Leap ahead with applied AI

Happy 29 February! Do you consider yourself innovative and work in sales or operations? Do you hear a lot applied AI but don't really understand how to get started? Could you tell the difference between predictive analytics and generative AI? Is investment in your IT team and / or data team getting you successful outcomes?

Lots of questions - but first one is what really matters. The other problems are solved by working with people that have built value propositions, business cases, project plans, data maps, AI designs and then developed and delivered successful applied AI solutions.

AI is in the news every day. It is a popular topic and means a lot of different things to different people.

It is easy to get caught up in hype.

• Company X is doing Y with AI and we are still using Excel.
• My job will be done by robots in X years.
• We can't use AI because it is dangerous and re-enforces bias (or anti-bias).
• We will all be slaves to a super-intelligence.

The reality is that huge computing power and data availability have unleashed the power to understand data. Not just data itself, but meta-data and relationships between data (graphs). This means that:

• Information can be retrieved, organised and presented much more effectively (eg ChatGPT and the Large Language Models (LLM's) behind them).
• Forecasts and predictions are much more accurate as there are more data points to inform decisions.
• Outlier detection is much easier as errant data points and their dependencies and impact can be calculated much faster, much easier and be presented in real-time using interactive dashboards.
• Classification and identification of anomalies in data tables, images and text is also much easier.
• Processes can be optimised much easier and people focused on "edge cases", (ie difficult decisions) rather than mundane checking.
• Analysts and decision makers spend less time to decision and make better decisions using data supported decision frameworks.

So where to start? Applied AI is really routed in business transformation. If an organisation or even a process is not willing to change it's ways of working then even the most value adding applied AI will not succeed. The most important thing is to start with an "innovation outlook" and be prepared to map out a brave "future state" or new way of working. Determine how you currently do things and then look for a subset of the challenge to prove out how applied AI and new ways of working will deliver for you. There is no need to change IT systems or processes at the outset as all development and piloting can be done in a "sandbox" or development environment. The design of an applied AI project, the business case and an early working model is the most important piece. Or put another way, What I am trying to do, why (early ideas of the value opportunity) and how can it be done in the shortest possible time to deliver credibility and acceptance that this can make a positive impact. You should discuss this with applied AI experts. A small upfront investment will save you significant time and money down the path.

We often hear - can I buy AI off the shelf? The short answer is no. And the real question should be - what problem am I trying to solve? Picking a technology and then thinking about a problem to solve is the wrong way around. I have heard the argument that - some products have lots of AI, so you can technically buy it off the shelf. Again, you are buying a product that is enabled by AI, you are not buying the AI per se.

Should you care about predictive analytics vs generative AI? Not really. They are used for different things. Predictive analytics is to help you make better forecasts / classifications and therefore decisions that are optimised for these outcomes. Generative AI takes a probabilistic word, sentence and paragraph structure approach to answering a question based on training huge power hungry algorithms on millions of documents. This means that it is able to answer questions very well based on what it has seen and adaptations to responses based on what it is told is a good solution. They are both very, very useful and are invaluable in applied AI along with other models and approaches.

Is your IT or data team spending the budget in the most effective way? If they:

• Have experience in applied AI and have already delivered successful solutions
• Have a business case and can demonstrate return on investment
• Use development sandboxes with an agile iterative approach and "deliver minimum viable product"
• Have a good knowledge of changes in application development, innovation in data engineering / processes and innovation in data science
• Work closely with "product owners" and "change leaders" in the business

Then the answer is probably yes. Unfortunately many organisations do not benefit from this experience and approach in-house.

So, ready to leap ahead with applied AI. Speak with us at Algospark.