12 AI predictions for the enterprise in 2023 – Dataiku

By Dataiku
Gregory Herbert
With 2023 likely to be a huge year for AI, experts from AI platform provider Dataiku deliver their enterprise AI and ML trends for the year ahead

From companies needing to reassess the value of their AI initiatives thanks to the economic volatility, and continuing to invest in AI ethical teams, to the introduction of new regulations, including the upcoming EU AI Act – Everyday AI provider Dataiku provides its predictions in AI and ML for the year ahead.


2023 will be a year of acceleration for the operationalisation of widespread usage of analytics and ML in all functions of enterprises

Clément Stenac, Co-founder and CTO of Dataiku

For years, early adopters have already been building out systems to automate a host of mundane tasks and to focus on higher-value activities. This has included everything from financial reporting to data cleansing and document parsing.

They have also combined automation with traditional analytics and AI or ML activities. The benefits can be significant, with companies reporting greater efficiencies and improved quality control, with time to focus on developing the next great ideas and products. Moving on to more profound work also delivers a higher sense of accomplishment: it makes people feel that their job has more value and sense.

All of this together creates a strong incentive for more conservative companies to heavily invest in these practices, which are more often than not accelerated by employees eager for more automation, more analytics, and more insight. When it’s grassroots-driven like this, you get buy-in from across the organisation. The success of these initiatives relies on appropriate tooling and standard processes (MLOps, data ops, sometimes called XOps) in order to disseminate such power across organisations, while retaining appropriate controls and governance.

Clement Stenac


Organisations will have to adapt to European regulations, including the upcoming EU AI Act

Gregory Herbert, Senior Vice President and General Manager EMEA

These regulations will soon apply fines of 6% of company revenue in the event of incidents on high-risk algorithms. To be one step ahead, organisations must invest in a data quality infrastructure via a sound data governance strategy. Investing in an end-to-end platform provides advantages such as cost savings, smoother governance and monitoring, and the ability to focus on implementing high-impact technologies.


Companies will think twice about their processes and associated costs

Gregory Herbert, GM, EMEA

Moving data around, for example, is an expensive process. Companies will make increasingly rational choices about the best infrastructure for a given task, rather than chasing the latest new thing. In certain contexts, where the existing stack is still providing the needed functionality, it can be wise to delay the costs of migrating by a year or two. That said, this should not delay the development of new analytics and machine learning capabilities, which in many cases can be done on this so-called ‘legacy’ infrastructure. We must remember it is not the data infrastructure that provides value to the company, but rather the analytics and models that are put to use by the business units. 


Machine Learning Operations (MLOps) will be increasingly practiced in the AI / ML space, and for good reason

Gregory Herbert, GM, EMEA

Time and time again we hear that a core reason that organizations struggle to generate and maintain business value from AI projects is a lack of operationalization and associated business impact. While the exact MLOps practice will look different for each organisation, one key component of all MLOps strategies is the ability to properly monitor models post-deployment and make subsequent model adjustments as needed. This matters for businesses: as data or business requirements change, models in production will need to be updated and tweaked accordingly to ensure that advanced analytics efforts continue driving business outcomes.


Businesses will re-assess their AI business use cases

Shaun McGirr, EMEA RVP of AI Strategy

As economic volatility makes companies reassess the value of their AI initiatives, and the enterprise platforms supporting them, we’ll see companies look to experts to focus on building realistic, lasting, and scalable impact. Any economic downturn may trim data teams, and, as with any technology, AI has to become a productivity multiplier quickly to prove its value. The best way to do that is to choose not to do certain things. AI-driven survival and success may come down to ensuring you don’t select the wrong use cases, as doing so might break stakeholder support for AI in your organisation.


Companies will look to AI to help them do more with less

Shaun McGirr, EMEA RVP, AI Strategy

Some companies see AI as a tool to help them do what they are already doing at less cost or faster, while others see AI as something strategic that will help them do something no one else in the industry is doing. There are however many examples of small data teams – M&M Direct is a great example – that have become powerful forces, even with only a handful of data scientists. With well-implemented AI, small teams can become drastically more powerful, and a great example to larger companies – if a small team could do it during good times, you can do it in a recession. 


Companies will begin to think differently about AI reuse

Shaun McGirr, EMEA RVP, AI Strategy

We will see reuse become about avoiding the costs of the most expensive kinds of AI models. While the cost-effectiveness of the cloud is enticing, the appetite for data from the most sophisticated AI models is increasing much faster than cloud costs are decreasing.

This means the most impressive AI models available today can only be built by a handful of very large companies. So if you are not a multinational tech giant, it’s time to think seriously about whether something like custom-built image recognition AI is actually the core of your business, or whether you can borrow someone else’s to start with, saving 99% of the cost and delivering value sooner than later.

Trivendi Gandhi


Most companies will continue investing in ethical AI teams

Triveni Gandhi, Responsible AI Lead

While we’ve seen headlines in the news about some companies cutting ethical AI roles, the reality is that most companies will continue investing in their ethical AI teams. This resource is crucial for the scale and operation of AI, helping companies to be confident that their AI outputs are aligned with their values and executed in a robust and reliable way. What's more, ethical AI teams give confidence to users that the products they are interacting with are considered and meet expectations around safety and trust. For any company to stay ahead of the curve, building and enabling an ethical AI group is a must.

Jacob Beswick


2023 will be a pivotal year for AI Governance

Jacob Beswick, Director, AI Governance Solutions

We are set to see companies build from years’ worth of principles-based discussions around AI Governance and Responsible AI to implementing practical approaches.  This actionability is critical: taking principles and translating them into actionable criteria that have a material impact on organisations, their employees and their end users is essential. The companies that will succeed are those that will take a unified approach that combines an AI Governance framework, a solid Responsible AI programme, and the successful implementation of MLOps to ensure that established processes and frameworks are made operational through the entire AI lifecycle. 

We’re set to see increasing momentum in the movement from years' worth of principles-based discussions around AI Governance and Responsible AI beginning to result in the building and operationalising of practices that aspire to meet the objectives that have been laid out. 

There are currently – and for a time will continue to be – first movers on this: at an organisational level, those operating within regulated industries, and those where leadership sees the value in establishing practices earlier rather than later. There are advantages here: not least the prep time they'll have in finding the best change management practices, but also in aligning employees to new ways of working. Companies investing in Governance and Responsible AI will also have a first shot at communicating to their customers and the wider world what soon everyone will be expected to do: putting principles and values into action.


In the domain of AI Governance and Responsible AI, actionability is critical to efficacy

Jacob Beswick, Director, AI Governance Solutions

This means taking principles and deciding how they can be met through, for instance, actionable criteria and indicators. Ultimately, the companies that will succeed are those that will be holistic in their approach: setting out their priorities and values through an AI Governance framework, balancing and allocating responsibilities within the organisation to deliver, and ensuring consistent operationalization through leadership engagement on the one hand, and systematized implementation through MLOps on the other.


The search for best practices will continue

Jacob Beswick, Director, AI Governance Solutions

The search for best practices is going to continue to take place on a number of fronts and to varying degrees. Alongside first movers, public sector entities will be a driving force. And while the prospect of regulation continues to be catalytic, it's signing into law (for the EU possibly as soon as the end of 2023) and the role of domestic and international standards organizations will provide first movers opportunities for reflection, and later movers the material to consider when building out their AI Governance and Responsible AI practices.


Cloud companies will continue to perform well 

Kurt Muehmel, Everyday AI Strategist

In 2023, cloud companies will continue to perform well despite the current market conditions. When you enable your customers to improve the efficiency of their core operations, they will keep spending even when they have to cut back elsewhere. Data is the raw material from which these efficiencies are built, using the software from the cloud giants, and the successive application layers running on top of them.


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