AI strategy for CIOs: The top 5 productivity problems to solve

Today, CIOs are more than IT managers; they’re strategic leaders driving business transformation through technology. In today's rapidly evolving digital landscape, crafting a robust AI strategy is imperative for CIOs to successfully lead their organizations. With AI hype at an all-time high, CIOs must navigate the noise to identify and implement truly impactful AI solutions.

In this blog, we're going to dive into the insights from a recent webinar hosted by Varun Singh, President and Co-Founder of Moveworks. We’ll explore five key challenges that are keeping CIOs up at night and how AI can serve as a solution. 

Whether you're a seasoned CIO looking to stay ahead of the curve, or you're new to the game and feeling a bit overwhelmed, don't worry. Discover how to prioritize your AI roadmap to align with your specific business needs, guiding your organization into a future where AI isn't just a buzzword, but a powerful tool in your strategic arsenal.

Here’s what we’ll cover:

• The knowledge worker’s day to day
• The top 5 problems AI can help solve:
  • Hard-to-find information
  • Inefficient workflows
  • Poor user experience in business apps
  • Time consuming in-app workflows
  • Lack of app extensibility
• Key considerations for building an effective AI strategy
  • Measuring success
  • Ensuring security compliance
• Choosing the right vendor

Understanding the knowledge worker's day

Before diving into the specific problems, let’s understand the day-to-day tasks of knowledge workers. Identifying the pain points in their workflows is crucial, as these are the areas where AI can have the most impact by streamlining operations and boosting productivity.

Typical tasks and workflows

Knowledge workers juggle a variety of tasks each day. Their workflows are often complex, requiring bouncing from one task to another, and constantly needing to context switch. These tasks include:

• Checking and managing calendars
• Preparing for and attending meetings
• Drafting emails and reports
• Collaborating with team members
• Accessing and synthesizing information from various sources
• Communicating with clients and vendors

Identifying productivity pain points

Despite their best efforts, knowledge workers may frequently encounter productivity barriers. These pain points can include:

• Data sprawl and information silos: Information is scattered across applications and content repositories, making it difficult to find and synthesize relevant data.
• Inefficient workflows and manual processes: Repetitive tasks and cumbersome workflows create bottlenecks and hinder overall productivity.
• Complex user interfaces: Complex interfaces with steep learning curves slow down users and decrease efficiency.
• Time-consuming in-app workflows: Navigating and managing in-app workflows for creative and analytical tasks is often time-consuming.
• Lack of application extensibility: Limited functionalities in applications restrict their ability to adapt to evolving needs.

The potential for AI to streamline operations

For CIOs, the enterprise-wide impact of AI is particularly exciting. Even with innovations like ChatGPT, AI primarily enhanced efficiency within isolated business units. The truth is that while AI is often percieved as helping individual productivity, its implications are much broader. 

AI does more than just help individuals; it now has a far-reaching impact effecting the entire enterprise. This shift isn’t just impressive, it's game-changing. By addressing the key pain points we've discussed, AI can reshape how knowledge workers in every department spend their day.

AI is redefining operations in every department: automated systems handle routine tasks, AI-powered information retrieval instantly extracts precise data from company databases. Complex interfaces become intuitive, boosting productivity at all levels of the organization, and so much more.

AI allows knowledge workers to focus on high-value tasks that require creativity and critical thinking. It paves the way for a more efficient, innovative, and productive workforce, enabling CIOs to drive enterprise-wide change.

The top 5 productivity problems to solve with your AI strategy 

Having an AI strategy can help businesses overcome common challenges that hinder productivity and growth. Let's go deeper into some of these problems and explore how AI-powered solutions can empower your workforce.

Problem 1: Employees struggle to find information

Information overload is a real challenge. Employees often waste valuable time searching for the data they need, leading to frustration and decreased productivity.

Issue: Data sprawl and information silos

Data sprawl and information silos are significant obstacles for employees. With data scattered across numerous apps, emails, and documents, locating the right information becomes a time-consuming challenge. This fragmentation not only frustrates employees but also hampers overall productivity. 

When information is locked in silos, it impacts the entire organization. Teams miss insights, opportunities slip away, and decision-making slows down.

Solution: AI-driven search and synthesis

AI-driven search and synthesis tools are reshaping the way organizations manage and retrieve information. They help employees find information across applications and content systems, breaking down data silos by searching across multiple platforms.

These tools excel at:

• Understanding natural language queries
• Processing both structured and unstructured data
• Aggregating information from various sources
• Creating knowledge graphs to link related data
• Saving time on manual searches
• Summarizing findings for quicker decision-making

An effective AI solution must integrate with multiple content systems and adhere to underlying permissions without compromise. Often, these tools use large language models for search, retrieval, and summarization. It’s crucial to ensure these tools are integrated with your chat platforms like Microsoft Teams or Slack. 

Implementing AI-powered enterprise search engines or knowledge management systems can dramatically improve your team's efficiency. They help everyone find and use the right information at the right time.

Problem 2: Employees are unable to get things done

What if your employees have all the information they need, but inefficient processes are still holding them back? Consider how some outdated workflows and manual procedures can create bottlenecks.

Issue: Inefficient workflows and manual processes

Many employees find themselves navigating complex processes just to complete simple tasks like booking time off or ordering supplies. These processes often span multiple business applications, creating confusion and delays. To overcome these hurdles, employees frequently rely on IT and HR services, which — especially when there are hundreds of routine issues bogging down these teams — can lead to additional operational inefficiencies.

For instance, imagine an employee trying to arrange a business trip. They might need to:

1. Submit a travel request through an HR system
2. Get approval from their manager via email
3. Book flights and accommodation through a separate travel portal
4. Submit an expense report using yet another finance application

This cycle of repetitive tasks wastes valuable time and resources, hindering overall progress.

Solution: AI-driven workflows and reasoning

By implementing AI-powered AI-driven workflow automation tools with reasoning capabilities, you're not just saving time — you're enhancing overall efficiency and allowing your team to focus on what truly matters.

Maybe an employee needs to book time off. They might have to flip between their HR PTO documentation, Workday, and their calendar. With AI-driven workflows, these tasks become seamless. AI can handle approvals, tickets, procurement actions, and project tracking, integrating smoothly with multiple business systems.

What does this look like across the enterprise? Here are a few examples:

1. Automated approval processes: An AI system can route travel requests to the appropriate manager, automatically approve requests within certain parameters, and trigger necessary actions across multiple systems.
2. IT service desk automation: AI copilots can handle common issues, such as password resets, freeing up IT staff for more complex tasks.
3. Procurement automation: An AI system can guide employees through the purchasing process, automatically routing requests for approval based on company policies.
4. Project tracking: AI can update project statuses across different tools, send automated reminders, and flag potential delays or issues before they become critical.

AI-driven workflows offer a way to cut through the operational inefficiencies, enabling employees to get things done faster and more effectively.

Problem 3: Business apps have cumbersome UX

Many business applications suffer from cumbersome user experiences (UX), often requiring numerous clicks and detailed knowledge to perform a single task. This complexity can turn even simple tasks into time-consuming ordeals, impacting overall productivity and employee satisfaction.

Issue: Complex interfaces hindering productivity

Navigating business applications can be difficult for multiple reasons. We’ve all experienced a non-intuitive interface. And it doesn’t help that every different system seems to have a slightly different approach. This complexity not only slows down experienced users but also creates a steep learning curve for new employees, impacting overall productivity.

When your team is spending more time figuring out how to use a tool than actually using it, that's not great for productivity. It's not just frustrating; it's a significant barrier to adoption and efficiency. 

Solution: Application-specific copilots

This is where application-specific copilots come in. Powered by AI, these copilots simplify user interactions within business applications, allowing employees to navigate complex systems and complete tasks efficiently without leaving the application.

For example, connecting an AI copilot  to your CRM system could help sales teams update records and generate reports effortlessly. These tools are not just about convenience; they're about boosting productivity and reducing friction. They flatten the learning curve for new users and help experienced ones work faster. By simplifying interactions with complex software, app-specific copilots allow your team to focus more on their actual work and less on wrestling with tools.

Problem 4: In-app workflows are time consuming for employees

Even when applications have user-friendly interfaces, the workflows within them can still be time-consuming and inefficient. This is especially true for tasks requiring creativity or complex analysis, where employees often get bogged down in repetitive processes instead of focusing on high-value work.

Issue: Inefficient creative and analytical tasks

In-app workflows can sometimes be productivity blockers, particularly for tasks requiring creativity or complex analysis. For instance, employees might spend excessive time comparing multiple document versions, organizing ideas from brainstorming sessions, or manipulating images in design software. Streamlining these workflows through AI can significantly reduce the time spent on these tasks, enabling employees to focus on what truly matters.

Solution: AI-powered workflows

AI can transform how we work within applications by automating complex workflows, freeing up employees to focus on more critical tasks. 

For example, design software that uses AI to automatically generate design variations, allowing designers to spend more time on creativity rather than repetitive tasks. Legal professionals can benefit from AI summarizing lengthy document changes, saving time and improving accuracy. Product managers can leverage AI to summarize outcomes from digital brainstorming sessions, eliminating the need for manual organization and clustering of ideas.

AI-powered workflows enable employees to focus on what truly matters, enhancing efficiency and productivity across the board.

Problem 5: Apps are often not extensible

When AI applications aren't extensible, they hit a ceiling on their usefulness. Without the ability to expand their capabilities to new tasks, domains, and datasets without retraining, these applications may limit an organization’s potential to adapt and grow with evolving business needs. Over time, this rigidity stifles innovation and limits the effectiveness of technological investments.

Issue: Lack of flexibility and adaptability in applications

Many applications today fall short when it comes to extensibility. Their lack of flexibility makes it difficult to adapt to evolving business needs, extend functionalities, or integrate new solutions. This limitation can significantly reduce their long-term value and restrict their usefulness, ultimately hindering strategic goals.

Solution: Extensibility capabilities

Choosing AI solutions with robust extensibility capabilities is crucial for maximizing long-term value. These platforms ensure your applications can adapt to future requirements and support continuous innovation. Look for AI applications that enable developers to extend their functionality without needing to worry about the underlying complexities of large language models or AI.

Platforms like Moveworks provide extensibility features that allow developers to create additional automations and extend workflows easily. This ensures that your AI investments remain relevant and valuable over time, seamlessly integrating with other tools and adapting to new use cases. By prioritizing extensible AI solutions, you're future-proofing your technology and fostering a culture of long-term innovation within your organization.

How to build an AI strategy

Creating a robust AI strategy involves careful planning and strategic alignment to ensure success. The following sections explore essential elements of building an effective AI roadmap, from setting clear goals and measuring ROI to addressing security concerns and fostering organizational adoption.

Building an effective generative AI strategy

Here’s how CIOs can maximize the impact of AI initiatives:

1. Align AI initiatives with business objectives: Impactful AI projects support and enhance your core business goals. This alignment is crucial for gaining executive support and ensuring the relevance of AI efforts.
2. Prioritize use cases: Some AI applications offer more impact than others. Evaluate potential use cases based on their feasibility and the value they can bring to the organization.
3. Balance quick wins with long-term vision: Striking the right balance between quick wins and long-term strategic goals is essential. Quick wins can demonstrate the value of AI and build momentum, while a long-term vision ensures sustainable growth and continuous improvement.
4. Adopt a phased approach: It can be best to start with measured bets to learn and iterate. Developing a phased approach allows for gradual implementation, learning from each stage, and scaling successful initiatives.
5. Foster a culture of AI adoption and continuous learning: Encourage an environment where employees are excited about AI and its potential. Continuous learning and adaptation are key to staying ahead in the fast-evolving AI landscape.

By following these guidelines, CIOs can build a robust and effective generative AI strategy that not only meets immediate needs but also positions the organization for future success.

Goals and measuring the ROI of AI vendors

When it comes to AI implementation, setting realistic expectations is crucial for success. CIOs need to establish clear, measurable goals to guide their AI initiatives. Here’s how to do it effectively:

1. Define clear goals: What you want to achieve with AI? Whether it's improving efficiency, reducing service desk tickets, or enhancing customer satisfaction, having specific goals helps in steering your strategy in the right direction.
2. Measure ROI: Establish metrics to measure the return on investment (ROI) from your AI vendors. Consider focusing on tangible metrics like a reduction in service desk tickets, cost savings, and productivity gains.
3. Communicate value: It can beimportant to communicate both the hard cost savings and the softer productivity gains to stakeholders. Highlight how AI solutions not only save money but also improve overall efficiency and employee satisfaction.
4. Promote adoption: Successful AI implementation tends to hinge on adoption across the organization. Ensure you have a comprehensive adoption strategy in place. This includes providing adequate training, clear communication about the benefits of the AI solution, and ongoing support. 

Remember, a solid AI strategy isn't just about implementation — it's about measuring success and continuously improving. By setting clear goals and keeping a close eye on your AI vendors' performance, you'll be well on your way to maximizing your AI investments.

Security and data privacy

When it comes to implementing AI solutions, security and data privacy should be at the top of your list. You're dealing with vast amounts of data, often including sensitive information about your company, customers, or operations. The last thing you want is for that data to fall into the wrong hands or be misused.

It’s essential to ensure your chosen AI vendor has robust security protocols and complies with industry standards and regulations. Here’s what to focus on:

1. Prioritize security: Does your AI vendor have strong security measures in place to protect sensitive information from breaches and misuse? This includes encryption, access controls, and regular security audits.

2. Ensure compliance: Verify that your AI vendors are up to speed with all the relevant industry standards and regulations. GDPR, CCPA, HIPAA — whatever applies to your sector, your vendors need to be on board.

3. Protect data privacy: Ensure that the AI solutions respect data privacy, handling personal and sensitive information with the utmost care. This involves transparent data practices and user consent management.

By putting security and privacy at the forefront of your AI strategy, you're not just protecting your data — you're safeguarding your company's reputation and future.

Choosing a vendor

When it comes to selecting the right AI vendor, CIOs should be thorough and strategic. Here are some key things to consider when evaluating potential AI vendors:

• Define your needs: Before beginning your search, conduct a comprehensive needs assessment with key stakeholders to clearly identify your organization's requirements and pain points.
• Alignment with business needs: Focus on finding a vendor whose solutions actually address your business needs and ease your team's pain points. It's not about having the flashiest tech; it's about having the right tech for you.
• Scalability and performance: Can the vendor's solution grow with you? It should handle your needs today and have the flexibility to adapt as your business evolves.
• Integration capabilities: Ensure the AI solution integrates seamlessly with your existing IT infrastructure and applications. 
• Vendor expertise and reputation: Evaluate the vendor's expertise in large language models and conversational AI. Consider their reputation and customer support.
• Enterprise-specific features: For enterprise-level implementations, look for vendors offering custom AI models, strong analytics capabilities, and solutions that can understand and leverage your company-specific data.
• Security and data privacy: These are non-negotiable. Choose a vendor with a proven track record of robust security protocols and a strong commitment to data privacy compliance to protect your sensitive information.
• Personalized demos: Request demos using your own data to gain valuable insights into the tool's functionality and compatibility with your infrastructure.
• Implementation and ongoing support: A successful AI strategy requires more than just a great product. Look for vendors that offer comprehensive implementation services and ongoing support to help your team effectively leverage the solution.

Selecting an AI vendor is an investment in your organization's future. By carefully evaluating vendors based on these criteria and understanding the differences between different AI tools, you can find the ideal partner to empower your workforce and achieve your goals.

Transforming productivity with AI

The most successful leaders will be those who view AI not as a standalone solution, but as an integral part of a broader digital transformation strategy — one that empowers employees, enhances decision-making, and ultimately propels the entire organization forward in an increasingly competitive landscape.

To achieve this vision, CIOs must focus on employee pain points, aligning AI initiatives with core business objectives, and choosing the right partners.

As you begin to create your own AI strategy, consider these next steps:

1. Work backward from employee journeys to identify pain points and opportunities for AI integration.
2. Focus on creating seamless experiences that don't disrupt existing workflows.
3. Choose solutions that can scale from departmental improvements to business-wide transformation.
4. Empower developers with AI tools that offer extensibility and customization.

Ready to solve your organization's productivity challenges with a powerful AI strategy? Request a demo today and discover how Moveworks can streamline your operations and drive innovation.