By Chet Kapoor, CEO, DataStax, and Prasad Setty, DataStax advisor and Stanford Graduate School of Business lecturer.
“Don’t waste an ounce of your people’s talent or a minute of their time.”
This statement beautifully captures what’s possible when we use AI at work. Many people fear AI replacing jobs, but the reality is that AI can amplify human capabilities and make organizations 10x more productive. What’s more, the next generation of talent will enter the workforce with vastly different expectations and skills. If employers want to attract and retain top talent, they will need to give people a place to thrive. Successful companies in the age of AI will not only build AI products, but they’ll act like AI companies.
If you were to build an organization from scratch today, how would you integrate AI? And if you’re already established, where should you begin your AI journey? Our perspective is simple: people and processes are key.
How AI solves two problems in every company
Every company, from “two people in a garage” startups to SMBs to large enterprises, faces two key challenges when it comes to their people and processes: thought scarcity and time scarcity. AI not only solves both of these pervasive problems, but it opens up a whole new world of possibilities.
1. Thought scarcity: Innovation thrives on thought (ideation, creativity, and critical thinking) and thought-sharing (dialogue, curiosity, and collaboration). But our days are so frequently filled with “shallow work” – going from one task to the next, one email to the next, and so on – that there’s minimal time to engage in deep work, where we have the freedom to flex our creative muscles, brainstorm, and ideate. “Flow state,” being fully immersed in a single important activity, is more of a concept than a practice.
AI changes the game. Its ability to automate routine processes and provide data-driven insights helps create a conducive environment for deep work. And because generative AI (genAI) is interactive and dialogue-based, it can help you get into a state of flow. It might not be the best source of knowledge due to the potential for hallucinations, but more than being a knowledge engine, it helps us reason and inspires more critical and deeper thought.
2. Time scarcity: Time is a precious resource, and inefficient workflows will drain productivity. Most people agree that meetings can be a big time-suck. As we touched on above, manual and repetitive operational tasks take up much of our valuable time.
Using AI allows us not to just “get a few minutes back,” but to free up hours every week. If everyone in an organization could apply AI to an hours-long process and do it in mere minutes – what more can you achieve?
How to implement AI effectively
We can talk about possibilities all day, but it means nothing without action. Very few companies are actually implementing AI at work. Progress is stagnated by concerns about privacy, algorithmic bias, and compliance. But in six months, it will be too late. We’ve thought a lot about this and discussed success patterns with some “positive deviants” who are putting it into practice. Here’s what we’ve learned.
Someone has to lead it: Who has the most influence in a company? Its leaders. To succeed with AI in the workplace, a CEO’s strong belief and mandate to use it is necessary – but this isn’t sufficient on its own. All your leaders should not only champion AI, but also walk the talk.
We’ve also seen success with putting together an AI “SWAT team” or “task force.” This is a dedicated team whose main purpose is to drive AI implementation and integration within your organization. If the C-suite’s role is to lead by influence, the SWAT team’s role is to lead by execution.
Experimentation drives momentum: How do we maximize the value of a given technology? Via experimentation. People should be consistently exploring applications of AI across different functions. A couple of things need to happen for experimentation to drive momentum:
Employees need access to the tools they want to try, so organizations must have a quick approval process in place to ensure their safety and security
Organizations should provide a place to share experiences, learnings, and feedback for everyone to see. This can be as simple as a Google Sheet or sharing examples at weekly all-hands meetings
Many enterprises do “blameless postmortems” to encourage experimentation without fear of making mistakes and reprisal. It’s like “fail fast” for genAI projects.
Some of the GenAI tools used internally at DataStax
DataStax
A programmatic approach: Ultimately, putting a program in place will be your best way of seeing real outcomes with AI. Here’s an approach your AI SWAT team or task force can follow for effective implementation:
Start with incremental use cases by function or workflow.
Example: Automate content creation in marketing using AI tools and voice models, or automate legal processes like contract reviews with AI-powered chatbot.
Identify mission-critical business processes and select a small segment of the process to begin with. (Note: Tackling mission-critical processes will require a cross-functional team of technologists and business experts, and you’ll have to give them the time and resources to innovate. The good news is once you demonstrate success in one area, scalability follows naturally … and you’ll see the impact on your bottom line in a big way.)
Example: Optimizing the production line efficiency and quality control processes in a manufacturing company would be mission critical. This company might begin by optimizing the quality control process for a specific product line. Once they see success, they can scale up and expand to other product lines.
After incorporating AI into mission-critical processes, you can start working toward custom app development.
Example: Creating a tailored customer relationship management (CRM) application.
Case study: esynergy
esynergy, a consultancy that builds AI solutions for clients (and a DataStax customer), has incorporated genAI into several internal functions. The benefits are particularly impactful for its sales team, said Prasad Prabhakaran, Head of Artificial Intelligence at the company.
Using esynergy’s in-house built genAI application, salespeople can use semantic search to quickly find relevant information among thousands of internal proposals and documents. The insights provided by the assistant help reduce the manual workload, support better sales strategies, and drive faster decision-making.
But esynergy faced a few challenges. “Seamlessly incorporating AI into existing systems can be complex, and ensuring compliance with data privacy regulations is crucial,” said Prabhakaran.
He offered three pieces of advice for those looking to implement AI in their organizations:
Pilot testing: Begin with a small-scale pilot to evaluate how genAI fits into workflows.
Training: Provide comprehensive training to ensure the effective use of the AI tool.
Ensure accurate and relevant responses: Adoption can be improved by ensuring a less hallucinated response with effective retrieval augmented generation (RAG).
The future is now
With AI, humans can focus on what we do best: think, connect, and create. As today’s students embrace AI tools in their education and day-to-day lives, their entry into the workforce will bring a paradigm shift. Agile and forward-thinking startups are already redefining job roles by leveraging AI. To get top talent and keep them, organizations have to believe in AI and embrace it fully — not just as a technology, but as a mindset that makes humans better and more productive. The winners and leaders will be those who see AI as a catalyst for growth and success.
Our CTA: Be the “positive deviants” and shape the future with AI.
Learn how DataStax enables production-ready GenAI applications.
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