It’s a great time to do business in the U.K. Many likely have heard that the Bank of England will be cutting interest rates, wages are growing faster than inflation, and companies are taking advantage of the situation. EMEA Oracle NetSuite SVP Nicky Tozer and COO Ham Patel took the stage at SaaStr Europa to talk about real-world examples of how traditional and Generative AI can help supercharge your growth.

We’re at a time when everyone is trying to do more with less. It’s not growth at all costs any more, so how do we make people, processes, and technology more efficient while still achieving growth?

AI is part of that answer. Your data is the other part. AI can drive profitability, productivity, and ROI, but to supercharge growth, you need fuel. That fuel is your data. Let’s discover how you can unlock the value of your data with the help of AI.

The AI Opportunity

By 2027, 29% of organizational spend will be on AI. That’s about $150M with a $17T impact on the global economy. 60% of CEOs expect AI to drive product and service quality, and 75% of that value will come from use cases across four different areas.

  1. Operations
  2. Sales and marketing
  3. R&D
  4. Product development

If you can use AI, you can compete faster, better, and with fewer resources.

Goldman Sachs did a survey last year that said GenAI would drive productivity by 1.5% over adoptions of about ten years, so there’s real value in investing time and money into applications that help you achieve this.

Traditional AI vs. Generative AI

Everyone has varying knowledge of AI and what it can do, but everyone is looking at it and trying to understand the value it can bring to their businesses. A classic example of AI is IBM’s Deep Blue, defeating the world chess champion in 1997. That’s traditional AI, where it looks at a task a human can do, identifies data patterns, works out what might happen next, and makes suggestions on it.

However, generative AI is the newest part of AI that can create something for you using natural language processing. It might create documents, images, or text based on the data you provide.

Using GenAI, you can generate code and documents. You can summarize or extract from millions of data that a human couldn’t interpret, and without changing the original message.

But when it comes to reasoning and acting on that reason, AI, to an extent, can have a multi-turn conversation and an iterative discussion to come up with an outcome. Yet, it’s not a human being. It’s not advisable right now to use AI for consulting.

Sales and Product Development Perspectives

A typical use of an LLM relates to five areas when thinking about that 75% of value:

  1. Customer operations
  2. Marketing
  3. Sales
  4. Product development
  5. Strategy and Finance

From a sales perspective, you might call this engineered selling when you’re generating pipeline in an automated way and figuring out how to interact with those people and sell to them. By looking at your data model, you can determine which kinds of companies to sell to and have AI produce that for you. You can have AI create material based on industry, complete with generated sales pitches.

It’s an end-to-end engineered selling-type process where you’re generating pipeline, acting on that pipeline, and taking it through to a sales pitch. That’s just one example of what people are doing with GenAI.

The other piece is product development. If you’re developing a new product or expanding an existing one, you can use large volumes of data that might include user feedback and market trends to have AI create an analysis for you of why you need to develop this particular product.

Once you’ve done that, you can have AI build the code. You don’t need to replace the people who are coding. Instead, you can expand on and accelerate what they’re doing to create more products. You can also speed up the delivery of those products and have AI test them.

Real-World Examples of Traditional and Generative AI

When it comes to value creation, there’s a great deal of promise and opportunity, which is why everyone’s talking about it. GenAI is just the tip of the iceberg if organizations want to create value now. To get a real return on investment, you shouldn’t overlook traditional AI and the many ways you can use it today.

Traditional AI: Document Understanding

A good example of traditional AI in action is document understanding. You may receive invoices in back office systems. They’re either scanned images or emails. These can be accessed via traditional AI using object document detection, which inputs invoice numbers, purchase orders, item quantities, and other details you might need for downstream processing of that invoice.

This is being used today by accounts payable departments, allowing these teams to get through a lot more volume than they could in the past.

Generative AI: Generating Item Descriptions

Let’s look at a retailer as an example. In a commerce department, one of the things they may do regularly is add products. Those products require product descriptions, and you can use GenAI to support the generation of those descriptions.

A user would go to an AI-powered commerce software and enter some details. For example, they might want to add black leather sandals with rubber soles that are suitable for summer and have some level of comfort. The LLM can generate a more complete description by putting it into a system with GenAI.

GenAI can take on your brand’s tone and style and provide it in a format your customers are more likely to respond to based on public and private data.

Traditional and GenAI: Financial Forecasting

Finance departments may use planning and budgeting software, often AI-powered with traditional AI. What these tools typically do is analyze the plans for costs and variances. Then, the predictive algorithms constantly surface the information you need to know, and wouldn’t be able to get through if you were an analyst looking through all of this information on your own.

Traditional AI can take large volumes of financial data, surface the information you need to know about, and quickly act upon it. This saves people time on analysis.

You can take this a step further with GenAI. Imagine you need a narrative created for financial reports. GenAI can do that for you by looking at the data in your planning and budgeting systems. It can provide this on a quarter-by-quarter basis, so it’s learning all of your historical information to deliver value to someone who does this as their day job.

These tools are here now and can provide value to any finance organization.

The Best AI Starts With The Best Data

Data is critical for your success with AI. The data tells the story of your business. AI allows you to make that story clearer, more useful, and easier to interpret for the people in your organization. AI can only produce results based on the data it’s given, and the best data is your data.

If you want to drive real results within your business, you need to use your business data. AI can make your data work for you instead of having a specialist team of people doing analysis for you and delivering that information too late to your business.

How do you unlock the value of your data and AI?

  1. Build a strategy for AI. If you don’t have a strategy, build one. If you already have one, develop it and make it with short-term deliverables and a long-term vision. That 1.5% increase may come over a ten-year broad use of AI, so don’t limit yourself to what happens in the short term.
  2. Prepare your data for broader GenAI use. Rubbish data in means rubbish data out. AI relies on good data that is structured correctly, standardized, and documented to enable you to get the right output.
  3. Don’t start from scratch. Depending on the size of your organization, you may or may not have lots of data. Your data is good data, and you can combine it with public data to make it even better, but don’t only use public data. You can also buy applications that collect data and use AI to suit your business. If you do that, you can accelerate your AI strategy and deliverables with value immediately.

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