UK Media Company WPP Acquires AI Company Satalia

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The process of developing advertising campaigns is evolving with the increasing use of artificial intelligence (AI). Advertisers want to optimise the amount of data at their disposal to craft better campaigns and drive more impact. Since early 2020, there has been a real push to integrate AI to help measure the effectiveness of campaigns and where to allocate ad spend. This now goes beyond media targeting and includes planning, analytics and creative. AI can assist in pattern matching, tailoring messages through AI-enabled hyper-personalisation, and analysing traffic to communicate through pattern identification of best times and means of communication. AI is being used to create ad copy; and social media and online advertising platforms are starting to roll out tools that help advertisers create better ads.

Ecosystm research shows that Media companies report optimisation, targeting and administrative functions such as billing are aided by AI use (Figure 1). However, the trend of Media companies leveraging AI for content design and media analysis is growing.

Business Leverage of AI in media

WPP Strengthening Tech Capabilities

This week, WPP announced the acquisition of Satalia, a UK-based company, who will consult with all WPP agencies globally to promote AI capabilities across the company and help shape the company’s AI strategy, including research and development, AI ethics, partnerships, talent and products.  

It was announced that Satalia, whose clients include BT, DFS, DS Smith, PwC, Gigaclear, Tesco and Unilever, will join Wunderman Thompson Commerce to work on the technology division of their global eCommerce consultancy. Prior to the acquisition, Satalia had launched tools such as Satalia Workforce to automate work assignments; and Satalia Delivery, for automated delivery routes and schedules. The tools have been adopted by companies including PwC, DFS, Selecta and Australian supermarket chain Woolworths. 

Like other global advertising organisations, WPP has been focused on expanding the experience, commerce and technology parts of the business, most recently acquiring Brazilian software engineering company DTI Digital in February. WPP also launched their own global data consultancy, Choreograph, in April. Choreograph is WPP’s newly formed global data products and technology company focused on helping brands activate new customer experiences by turning data into intelligence. This article from last year from the WPP CTO is an interesting read on their technology strategy, especially their move to cloud to enable their strategy.

AI Research and Reports

Ethics & AI – The Right Focus

The acquisition of Satalia will give WPP and opportunity to evaluate important areas such as AI ethics, partnerships and talent which will be significantly important in the medium term. AI ethics in advertising is also a longer-term discussion. With AI and machine learning, the system learns patterns that help steer targeting towards audiences that are more likely to convert and identify the best places to get your message in front of these buyers. If done responsibly it should provide consumers with the ability to learn about and purchase relevant products and services. However, as we have recently discussed, AI has two main forms of bias – underrepresented data and developer bias – that also needs to be looked into.


The role of AI in the orchestration of the advertising process is developing rapidly. Media firms are adopting cloud platforms, making IP investments, and developing partnerships to build the support they can offer with their advertising services. The use of AI in advertising will help mature and season the process to be even more tailored to customer preferences.

AI to Power the Greener Grid of the Future

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Energy providers around the world have transformed their electricity generation profiles to include solar, wind, hydro, and geothermal to reduce the carbon intensity of their economies. Many countries have surpassed expectations by approaching or exceeding 50% of production stemming from renewable sources. Concurrently, the decarbonisation of the transportation sector and the growing use of air conditioning is putting upward pressure on electricity demand.

Energy providers are keen on leveraging AI in several areas (Figure 1).

Energy Providers Leveraging AI

The sudden evolution of the Energy sector is creating new complexity in the grid, which human operators will be unable to monitor and manage without the assistance of AI.

Predicting Supply and Shaping Demand

Output Forecasting. The primary inhibitor to the mass adoption of renewable energy is the issue of intermittency. Solar is affected by shorter winter days and cloud cover, while wind turbines are ineffective during periods of low or even high wind speed. If electricity supply does not precisely match demand, grid operators must fire up costly and carbon-intensive peaker plants to fill the gap or resort to rolling blackouts. AI is becoming a tool for generators and grid operators to forecast renewable output more accurately, insight which can, in turn, be used to shape demand.

Competitive Pricing. Wind farm operators are beginning to use adversarial AI to judge if publicly available data for velocity, pressure, and density obey turbulent flow physics and if not, to refine their forecasts. Equipped with more accurate projections, they can bid with greater certainty on day-ahead auctions rather than relying on less profitable spot prices. This consequently makes wind power more competitive with fossil-fuel-based generators and smoothens out hourly price variability.

Predicting Usage Patterns. Improved temperature and humidity forecasts can also be used by grid operators to carry out peak shaving – encouraging consumers to reduce consumption during high-load periods. By accurately predicting intervals of increased use of home heating or cooling, programs such as Rush Hour Rewards by Nest, allow distributors to remotely adjust thermostats during seasonal extremes for cash incentives. Advanced knowledge of these weather events can also give grid operators the chance to temporarily lift regulatory hurdles or conduct emergency maintenance to ensure maximum capacity is available.

Supply Orchestration. Home battery systems and electric vehicles are growing in acceptance and their storage capacity will eventually become an important piece of infrastructure for time-shifting supply to match demand. The increasing build out of solar PV has created an oversupply in the middle of the day while the rising adoption of home air conditioning creates a spike in demand after working hours, resulting in the so-called Duck Curve (see Figure 2).

Impact of Solar PV on net consumption

By predicting periods of potential supply shortfall, distributors can increase prices to a level attractive enough to prompt battery owners to sell excess electricity rather than store it. The complexity inherent in such a distributed system is only manageable with machine learning to constantly optimise pricing and supply orchestration to simultaneously prevent excessive degradation of battery performance. This is already available for large scale battery operators, e.g. using Tesla Autobidder, and will become accessible to networks of home and eventually vehicle owners.

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The Future

Optimising Renewable Generation with AI

Renewable energy sources continue to make efficiency gains due to engineering improvements. However, advances in AI will increase generation even further. Solar PV and solar concentrators that rotate on dual-axis trackers to follow the path of the sun must each operate individually according to their own precise position and the time of day and year. This must be balanced for efficiency to reduce excessive movement, which consumes a portion of electricity output. Neural networks and fuzzy logic can be applied to optimise rotation to maximise production while reducing power consumption for operation. Input variables can include position, time, temperature, and even sky colour. Similarly, wind turbines can dynamically alter their positions to maximise wind flow across the entire fleet rather than at an individual level. The large streams of data must be processed in real-time as wind variables change to have an immediate effect on output.

Stabilising the Super Grid

To improve resiliency and lessen the effects of renewable intermittency, there is a growing push towards increasing the interconnectivity of national grids. This ensures supply even when regional generators go offline or if sudden local peaks in demand occur. Moreover, interconnected grids help even out supply from renewable sources using the philosophy that it is always windy or sunny somewhere. For example, the proposed European super grid would take advantage of higher wind generation in northern countries in winter and in North Africa in the summer. Additionally, hydroelectric plants in the north could be modified to become pumped storage facilities powered by solar thermal plants in the south to supply all of Europe.

Not only will a super grid require investment in new infrastructure, such as high voltage direct current (HVDC) for efficient long-distance transmission but also in intelligent systems to manage the new complexity. The retirement of fossil-fuel generators and greater variability of renewable sources will require rethinking grid inertia and frequency control between countries. Measurement solutions, such as GridMetrix by Reactive, have been deployed by AEMO in Australia and National Grid in the UK to better monitor how inertia fluctuates as renewable sources ebb and flow. Once real-time data becomes available for analysis, infrastructure such as synchronous condensers and quick-response batteries can be automatically utilised to regulate frequency.

A Positive Outlook

Countries such as China, India, the US, Germany, and Spain have shown that it is possible to add large amounts of solar and wind generation capacity at a pace. The next chapter in the renewable revolution will be ensuring that this can be done at scale without disrupting the grid and AI will be a key component in managing the transition.

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