Recent studies have revealed a significant uptake of AI tools in the workplace, with a substantial percentage of knowledge workers across various professions utilizing AI to enhance their productivity. For instance, in Denmark, 65% of marketers and 64% of journalists reported using AI at work, while in the U.S., one-third of workers had used generative AI (GenAI) in the previous week.

Despite individual workers experiencing notable productivity gains from AI use, many organizational leaders report limited AI adoption and minimal productivity improvements beyond specific authorized use cases.

This discrepancy highlights a crucial challenge — translating individual AI-driven performance boosts into organizational-level gains.

Many employees are experimenting with AI tools to improve their work efficiency but are not sharing their findings with their employers. These employees keep their AI use hidden for various reasons:

• Fear of punishment due to unclear AI usage policies

• Concern about losing respect or recognition for their work

• Worry about potential job losses if AI capabilities are revealed

• Lack of incentives to share AI-driven productivity gains

• Anticipation of increased workload expectations without compensation

• Finally, many companies don’t have an AI Usage policy

To capitalize on AI’s potential in the workplace, organizations need to adopt two key approaches:

First approach:

• Reduce fear: Implement clear, permissive AI usage policies that encourage ethical experimentation. For example, researchers warn AI drug discovery systems might be repurposed to make chemical weapons. A demonstration with AI drug design software shows the ease with which toxic molecules can be generated

• Align reward systems: Offer significant incentives for sharing AI innovations and productivity gains.

• Model positive use: Executives should openly use and discuss AI applications that are relevant to their organization

• Build community: Create opportunities for AI enthusiasts to share their knowledge and experiences.

• Provide tools and training: Offer access to advanced AI models and provide introductory training sessions.

Second approach (R&D type effort):

Establish a dedicated team of subject matter experts and technologists to focus on AI integration:

• Develop AI benchmarks: Create organization-specific tests to evaluate AI performance on relevant tasks.

• Build and iterate: Transform ideas from the Crowd into functional tools and prototypes.

• Explore future possibilities: Experiment with AI agents for key business processes to anticipate future advancements.

• Create provocations: Develop demos that showcase AI’s potential to engage and inspire employees.

Long-term considerations

As AI capabilities continue to advance, organizations must look beyond immediate innovation to consider:

• AI-aware leadership

• Organizational transformation: Explore new company roles for humans and machines.

• Preparing for autonomous AI: Consider the potential impact of AI agents capable of independent planning and action.

By embracing grassroots experimentation and focused R&D efforts, organizations can better position themselves to harness AI’s transformative potential in the workplace. This approach allows companies to navigate the uncertain future of AI development while maintaining an agency to shape their technological landscape.

How to identify the best areas of your business to integrate AI?

To identify the best areas of your business to integrate AI, consider the following approach:

1. Conduct a thorough assessment of your current processes

• Map out your key business processes and workflows.

• Identify manual, repetitive, or time-consuming tasks that could benefit from automation.

• Look for areas where data analysis and decision-making could be improved.

2. Align potential AI use cases with strategic goals

• Review your organization’s mission statement and strategic objectives.

• Identify how AI could support your growth agenda, improve efficiency, or reduce costs.

• Consider how AI could enhance your products, services, or customer experiences.

3. Evaluate data availability and quality

• Assess the quality, quantity, and accessibility of data in different areas of your business.

• Prioritize areas with rich, well-structured data that AI can leverage effectively.

4. Consider potential impact and feasibility

• Estimate the potential business impact of AI implementation in different areas.

• Assess the technical feasibility and resource requirements for each potential use case.

• Look for “quick wins” that can demonstrate value quickly and build momentum.

5. Engage stakeholders across the organization

• Conduct interviews or workshops with department heads to identify pain points and opportunities.

• Encourage employees to share ideas for AI applications in their work.

6. Prioritize use cases

• Create a master list of potential AI use cases across your organization.

• Evaluate each use case based on strategic alignment, potential impact, feasibility, and resource requirements.

• Prioritize use cases that offer the best balance of impact and ease of implementation.

7. Start small and scale

• Begin with pilot projects or proofs of concept to validate ideas and build expertise.

• Use insights from initial projects to refine your approach and inform larger-scale implementations.

By systematically evaluating your business processes, aligning with strategic goals, and engaging stakeholders, you can identify the most promising areas for AI integration in your organization.

Remember to consider both the potential benefits and the practical considerations of implementation when making your decisions.

In conclusion, beginning with a clear and shared understanding of what you want to achieve is crucial.

*Note: The opinions expressed in the article are that of the authors and not of the organizations they represent.

About the Author:

Partha Anbil is at the intersection of the Life Sciences industry and Data & Analytics, including GenAI/ML/NLP. He is currently with NYSE-listed WNS, a digital-led business transformation company, as Senior Vice President and Practice Leader for their Life Sciences practice. He has over twenty-five years of experience in the industry and was recently the Chief Data and analytics AI officer at IBM’s HCLS integrated Accounts. He is a certified distinguished Data Science Thought Leader by both IBM and the Open Group, an industry consortium.

Before IBM, he held senior leadership roles at Booz & Company (now PwC Strategy&), Wolters Kluwer Pharma Solutions (now Symphony), IMS Health Management Consulting (now IQVIA), KPMG Consulting, and PricewaterhouseCoopers (PwC) LLP.

Anbil has consulted and counseled life science clients in structuring solutions for strategic, operational, and organizational challenges and issues. He has advised clients on different Clinical, R&D, and Commercial strategies based on Market, Product, and Company Characteristics, as well as Regulatory Environments – counseled Life Sciences clients on maximizing Commercial Performance and R&D Productivity of their products from clinical development to initial launch through product maturities to maximize their ROI.

Vivek Suryanarayanan is a seasoned technology leader with over 18 years of experience driving digital transformation, data analytics, and GenAI solutions in the pharmaceutical and life sciences industry. He has deep expertise in leading product development teams and is highly proficient in cloud technologies and data management. He spearheads large-scale digital transformation programs and strategic initiatives at Takeda Pharmaceuticals. Before this, he served as a Sales Engineering Leader at Experian, providing  technical consulting to life sciences and public sector organizations. In his prior role at PerkinElmer, he collaborated with 20+ pharmaceutical and health sciences companies, developing advanced analytics solutions for Clinical Data Review and Risk-Based Monitoring teams.