Are You AI Ready? Build Your Framework for Success

At Ipsos Jarmany, we’re conscious of all the media hype around artificial intelligence (AI), and how the discourse has been mixed, to say the least. The idea that general-purpose AI will be the biggest event in human history may feel like hyperbole, and it’s probably too early to call. But what’s certain is that it’s going to change all our lives and is already transforming business.

In this blog, we want to touch on the opportunity that AI presents organisations, but more importantly we’ll get into what you need to do to make sure your business can leverage AI to the max.

What is the business opportunity of AI?

Just so we understand what we’re talking about here—AI will have made the world $15.7 trillion richer by 2030[1]. It will also have given a 26%-plus boost in GDP for local economies by the same date.[2]

Those figures may actually be conservative bearing in mind how quickly AI and its adoption is advancing, but regardless of how many trillions-of-dollars AI generates, there’s plenty for business to get excited about. Indeed, McKinsey found that way back in 2021, 27% of the companies it spoke to in an AI-related survey said 5% or more of their profits were already down to AI.

The difference between generative and non-generative AI

So what do we mean by AI? There are actually two kinds—Generative AI which produces new content, like chatbot responses, that imitate human creativity. And non-Generative, or predictive, AI forecasts outcomes based on patterns in historical data.

It’s generative AI and ChatGPT from OpenAI in particular that’s been grabbing all the headlines recently, which is unsurprising since Microsoft pumped a massive $10 billion into the continued development of this natural language processing tool back in January.

In practice, Generative will work alongside non-Generative, and in unison at times to enhance outcomes. Right now, these two types of AI are revolutionising businesses, from sales & marketing departments, to logistics and inventory, accounting & finance and human resources.

Whether it’s boosting efficiency by removing repetitive tasks like writing emails or summarizing large documents; or improving supply chains by showing how much of anything should be stored where and when, AI is there to give your business an edge.

How difficult is it to use AI in a business?

You won’t be surprised to learn that successful adoption of AI depends on how much effort you put in beforehand. There are plenty of problems to making AI work for a company—but for every issue there is a solution and we’re going to walk you through the key ones now.

We recommend establishing an AI Framework for Success. Make it a mental checklist that you go through and learn and share with colleagues so everyone interested in making AI a success is aligned. Remember AI adoption is a team game and you don’t want anyone from across the company going off-piste.

The Ipsos Jarmany AI Framework for Success

We’re going to split the framework broadly in two. There are the structural parts that you have to get right, covering data, architectures, legal requirements and skillsets for example. Plus, there are the softer parts, which cover things like sensitivities and ethics.

AI Framework for Success—1st Phase:

Time to make sure you have the correct foundation for AI:

What’s your AI mission statement?

Sounds obvious, but you’d be shocked by the number of companies we’ve come across that launch into AI without a clear vision of the revolution’s ultimate goals. Get together, agree and write down what you want AI to achieve for the business. Decide what you want the main benefits to be—enhance user experience, improve topline revenue or reduce internal costs?

Check your data quality

You need to audit your current data sources to ensure you have enough data and that it’s in the right place, clean enough and essentially fit-for-purpose. It’s worth spending a moment on this because you also need to consider how accessible your data is. Your systems-data needs to be able to flow freely in order for AI to work. The last thing you need are data siloes.

Do you have enough performance?

Along with your data, you need to audit your infrastructure to find out whether you have the basic computing capabilities to process large amounts of data for AI. Sure, the availability of AI services on public clouds like Azure offering massive amounts of compute and storage can help you here but see what you have in-house before you take that step.

Who is on the AI team?

We all know how labour shortages are hurting IT at the moment, so you need to count the number of hands you have available for your AI taskforce. If you’re short, then we recommend training for those who want to join up and, more for the longer term, think about bringing in AI specialists.

AI Framework for Success—2nd Phase:

You’ve put a check against everything structural, so now it’s time to move into the second, softer phase, which is just as important.

Data governance, ethics and bias?

Governance is going to need some thought because to train AI algorithms, for example, you need large quantities of data, making storage and security of major importance.

Racial and gender biases are also a known problem with AI unless work is done to iron out discriminatory assumptions in algorithms, often associated with low-quality data. Set down standards that will help control the problem, and check out the UK Government’s white paper on its approach to AI regulation and the EU’s AI Act for guidance.

Deal with employee concerns

Your personnel will have legitimate worries over how AI is going to impact them. The question over whether they will they lose their jobs is the elephant in the room that you’ll need to address first and foremost. You need to correct many of the negative assumptions about AI and communicate the benefits, reinforcing that it will enable them to focus on other, less mundane, repetitive and manual tasks, freeing them up to work on more interesting stuff.

Walk before you run

Everyone comes to AI nowadays with preconceived ideas—and it’s most likely that internal stakeholders will have massive expectations for AI in general. Afterall, they read the news, right? While it’s great to have high-level interest in a project, you have to manage people’s expectations at the start.

Therefore, consider a proof of concept to test that your AI model is working before going big. Use just a small sample of data to demonstrate the model’s effectiveness to the people that really matter before launching anything wide scale across the business.

Summing Up

With so much excitement around AI—and its transformative power for business—we could forgive anyone for not wanting to hold things up with questions like—Are we AI ready?; because quite frankly that’s incredibly boring, and who wants to be a killjoy?

But asking that question and following a framework like the one we’ve shared is incredibly rewarding in the long term and is the best way to get the most out of your AI investment.

Still, even with your AI Framework to Success, the time and expertise needed to get everything lined up can be a challenge; and so, at Ipsos Jarmany, we’ve created a team of AI specialists that can deliver AI in the most time effective and cost-efficient way possible.

If this blog has trigged some questions, thoughts or ideas, speak to us today and let us see how we can get your business on the path to a best-practice adoption of AI.