Ciesco Commentary 15 April 2024

“Trends in 2024 in Artificial Intelligence” – Ciesco Senior Advisor, Daniel Singer


Daniel Singer, Ciesco Senior Advisor specialising in data, digital research & AI, shares his thoughts on key trends and dynamics across the Digital data and Artificial Intelligence space. Earlier this year, Daniel received overwhelmingly positive feedback for the unique perspective he brought to the table in his presentation on AI at the Ciesco event, and this article builds on that perspective, exploring the evolving landscape of AI-powered solutions, determining value hinges on whether the technology enables new revenue streams or boosts profitability.

“Trends in 2024 in Artificial Intelligence”, by Daniel Singer, Ciesco


If there is one safe prediction for 2024, it is that ‘AI Powered’ will appear in nearly every piece of sales collateral between now and 2025. How can we make sense of all these ‘AI’ solutions, and which ones are worth our time?

Rather than trying to define ‘AI’ in general, this piece hopes to share some guidelines for valuing the role of AI in the fast-moving data and analytics space. Let’s start with two questions that can often be helpful to ask of any new technology:

  • Does it let you to do something new? Perhaps it enables a new service, or it lowers barriers to entry against incumbents. If so, can the technology provide you with new revenues?
  • Does it lower the cost of what you do? Maybe it lets you automate and reduce your hiring or do more work with existing resources. If so, does the technology make you more profitable?

AI and Data Analytics can do both of the above, but it’s critical not to confuse them. When assessing a new data analytics solution, investors and business leaders need a clear idea of what they are looking to achieve as an outcome– increase in revenue or improvement in profitability.



If everyone has an ‘AI Powered’ solution, then winning new opportunities would require validation of your solution being better than competitors. A ‘better model’ is hard to prove in practice as few clients run multi-vendor comparisons, and even the best model doesn’t help if your data is poor and your benefits vague.

When it comes to assessing an ‘AI powered’ revenue opportunity there are two criteria that can be useful:

  • Unique Data – Does your solution use unique (or close enough) high quality data? If the data is easy to acquire (i.e. most market research) then the AI is easily replicated by competitors. But, if the data is unique then it could create a new opportunity (for example, large scale search algorithms).
  • Repeatable outcomes linked to business goals – Does your solution deliver useful and reliable recommendations? If the solution is based on rigorous, well verified results checked by a robust peer review system (i.e. pharmaceuticals, chemistry, etc.) then AI can provide recommendations based on centuries of human knowledge. Alternatively, if the solution is based on modelled data from patchy sources (i.e. internal business processes or ad platforms) and it will then be used to change complex human behaviour, this may lead to results that are less reliable and likely less valuable too.

This does not mean that a business without unique or proven data is bad. They could be a great business in a complex sector, but their AI solutions are more likely to be undifferentiated, hard to link to provable benefits, and difficult to scale as they lack repeatable data sources and objectives.



When using AI to improve profitability, there are two things to consider. First, how do you get an organisation to change working practices (a whole other topic). Second, how do you convince customers that you should retain the profit despite your now reduced costs. This is especially true in sectors where AI lowers barriers to entry, giving new entrants a chance to disrupt the sector.

Again, there are two questions that can be helpful:

  • Pricing power – Do you set prices? This could be because of a unique advantage (i.e. unique data), or because your prices are already the lowest in the industry. In this case, AI will allow you to automate and retain the profit as clients and customers can’t replace you.
  • A plan to invest if you can’t set price – Is there a plan to use any short-term profitability gains before cheaper competitors disrupt you? Investing short-lived profits to strengthen your unique capabilities is more likely to offer long-term success than hoping to retain a higher margin when the market inevitably catches up.

If the answer to both of the above is ‘no’ then there is a strong risk that your AI efficiency gains will be competed away as clients ask for savings that competitors will be only too happy to oblige.



As with any new technology, AI and Data Analytics offer transformative advantages but AI is also subject to the usual rules of business. So, when the next AI sales pitch comes your way, it never hurts to ask: Does it let you make more money in a way that is genuinely different from your competitors, or will it improve your profit despite AI becoming part of the ever-changing rhythms of business for your clients and competitors?



If everyone has an AI powered data-analytics solution then is your version as special as you think it is, or is it just the price of doing business today?


Daniel is a Senior Advisor at Ciesco, helping clients to optimise their digital strategy and online marketing in the Technology and Media Sectors.

He loves to help organisations see around corners. He’s been a data practitioner, a regulator, and the CEO of high growth data business that he helped built. Daniel’s expertise helps organisations make better use of data both from the inside, having managed a large team of highly skilled data scientists, and from the outside as an experienced consultant.


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