Realising Generative AI Productivity Gains

Generative AI has been a game-changer since the launch of ChatGPT two years ago. The benefits of this groundbreaking technology are no longer in question, yet many businesses are still struggling to realise tangible productivity improvements. Why? The challenges lie in rolling out AI-powered solutions effectively, balancing security concerns, and aligning AI with existing workflows. To help businesses overcome these obstacles, let’s explore how to unlock generative AI’s full potential—without replacing the systems and processes that already work.

The BYOAI Challenge: A Double-Edged Sword

The rise of Bring Your Own AI (BYOAI) - where employees use their personal AI tools at work - is both a blessing and a curse. On one hand, employees using free AI services like ChatGPT can boost individual productivity. On the other, this trend creates significant risks for organisations, including:

• Data Privacy Risks: Employees may inadvertently expose sensitive company data when using AI tools that have not been vetted and approved by IT security.
• Compliance Issues: Free AI services may not meet corporate compliance or regulatory standards.
• Inconsistent Adoption: While some employees thrive with AI tools, others resist change, leaving organisations with patchy adoption.

Even when companies provide AI tools for corporate use, it’s up to employees to actually use them. Many prefer to stick with traditional workflows or tools, creating a divide between teams that embrace AI and those that lag behind.

The Next AI Frontier: From Chatbots to Intelligent Agents

The excitement around AI has shifted from simple chatbots to AI agents - tools capable of reasoning, problem-solving, and automating more complex tasks. However, this shift presents a dilemma:

• Should companies invest in entirely new, unproven AI-driven technologies?
• Or should they enhance their existing workflows by integrating generative AI?

Most businesses have already made significant investments in automating critical processes. Replacing these systems with new AI software isn’t practical, especially when it involves re-skilling entire teams or risking disruption. Instead, the smarter approach is to enhance and evolve existing automation projects with AI.

Building Trust with In-Context AI

How do you break down resistance to AI adoption? The answer lies in in-context AI - embedding AI tools into the software employees already use every day. Instead of asking someone to embrace an entirely new system, in-context AI makes AI adoption seamless. For example:

"Imagine a salesperson using their CRM tool to access an AI-powered assistant that suggests the best next steps for closing a deal. The system feels familiar, and the AI is simply an enhancement - not a replacement."

This approach allows employees to:

• Experiment with prompt engineering in a familiar environment.
• Build trust in the AI by seeing how it works in real-world contexts.
• Gain confidence when AI responses include references or links to their sources, improving transparency and reliability.

A First Step: AI Retrieval Agents

A great entry point for companies looking to leverage generative AI is retrieval agents. These agents automate the tedious task of searching for or researching specific information, delivering immediate productivity gains without disrupting existing workflows. As an example, consider a common banking scenario:

“Analysts manually review hundreds of pages from annual reports and financial statements to identify expiring hedging or derivatives contracts, that could be an opportunity to sell their risk management solutions. This time-consuming task involves cross-referencing complex documents to find actionable insights.”

By deploying an AI Retrieval Agent within their PDF application, the team can:

• Instantly extract key data points from reports.
• Save time by automating repetitive, labour-intensive tasks.
• Scale up their capacity to analyse more reports daily, increasing efficiency and productivity.

This small yet impactful change didn’t require the team to learn new tools. Instead, it enhanced their existing processes, delivering immediate value.

Evolution, Not Revolution: The Kaizen Approach to Automation

While the latest Large Language Models (LLMs) promise to automate tasks once thought of as impossible to automate, businesses need to proceed with caution. Trust and confidence in AI don’t happen overnight, especially in regulated industries where guardrails and audit trails are essential. Instead of jumping straight to fully autonomous AI agents, organisations can use a gradual, iterative approach - often referred to as the Kaizen method of continuous improvement – to incrementally augment automations with AI. This involves:

1. Starting Small: Use AI to enhance existing workflows or solve low-risk problems.
2. Adding Intelligence Incrementally: Gradually increase the level of autonomy given to the AI as trust and confidence grow.
3. Measuring Progress: Continuously monitor results to ensure productivity gains and identify areas for further optimization.

By evolving existing automation projects with AI, and gradually increasing the level of “agency” or self-direction allowed to the AI, companies can unlock incremental productivity gains without overhauling their operations.

AI Agents: The Natural Next Step

AI agents represent the next phase of automation industry’s evolution, building on the foundation of existing business process management (BPM) and robotic process automation (RPA) systems. Instead of deterministic workflows that follow rigid rules, AI agents add intelligence and self-direction, enabling more dynamic and efficient processes. The best part? Companies can start today. By integrating AI into existing automated workflows, businesses can:

• Convert automated processes into intelligent agents.
• Measure productivity improvements to prove the value of AI.
• Build a framework for creating and governing AI agents in the future.

Agency Specrum

To see how generative AI can boost the efficiency of existing automations in just a couple of clicks, see: Creating AI Agents with TotalAgility.

Conclusion: Start Small, Think Big

Generative AI isn’t just a passing trend - it’s reshaping the way businesses operate. But success doesn’t come from replacing what works with flashy new tools. Instead, it’s about evolving your existing workflows to unlock the full potential of AI, one step at a time. By starting with simple use cases like retrieval agents, embedding AI into familiar tools, and gradually increasing its autonomy, businesses can realise tangible productivity gains while building trust in this transformative technology. Ready to take the first step? Discover how to create AI agents with TotalAgility and see how generative AI can boost your business efficiency today.