate predictions once the model is deployed. Furthermore, its performance can degrade over time due to changes in input features and relationships among variables (the so-called ML model drift).

You should train your model on reliable, high-volume data from selected sources. This requires data mapping, cleansing, and transformation via ETL pipelines, along with its consolidation in a data lake or data warehouse to create a single source of truth. Prepared data can be divided into training, validation, and test sets to split modeling into multiple phases and mitigate overfitting issues. After deployment, the model should be monitored and fine-tuned with “fresh” datasets in multiple iterations based on MLOps best practices.

Given AI's data-centric processes and policymakers' growing interest in data protection, companies should effectively address privacy concerns and cope with the higher risk of cyber exposure. In fact, AI solutions can handle sensitive information, such as financial transaction data, IP, or trade secrets, which can become the target of frauds, hacking, and breaches. Moreover, integrations with external systems can further amplify such issues.

Protect your AI solution and data with solid cybersecurity features. These encompass identity and access management, multi-factor authentication, user activity monitoring, and encrypted data exchange via cryptographic protocols (such as Transport Layer Security or X.509 certificates). Also, adopt a solid data governance strategy, including best practices and internal policies to properly manage your data assets, and ensure your software complies with the US HIPAA, European Union’s GDPR, or other applicable standards.

AI-enabled analyses and operations require a stream of information from multiple corporate systems (including legacy software and IoT devices), which can handle various data types or formats and use different communication protocols, namely sets of rules defining how to transmit or exchange data across a network. When such systems are fragmented and do not interact properly, the result is information silos, data inconsistencies, and inaccurate insights.

You should integrate your systems via APIs. Cloud data integration services offered by major cloud providers (such as AWS Glue or Azure Data Factory) and iPaaS platforms (including Informatica, Boomi, and Workato) facilitate this process by providing pre-built APIs. You can also rely on data virtualization techniques or build middleware architectures like ESBs to convert different protocols. Whenever possible, however, adopting systems with shared protocols or complying with your industry's interoperability standards can be the best option.