Generative AI in 7 easy steps: A practical business guide

Rapid advances in generative AI present both opportunities and challenges for your business. 

Your emphasis should be on how AI can augment human capabilities rather than replace them. Despite apprehensions surrounding AI, focus on its advantages. 

At London Tech Week, Microsoft UK CEO Clare Barclay, said that 64% of workers don’t have the time and energy to do their jobs, according to the company’s Work Trends Index. In these hustle-focused times, it’s rare to see people unchallenged with burnout because the pace of work doesn’t keep up with our human ability to keep up.   

“We call this deluge of information digital debt,” she said. “It saps energy, slowing down the ability to think clearly, which severely impacts thinking for innovation.” 

“Business leaders feel it and recognize it in their employees. They are less interested in using AI to cut jobs and instead value helping employees be more productive and focus on more meaningful work. It can also provide well-being benefits.” 

Seeing AI as a co-pilot can significantly boost productivity—often by as much as 30 to 40%. It’s a prime opportunity to empower your employees with AI “superpowers” to help them succeed. 

Here are 7 practical steps outlining a pathway to navigate your generative AI journey. 

• Make customer needs your north star
• Use data to enhance customer experiences and address pain points
• Trust—build in a robust framework 
• Augment the creativity of your people 
• Understand the technical feasibility of generative AI 
• Use incremental deployment 
• Continuously learn and develop 

1. Make customer needs your north star 

Customers are the lifeblood of any successful business. Listening to your customers is crucial to stay ahead of the competition and drive growth. Customer feedback and preferences can provide key insights into how you can better tailor products or services to meet their needs with generative AI.  

Speaking at a London Tech Week panel, AI tech entrepreneur and board member Nathalie Gaveau said: “Always start with the customer in mind. Consider their needs as your north star. How does your customer experience compare with the best in the world?” 

Nathalie said businesses should understand metrics such as Net Promoter Score (NPS), which tracks customer satisfaction. NPS asks customers to rate the likelihood of recommending your product or service to others.  

NPS can help you analyze customer responses to gain a holistic understanding of their behavior, which can help you refine business strategies accordingly. 

By building generative AI models grounded in customer needs, you can steer your business towards increased customer satisfaction and loyalty, ultimately growing customer lifetime value. 

Nathalie added: “CEOs must understand which areas of their businesses are being disrupted by AI. Two areas ripe for AI integration are coding and customer service. I’ve seen substantial productivity improvements in companies that have piloted AI in these areas. 

“Engage your board and leadership team in discussions about investment in AI, considering your company’s maturity and capital allocation strategy.” 

2. Use data to enhance customer experiences and address pain points 

Encourage your team to make the most of data from various sources. Customer feedback, social media, and purchasing behavior will give deep insights into customer needs, preferences, and pain points. 

You want to incorporate those insights to create personalized generative AI experiences that delight and retain customers. Additionally, it could position you to address potential issues proactively, further fostering customer loyalty. 

Kate Janssen is the senior product manager for machine learning and chat at Cleo, speaking in the same panel as Nathalie at London Tech Week. She said: “Product managers aim to create solutions that address customer needs and serve business objectives.  

“We aren’t crafting AI solutions in search of problems, but rather treating generative AI as a tool to enhance customer experiences and solve problems more efficiently.” 

Kate said that instead of asking how you can use generative AI, you should be asking how to use it to make your customer journey smoother and more delightful.  

She advised: “If generative AI aids in achieving that, fantastic. If not, forcing it into the equation isn’t necessary.  

“Always let the customer problem guide your application of AI and not vice versa. It’s not about shoehorning AI into your product, but about using it to enhance your solution where it makes sense.” 

Kate also pointed out that understanding your customers’ perceptions of generative AI is key.  

“If your target audience harbors fears about AI’s data usage or the security of their proprietary information, then an educational component may enhance product acceptance.”

3. Trust—build in a robust framework 

You earn trust with customers, stakeholders, and partners through consistent actions and transparency. Embrace the responsibility of ensuring generative AI reliability and take proactive steps towards mitigating risk. 

Build a solid framework that instils trust and verifies the accuracy of AI-generated outcomes. Here are some tips on doing just that: 

• Start by evaluating existing AI processes and identifying potential vulnerabilities.  

• Take a proactive approach to understanding potential risks with generative AI, such as biased or incorrect outputs, and develop strategies to address them.  

• Implement robust data quality controls and validation processes to ensure the accuracy and reliability of the data used in AI models. 

• Review and update your framework to align with emerging best practices and industry standards. 

Kate Janssen said: “CEOs and leaders should seize the opportunity to provide guidance and context, rather than exercising control over AI applications.  

“These tools can significantly increase productivity, but their usage should align with your organization’s risk appetite and data privacy policies. 

“To ensure the right balance, it’s crucial for leaders to communicate what types of data employees can safely share or use in applications like ChatGPT.  

“Creating a safe space for dialogue is key. For example, leaders could establish regular meetings or dedicated digital channels where employees can clarify doubts about data usage. 

“In this environment, it’s important that leaders demonstrate their usage of AI applications for productivity gains, such as email drafting or content creation.  

“This hands-on approach will likely foster a more accepting and forward-thinking culture.” 

Involve legal experts if necessary 

Legal compliance is a critical aspect of trust and verification in generative AI. Engage legal experts to assess compliance requirements and mitigate potential legal pitfalls.  

Get the help you need to navigate the complex regulatory landscape and ensure your AI systems adhere to ethical standards and data protection laws.  

By involving legal experts from the outset, you can establish a strong foundation of compliance, building trust with customers, stakeholders, and regulatory bodies. 

Nathalie Gaveau said: “Agility often brings new risks. It’s essential to create a reliable framework that ensures no single point of failure, adds redundancy, and mitigates risks in your organization.  

“Compliance should be top of mind, and involving lawyers in risk assessment can be immensely beneficial. However, practicality is key because risk profiles are continually evolving.” 

AI advisor and startup investor Haibo E says that it is essential to remember that machine learning is a systems problem. 

She said: “If you decide to integrate machine learning into your customer propositions, you must examine your entire organization.  

“Consider everything from legal implications and data IP concerns to new tech stack requirements and the emerging field of machine learning ops. 

“Significant changes might be costly and will certainly require capital investment. But if you’re committed to taking this bold step, make it decisively and from the top.  

“It’s an exciting new era, and the benefits can be transformative with the right approach.” 

4. Augment the creativity of your people 

In the AI era, the roles and capabilities of your employees could transform.  

It’s important to recognize the unique strengths that AI brings to the table, such as generating insights and automating repetitive tasks.  

By understanding the augmenting power of generative AI, you can empower your employees to take on new responsibilities and focus on critical and creative tasks that require a human touch.  

Nathalie Gaveau believes that how you manage people in an era of augmented capabilities must evolve.  

She said: “Regardless of the system you’re using, understand that your employees are not just ordinary people, but ‘augmented’ individuals who can code, create videos, or perform tasks beyond their traditional roles.” 

She recommended implementing a bottom-up strategy: “Create a secure sandbox loaded with user-friendly, no-code applications.  

“Encourage teams across the organization to experiment with generative AI tools for tasks like intelligent document search and content generation. 

“You’ll be surprised at how quickly some teams will outpace others in leveraging these technologies, accelerating the transformation, ideating new products, and leading pilot projects.  

“This approach requires a robust framework and expert tech teams, but it can powerfully speed up your organization’s transformation.” 

Kate Janssen agreed: “Fostering a safe, sandbox-like environment with clear guardrails enables employees to interact with AI applications confidently, increasing productivity while ensuring data security.” 

Hire and retain skilled individuals 

To fully take advantage of the power of generative AI technologies, it’s crucial to identify and nurture talent capable of using it to its full potential.  

• Build a growth mindset within your business, where employees embrace the opportunities provided by AI and continuously develop new skills to stay relevant and thrive in this changing landscape. 

• Provide training and upskilling opportunities to equip your employees with the knowledge and skills to use generative AI effectively.  

In the competitive AI landscape, hiring and retaining skilled individuals is key to staying ahead.  

• Look for individuals with a passion for learning and a curiosity to explore new technologies, demonstrating technical expertise and a creative mindset.  

A supportive work environment rewards innovation, collaboration, and personal growth.  

• By having a team of skilled individuals who understand the potential of generative AI, you can drive innovation, enhance productivity, and maintain a competitive edge in your industry. 

5. Understand the technical feasibility of generative AI 

Embracing generative AI holds tremendous potential for your business, but it is crucial to understand the technical feasibility before diving in. Keep an open mindset and seek the support of experts and resources.

Let’s explore practical steps to assess the technical feasibility of generative AI and set your business up for success. 

Consider costs 

Integrating generative AI involves various costs to be mindful of. Understanding the finances allows you to plan your budget effectively and make informed decisions about resource allocation. 

• Begin by evaluating API charges, as they can vary depending on usage and customization 
• Research different providers to select one that aligns with your budget and offers the necessary features.  
• Consider hosting expenses, as AI models may require substantial computational power.  

Kate Janssen said: “When it comes to cost, it’s crucial to consider the potential expenses involved. There are charges associated with open models like OpenAI’s APIs, which you must factor into your company’s unit economics.  

“For startups and smaller companies, hosting these models can be quite costly, running into thousands of dollars monthly. Therefore, it’s important to consider your use case and the business viability of AI integration. 

“If AI is at the heart of your product, you must carefully calculate how these costs will affect your margins. Concessions will be inevitable.” 

Evaluate business viability 

When assessing the technical feasibility of generative AI, it’s essential to evaluate its business viability based on unit economics and margins.  

Evaluating potential benefits against the costs incurred will give you a clearer picture of AI’s value to your business. AI integration must align with your long-term business goals. 

Conduct a thorough analysis of your financials, considering factors such as projected revenue growth, return on investment, and market demand. 

• How will generative AI impact your workflow and processes?  
• Will generative AI enhance operational efficiency, streamline decision-making, or improve customer experiences?  

If you’re interested in building a generative AI-enabled product, Kate Janssen advises: “It’s essential to make it defendable in the marketplace.  

“Consider what makes your product unique and compelling—is it the algorithms, distribution strategy, or data? Remember, your competitors have access to the same tools, so how will you differentiate your product and maximize its value?” 

Address latency challenges 

Latency, or delays in AI processing, can impact the user experience and undermine the benefits of generative AI.  

It’s crucial to address potential challenges related to latency to maintain a seamless user experience. 

• Evaluate your infrastructure and determine if your current systems can handle the increased computational demands of AI. 
• Consider implementing load balancing or distributed computing strategies to improve response times.  

• In cases where real-time AI processing is critical, explore edge computing solutions that allow you to deploy AI models closer to end users.  

By proactively addressing latency challenges, you can ensure that your generative AI applications deliver the desired outcomes without compromising user satisfaction. 

Kate said: “If you’ve used AI systems before, you’ve likely experienced a time when the service was unavailable or response times were slow.  

“When considering the use of an API, you need to consider the potential for slow response times or periods of unavailability.  

“What does that mean for your customer experience? How will you maintain their engagement during these times?  

“User interface design can greatly enhance the user experience during these latency periods, but developing a plan to address these inevitable challenges is vital.” 

Highlight the need for redundancy and continuous monitoring 

Redundancy and continuous monitoring are key to ensuring reliability in generative AI systems.  

Build redundancies within your infrastructure to avoid single points of failure. Have multiple AI models running simultaneously or implement fail-safe mechanisms.  

Consider establishing robust monitoring processes to detect anomalies and errors in real time. Continuous monitoring allows you to identify and address issues promptly, minimizing potential risks and instilling confidence in the reliability of your AI systems. 

6. Use incremental deployment: 

When it comes to integrating generative AI into your business, taking an incremental approach can lead to greater success and minimize potential risks.  

Following the “crawl, walk, run approach”, incremental deployment empowers you to harness the potential of generative AI while minimizing risks.  

Crawl: Rapid prototyping and testing 

The crawl phase is the first step in your journey towards generative AI integration. This phase involves rapid prototyping and testing to validate ideas and gather feedback from alpha and beta user groups.  

This approach allows you to understand the capabilities and limitations of generative AI within a controlled environment and sets the stage for future growth. 

• Start by identifying specific areas or use cases where generative AI can bring value to your business. 
• Develop a minimum viable product (MVP) that showcases the potential benefits of the technology.  

The next stage is gathering feedback from a select group of users and incorporating their insights to refine and iterate on your prototype.  

Walk: Iterative development and scaling 

Once you’ve refined and validated your initial prototype, you move on to the walk phase.  

This approach allows you to build confidence in the capabilities of generative AI while delivering value to your users. 

 This phase focuses on iterative development and scaling your generative AI solution.  

• Expand your user base beyond the alpha and beta groups, gradually increasing the scope and complexity of your implementation.  
• Continuously gather user feedback and use it to drive further enhancements and optimizations. 
• As you iterate on your generative AI solution, ensure transparency by documenting the changes, communicating updates to stakeholders, and addressing any concerns. 

Run: Full-scale deployment and continuous improvement 

With a solid foundation established through the crawl and walk phases, it’s time to accelerate towards full-scale deployment. During this run phase, you will expand the adoption of generative AI across your organization or user base.  

• Monitor key performance indicators to measure the impact and effectiveness of your implementation.  
• Regularly evaluate the fairness and accountability of your generative AI system to ensure ethical practices.  
• Implement mechanisms for continuous improvement, leveraging user feedback and insights to refine your solution over time.  

• Maintain a commitment to transparency, fairness, and accountability to build trust and confidence in the benefits and outcomes of generative AI. 

Staying at the cutting edge is critical because what makes you competitive may be easily replicated. But that fear should keep you on your toes and drive innovation. 

Yuelin Lee is the chief product officer at the identification verification company Onfido. At London Tech Week, she said: “While the advent of these new AI tools initially excited us and led us to plan a hackathon, we had to reassess. 

“While exploring non-critical applications of these tools today is important, a more cautious approach is required when embedding them into core products. We need to set up proper gateways and systems before leaping. 

“Our approach mirrors the ‘crawl, walk, run’ mantra. We’re already running regarding our core products but still learning to crawl or walk in other areas. We prioritize caution and structure, not haste, as we progress with generative AI.” 

7. Continuously learn and develop 

Continuous learning and innovation are the fuel that powers success. Developing a growth mindset and embracing the opportunities of staying at the cutting edge is crucial.  

Spark innovation with novel applications of generative AI 

Generative AI holds immense potential to revolutionize industries and unlock novel applications.  

Encourage a culture of innovation by exploring how generative AI can positively impact your business.  

Seek fresh perspectives and ideas from your team and empower them to experiment and think outside the box.  

By embracing the power of generative AI to solve problems, improve processes, and enhance customer experiences, you can drive innovation and set yourself apart from the competition. 

Cultivate agility and adaptability for success 

In a rapidly changing world, agility and adaptability are key attributes for success. Foster a culture that embraces change and encourages flexibility.  

Stay attuned to emerging technologies and emerging trends that have the potential to disrupt your industry. Be open to embracing new tools, methodologies, and ways of working.  

Encourage your team to be nimble in responding to challenges and to view them as opportunities for growth and improvement.  

Yuelin Lee says of cultivating a growth mindset: “Being open to change and adaptation becomes crucial. 

“In practice, this means empowering your teams. Top-down directives about what to do may not always be the most effective approach. Instead, giving your team the space, time, energy, and desired outcomes can spur innovation.  

“The best ideas often arise not from top-down mandates but from empowered teams—experimenting, playing around, learning from peers and customers. 

“Rather than being driven by fear and feeling the need to act because something is changing or appears threatening, adopt a mindset of exploration.  

“Acknowledge the change, create guardrails for safety, and allow room for innovation and experimentation. This approach manages the risks involved and fosters a culture of innovation and creativity.” 

Final thoughts on practical ways to use generative AI 

First and foremost, always prioritize customer needs as your guiding star. By understanding their pain points and leveraging data to enhance their experiences, you can deliver personalized solutions that truly resonate.  

Building trust through a robust framework ensures the responsible and ethical use of generative AI, fostering customer and employee confidence. 

Furthermore, don’t overlook the invaluable human touch. Augmenting the creativity of your people with generative AI tools can lead to innovative breakthroughs and new avenues for growth. However, it’s crucial also to understand the technical feasibility of adopting AI solutions, ensuring they align with your business goals and resources. 

Embracing incremental deployment allows for a smoother integration of generative AI into your existing processes, mitigating risks and facilitating a seamless transition. 

Finally, remember that the learning journey doesn’t end with implementation. Continuously learning and developing your AI capabilities will empower your organization to adapt to emerging trends and stay at the forefront of innovation. 

By embracing these practical steps, you will unlock the full potential of generative AI, driving efficiency, creativity, and customer satisfaction.