AI and Productivity: Turning Potential into Performance

Alex Honey, Senior Consultant in our Strategy & Change discusses the holistic approach that needs to be taken in order for businesses to boost their productivity and transform their operations.

As the initial hype around AI starts to fade, the focus is shifting to practical questions about its real-world impact on productivity and business transformation. This is especially relevant for small to mid-sized enterprises and traditional industries who are starting their AI journeys.

Like previous technologies, AI has enormous potential.  The question is will it deliver on its promise and improve labour productivity?  Is it going to follow in the footsteps of the motor car and the personal computer, or will it be another shiny distraction?

According to McKinsey research, companies that see real increases in productivity from AI aren’t just “grafting technology onto existing operations”.  The right question isn’t “how could AI help me improve my current process?”, but “how can we redesign our processes to fully leverage AI for maximum impact?”.  This question shifts the focus from merely improving existing processes to fundamentally rethinking and redesigning them to harness AI’s full potential.

Companies that ask, “how can I do something new that hasn’t been done before?”, rather than “how can we get a machine to do the same thing as this person?” will be the most successful of all, as they create opportunities.

Here are a few examples of how AI tools can improve productivity:

  • Content Production: AI helps to create content like product summaries from existing documentation or content for social media posts. It can personalize content for different customer needs and repurpose content for various platforms.
  • Customer Service: AI powers chatbots, enabling customers to get first-level support independently without human interaction.
  • Sales Support: AI listens to sales calls and extracts relevant information, which is then entered into a CRM.
  • Inventory Management: AI predicts inventory needs based on sales data and trends, reducing gaps and overstocks.
  • Business Information: AI automates insights and simplifies the analytical process, reducing the workload for analysts.
  • Document Processing: AI examines documents and extracts relevant information to drive downstream workflows and processes.
  • IT Support: AI monitors systems for issues, provides troubleshooting steps, and even automates routine maintenance tasks.
  • Strategy Development: AI detects trends, supports customer research, designs strategies, and writes presentations.

To get the most out of AI and boost productivity, it’s essential to follow a well-thought-out playbook. This involves understanding the AI tools available and their uses, taking a holistic view of how AI fits into your broader system, engaging and equipping employees, optimizing processes before automating them, ensuring data quality and security, being prepared to iterate, and establishing checks and balances. Let’s dive into each of these steps to see how they can help your organisation maximise the productivity potential of AI.

1. Understand what’s in the AI toolbox

AI is not a single tool but a toolbox. Each tool can be combined with others, and they are continually evolving. Organizations need to understand the capabilities of each tool.

One way of looking at AI is by the type of data they work with – this approach splits AI capabilities into five key groups:

  • Machine Learning: Used for predicting outcomes from various types of data.
  • Natural Language Processing: Helps computers understand, interpret, and generate human language, including text and speech.
  • Audio and Voice: Enables text-to-speech (TTS) and speech-to-text (STT) conversions, as well as voice recognition and synthesis.
  • Computer Vision: Allows computers to interpret and process visual information from images and videos.
  • Generative AI: Can create new content, including text, images, music, and more.

Keeping pace with technological advancements and selecting the right combination of tools is challenging. Therefore, partnering with experts who can provide guidance and investing in continuous learning and development should be a priority.

2.  Take a holistic view

AI should be viewed as part of a broader system that includes other technologies and people. Companies should adopt systems thinking to evaluate the entire organization and make informed decisions about where and how to implement AI for maximum impact. While isolated use cases are useful pilots, Gartner notes that taking a fragmented approach to AI implementation will lead to difficulties in scaling, managing risks, and realizing business value.   Research suggests that companies need to identify a slice of their business and rethink it completely. Changing the technology, operational processes and ways of working of an entire core process, journey or function will deliver a major improvement in performance that wouldn’t be achievable just by changing lots of isolated tasks.

3. Engage and equip your employees

Integrating AI into everyday workflows is a cultural transformation that touches every layer of your business, and its success depends on how well your employees adapt, engage, and work alongside the technology. Change management isn’t a nice-to-do – it’s a must-do. Organizations should consider a range of complementary activities, including regular updates and town hall meetings about AI initiatives to ensure clear communication and reduce resistance; small-scale pilot projects for testing and feedback before full-scale implementation; identifying change champions within the organization to advocate for AI adoption and provide support; customized training programs and e-learning modules to keep skills up to date; and the introduction of feedback mechanisms and incentive programs to encourage employee participation and innovation.

4. Optimise processes before applying AI tools

AI shouldn’t be a bolt-on to existing processes.  Organisations should be reimagining end-to-end workflows to maximize the role of AI and other digital tools, not just in completing actions but also in reporting, decision-making, and strategy development. Lean tools like Value Stream Mapping can help identify and streamline inefficient processes. Significant productivity improvements can often be achieved even before technology is introduced.

5. Ensure your data is secure and high quality

AI systems rely heavily on high-quality data. Organizations must invest time in sorting and cleaning their data, as poor data quality—including inaccuracies, inconsistencies, and incomplete data—can lead to unreliable AI outputs and reduced productivity. It is also critical to have controls in place to govern how sensitive data is handled and secured.

6. Be prepared to iterate

Big bang technology implementations are becoming a thing of the past. Agile methodologies should be used to support the rollout of AI technologies because this approach allows teams to refine the solution incrementally and respond to changes and new findings quickly. The speed of development of AI tools will mean that processes are likely to have a short lifespan before they need to be reviewed again. Continuous monitoring of the performance of AI tools over their lifecycle will highlight opportunities for improvement and trigger reviews of processes.

7. Establish checks and balances

As AI systems perform tasks previously involving human judgment, establishing guidelines for ethical usage, transparency, and accountability is essential. Organizations need to agree on questions such as: Which decisions or processes are too risky for full automation? How will bias be mitigated? How will AI usage rights, personal data privacy, security protocols, and other policies be applied? Are we ok with employees BYOAI (Bring Your Own AI)?, Who will be accountable for AI monitoring, retraining, and model updates post-deployment?

8. Measure the impact

Microsoft reports that 79% of leaders agree their company needs to adopt AI to stay competitive, but 59% worry about quantifying the productivity gains of A.  The best place to start is by establishing some metrics like completion rates and time saved, and gather employee feedback on efficiency and job satisfaction.  Organisations using Copilot will have access to Copilot-usage metrics which can provide insights into how the workforce is using Copilot, including meeting’s summarised and email drafts generated.

The transformative potential of AI technologies is huge, but realizing this potential requires more than just deploying the latest tools. It demands a holistic approach that integrates AI into the broader business strategy, redesigns processes, and continuously adapts to new insights and capabilities. By embracing systems thinking, lean methodologies, agile practices, and robust change management, organizations can navigate the complexities of AI implementation and unlock significant productivity gains.

As you consider integrating AI into your business, take a step back and evaluate your current processes. The journey to AI-driven productivity is not just about technology—it’s about transforming the way you work. Start today by conducting a business diagnostic and mapping out your opportunities. Improving productivity is within your reach, and it begins with thoughtful, strategic action.

If you would like to learn more about AI, our Microsoft Copilot Readiness Service or speak to one of our Agile Project Managers about an AI project please contact us