At Sommo, we actively leverage GenAI to accelerate product development, speed up time to market, and capture a larger audience. From writing code to generating ideas for user retention and creating AI support bots, GenAI is an integral part of our process.
Our excitement about GenAI's success always comes with understanding how fleeting this success can be and the need for constant oversight. Roman and I discussed this in more detail, with examples, in episode 2 of our podcast.
Artificial intelligence in the workplace can be both a powerful advantage and a potential challenge. I explored the benefits and challenges of AI in the workspace, providing insights from leading industry reports and practical strategies for organizations to harness AI effectively while mitigating potential drawbacks.
AI in the workspace: benefits
The Deloitte report "State of Ethics and Trust in Technology" has been one of the most exciting studies for me in the field of AI for business in 2024. Here are some key numbers from it:
- 94% of respondents said they are using GenAI, and 87% are increasing its use.
- 54% identified GenAI as the most significant risk among emerging technologies.
- The application of GenAI in high-tech sectors is expected to generate up to $460 billion, with the greatest impact in software engineering, marketing, and sales.
By the way, McKinsey estimated the contribution of GenAI to the global economy at $2.6 to $4.3 trillion per year.
Here are the top reasons why companies are implementing AI in the workplace (source: Deloitte).
№1 Improved efficiency, productivity, revenue, and cost reduction.
AI has the potential to automate tasks, improve decision-making, and provide valuable insights into business operations. Businesses are using AI to improve efficiency and productivity by automating repetitive tasks and streamlining processes. For example, AI-powered chatbots can provide immediate and personalized responses to customer inquiries, freeing up human customer service representatives to handle more complex issues.
№2 Іncreased innovation, improved products, and services.
AI can help businesses develop new ideas and products. More companies experimenting with and implementing AI will likely lead to more innovation. Generative AI can create new content, such as images, text, music, and other media, that mimic human creativity. This capability can be used to develop new products and services.
№3 Enhanced customer relationships.
AI can help businesses provide more personalized and efficient customer service. Generative AI can be used to personalize marketing messages and provide tailored product recommendations, leading to a better customer experience.
Credits: Deloitte
Here are some questions to help estimate the benefits of Generative AI for your company:
1. What are the key business functions in our company where Generative AI could be applied?
Research indicates that Generative AI has the most significant potential to add value in areas such as customer operations, marketing and sales, software engineering, and product research and development (R&D). Identify which functions are most critical to your company's operations and focus on exploring use cases within those areas.
2. What tasks or processes within those functions could be automated or augmented by Generative AI?
Generative AI is particularly effective for tasks that involve natural language processing, content creation, data analysis, and problem-solving. Identify specific tasks within your chosen functions that align with these capabilities, such as generating marketing copy, drafting code, or analyzing customer feedback.
3. What measurable outcomes or KPIs could we use to assess the impact of Generative AI in these areas?
Define clear metrics to track the potential benefits of implementing Generative AI, such as:
- Customer operations: Increased issue resolution rate, reduced handling time, improved customer satisfaction, decreased agent attrition.
- Marketing and sales: Increased lead generation, improved close rates, enhanced customer engagement, and increased sales productivity.
- Software engineering: Increased code development speed, reduced bug rates, and improved system design efficiency.
- R&D: Reduced research and design time, improved simulation and testing accuracy, and faster development of new products.
Risks of AI in the workspace
While AI can bring significant benefits, there are also risks to consider.
1. Data Security and Privacy: AI systems, particularly those that utilize large language models (LLMs), require vast data for training and operation. This raises concerns about potential data leaks, privacy violations, and misuse of sensitive information. Implementing strict security measures, establishing clear data usage guidelines, and ensuring compliance with relevant privacy regulations are crucial.
2. Bias and Discrimination: AI models can inherit biases from the data on which they are trained, which may lead to discriminatory outputs or unfair treatment of certain groups. Evaluating AI systems for bias, ensuring fairness in their development and deployment, and creating mechanisms to mitigate potential harms are essential.
3. Reliability and Accuracy: AI systems may occasionally generate inaccurate, misleading, or fabricated information—often called "hallucinations." To ensure the reliability and accuracy of AI outputs, it is recommended to incorporate human review, quality control checks, and continuous monitoring.
Credits: Deloitte
Here are some questions you should ask to estimate your company's costs of Generative AI.
What data do we have available that could be used to train and fine-tune Generative AI models, and how can we ensure data quality and security? Assess your data's quality, availability, and security to ensure it is suitable for training GenAI models. Consider factors such as data privacy, bias, and compliance with regulations.
What are the potential risks associated with implementing Generative AI in these areas, and how can we mitigate them?
What investments in technology, infrastructure, and talent will be required to implement and scale Generative AI effectively? Determine the resources needed for successful implementation, including hardware, software, cloud computing, and skilled personnel.
How can we embed Generative AI seamlessly into existing workflows and processes to maximize its impact? Focus on integrating GenAI into workflows to enhance productivity and streamline operations rather than treating it as a standalone solution.
How can we measure and communicate the value created by Generative AI to stakeholders, including employees, customers, and investors? Develop a framework for quantifying and showcasing the benefits of GenAI implementation using financial and non-financial metrics.
How can we foster a culture of responsible AI usage within our company, ensuring ethical considerations are prioritized throughout the development and deployment process? Establish clear guidelines, training programs, and governance structures to promote responsible AI practices.
Getting more of AI in the workspace
In my opinion, a perfect analogy for the implementation of AI in the workspace sounds like this:
"An LLM is like an engine. No one just wants the engine of a car or a plane; they want a car or a plane. So, there are all these things you need to do to make it part of business processes so the business can use it."
Like other technologies, generative AI will only realize its full potential when integrated into everyday tasks. While early projects have shown promising results and driven increased investment in generative AI, organizations must demonstrate consistent and significant value as quickly as possible.
Credits: Deloitte
Consider the following actions as a leader to get more benefits and reduce the risks associated with AI in your company's workspace.
1. Develop a Clear AI Strategy and AI Usage Policy:
- Create a Committee: Assemble a diverse group of stakeholders, including executives, board members, legal counsel, technical experts, and representatives from different departments, to develop and implement a comprehensive AI usage policy.
- Define Clear Objectives and Principles: Clearly articulate the company's objectives for using AI, such as increased efficiency, improved customer experience, or product innovation.
- Communicate the Policy Effectively: Once the Policy is developed, communicate it clearly and comprehensively to all stakeholders through training, workshops, and accessible resources.
2. Focus on Workforce Adaptation and Upskilling:
- Invest in AI Training and Education: Provide comprehensive training programs for employees at all levels to build their AI literacy and skills.
- Promote Collaboration Between Humans and AI: Recognize that introducing AI into teams can impact performance and coordination. Implement strategies to address potential aversion to working with AI agents, promote trust and job satisfaction, and ensure AI implementation enhances rather than hinders employee engagement.
- Consider the Evolving Nature of Work: Anticipate the potential for significant workforce transitions due to AI automation and the need for new skills and capabilities.
Credits: Deloitte
3. Prioritize Secure AI Tool Evaluation and Data Handling:
- Implement a Rigorous Evaluation Process: Establish a process for evaluating AI tools before incorporating them into workflows.
- Emphasize Data Security and Privacy: Institute strict data handling and privacy guidelines, mainly when using AI tools that involve sensitive company or customer information.
- Address Data-Related Challenges: Acknowledge that data issues can limit your options for AI adoption.
4. Balance Automation with Human Involvement:
- Use AI to handle repetitive and time-consuming tasks, freeing up your employees for more creative and complex work.
- Ensure that human oversight is present in critical decision-making processes where AI plays a role, maintaining accountability and adaptability.
5. Communicate Openly and Address Concerns:
- Maintain open communication about AI initiatives to reduce fear and uncertainty.
- Reassure employees that AI is there to support them, not to replace them. Highlight success stories where AI has led to improved outcomes for the team.
- Address concerns and engage employees in discussions on AI's ethical and practical impacts on their work.
6. Encourage Pilot Projects and Experimentation and establish Cross-Functional Teams for AI Projects:
- Encourage teams to experiment with AI in small-scale pilot projects to learn from mistakes and successes before broader implementation.
- Track results, gather feedback, and adjust strategies based on what works well and what doesn't.
- Form cross-functional teams that include experts from different areas (e.g., IT, operations, HR) to ensure AI systems are built and deployed with a holistic approach.
- Include diverse perspectives in the AI development process to ensure alignment with the needs and values of various stakeholders.
7. Measure and Communicate the Value of AI Initiatives:
- Develop Measurable KPIs: Identify key performance indicators (KPIs) to track the impact of AI implementations on efficiency, productivity, cost reduction, innovation, customer satisfaction, and other relevant metrics.
- Communicate Successes and Challenges: Regularly communicate AI initiatives' progress, successes, and challenges to stakeholders throughout the organization.
AI in the workplace has the power to revolutionize productivity and innovation, but its success hinges on thoughtful integration and risk management. Leaders must prioritize a balanced approach—combining strategic policy development, human involvement, and continuous learning—to truly harness its potential.
In the next article, we’ll dive into how to create an AI acceptable use policy, complete with a handy template to guide you through the process. Stay tuned!
