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What Is Google’s AI Adoption Framework

What Is Google’s AI Adoption Framework

What Is Google’s AI Adoption Framework

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Google Cloud, has recently launched their AI Adoption Framework whitepaper, authored by Donna Schut, Khalid Salama, Finn Toner, Barbara Fusinska, Valentine Fontama, and Lak Lakshmanan, to provide a guiding framework for enterprises to leverage the power of AI effectively. 

Google Cloud’s AI Adoption Framework has been designed on four pillars of an organisation — “people, process, technology, and data.” Google Cloud blog further noted that these four pillars of organisations should follow six critical themes — “learn, lead, access, scale, automate, and secure — for their AI success. According to Google, “These themes are foundational to the AI adoption framework.”



Explaining further, the blog highlighted these six steps that are critical for businesses:

The first step concerns the scale of ‘learning’ within an organisation, which includes the process of upskilling existing employees, recruiting new talents, and augmenting analytics and engineering professionals with “experience partners.” This process of learning will help organisations to decide which analytics and machine learning skills would be required for the business, and accordingly, they can strategies their hiring process amid this crisis. 

Second, comes the ‘leading’ which concerns whether or not the leaders of organisations provide enough support and guidance to data scientists and engineers to deploy machine learning and artificial intelligence in their business projects. This step would help businesses in understanding the structure of the team, the cost of the projects and the governance of the projects to encourage cross-functional collaboration in the organisation. 

Next is the ‘access” to data, where companies recognise the data management strategies and analytics professionals are able to collect, share, discover, analyse the data and other ML artefacts. 

Then comes the ‘scaling’ process, where companies can define their ability to use cloud-native ML services and scale large amounts of data with reduced business expenses. This process will also guide you to understand the cloud-based services and how the workloads are allocated to them.

The fifth is the ‘securing’ process, which is extremely critical for organisations. In this process, enterprises can understand their security strategies to protect sensitive business data and essential information. Additionally, this step will also help companies to ensure deploying responsible and explainable AI practices, which will intern drive their business value.

Lastly, is the process of ‘automating,’ where businesses would be able to realise their ability to deploy, execute, and operate the technology, to enhance and streamline data processing and ML productions. This process will further help companies to understand and track data lineage and monitor the process.

These processes are critical for businesses to adopt effective AI practices for their organisations. According to their blog post, “Successfully adopting AI in your business is determined by your current business practices in these areas…”

Also Read: Why Majority Of Data Science Projects Never Make It To Production

The post further noted that the preparation for companies to adopt AI in their business processes would be critically determined by their current business practices on the aforementioned steps. Further, these steps go through AI maturity phases where it undergoes tactical, strategic and transformational phases. In each of these levels, it will be determined how AI is going to be adopted for creating value for organisations.

Taking an example of the “learning” step, where in the tactical phase, organisations will encourage self-motivated learning using online courses, and businesses will prefer outsourcing their analytics needs, and therefore, their outdent would be in hiring any ML talent. On the other hand, for the same step, learning in the strategic phase, the businesses will be looking to hire data science and ML professionals as well as will be urging their existing employees to go through continuous training programs. Businesses will only be looking to involve third parties for consultation or specialised knowledge. Thirdly, once this step is in the transformational phase, companies will enhance their work by involving data scientists in their core businesses, as well as hiring new talents with industry expertise for innovation. In this phase, businesses also partner with other companies to augment their technical capabilities.

For more information, see the image below:

When AI gets incorporated in all these aspects of organisations, according to Google Cloud, then those companies are “fully harnessing” the capability of artificial intelligence to transform their work. “But at every step along the way, adding effective AI capabilities can bring benefits.”

Such an effective technology agnostic framework will not only help businesses to understand and strategies their AI game but also help them resolve the challenges that come along with it.

A Deep Dive In The AI Maturity Phases

The AI maturity depends on three phases — tactical, strategic, and transformational. And according to google, “every organisation’s AI capability falls under these three phases.” Each stage has separate characteristics and provide distinct opportunities for growth and advancement, explained below:

Tactical Phase

Organisations that are still in the tactical phase of AI adoption are working on gaining short-term benefits from AI. This is why the majority of them use AI for small cases, and the developers mostly rely on exploratory data analysis, and automated AI tools for “proofs of concepts and prototyping.” For instance, companies would prefer using pre-built and pre-trained facial recognition technology to monitor their business operations or use predictive analytics to enhance customer segmentation, rather than obtaining ML for complex business problems, which are usually outsourced to their parties. 

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Although this phase doesn’t include much of innovation or requires hiring newer talent, it indeed has some benefits. In the tactical phase, organisations can have access to better and cleaner data for making a better-informed decision with actionable insights. Also, considering this is the first phase of AI maturity, this indeed helps validate their process and power of ML in front of stakeholders. Companies, at this phase, should “develop foundational skills for core data wrangling and descriptive analytics.” For these businesses need to collate the siloed data into a unified data lake with decentralised access, where every employee of organisations can access the data to make necessary decisions. 

Strategic Phase

Secondly, in the strategic phase, organisations are clearly focused on deploying AI in different parts of organisations that leverage both ready to use and custom models to bring business value. For this, companies also require to hire talents as well as build core ML teams that can help deploy and manage the technology for businesses. This core team will not only help companies accelerate their business but will also ensure constant governance and ML collaboration amid different groups. 

At this phase, ML professionals use data in predictive analytics to solve business problems and also deploy ML in production. Thus, in this phase, companies can benefit from developing AI capability that is customised to business requirements. With AI and ML collaboration within companies, teams will help the businesses accelerate automation in every aspect.

Transformational Phase

The transformational phase is the most advanced phase where businesses actively use artificial intelligence and machine learning to innovate, streamline their business, and create an AI-driven culture amid organisations. In this phase, companies not only build and deploy machine learning platforms but also make ML accessible to every employee of organisations. Further, in this phase, enterprises work on a hybrid model where they combine functional or product-specific AI teams with broader product teams, which is also combined with analytics capabilities allowing employees to be responsible for building their own function-specific ML models. This, in turn, will enable companies to construct high-quality technical solutions which can be used to solve real-world problems.

In this phase, businesses also define ethical and responsible AI practices, which can hugely benefit organisations in the longer run. “The ML platform is supported with tools for continuous integration, training, and model serving and monitoring. Building and maintaining such a platform is a shared responsibility between ML and software engineers with skills in infrastructure, DevOps, and SRE,” stated the blog.

Moreover, companies at this phase usually tend to work on cutting-edge research and robust innovation in the areas where they have unique capabilities, domain knowledge, and data availability so that they can build a competitive advantage over others.

For more information, read the whitepaper here.

Wrapping Up

To summarise, it can be said that companies with any level of maturity, whether it be tactical, strategical or transformational, can benefit from artificial intelligence. However, the level of AI maturity can only be determined by analysing the amount of AI involvement in the business. By analysing these perimeters, stated in Google Cloud’ AI Adoption Framework, companies can understand how their businesses are currently functioning in the AI maturity scale, and how they can devise their strategies for better business outcomes.

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