The Key Building Blocks to Getting Results with your AI Strategy

Artificial Intelligence (AI) has significantly evolved from being the latest technology buzzword, to the commercial reality that it is today.  Companies with an expertise in machine learning (ML) are already evolving to build AI-based applications for their businesses. In today’s challenging environment, this must continue to happen but at an even higher velocity.   Amidst all the AI hype, and the concern over being left behind, how does an organisation implement an AI transformation strategy effectively and efficiently?

There are several challenges in establishing an enterprise AI transformation strategy, and these can be addressed in three primary areas.  Talent comes first, as organisations need to hire, train, and bring on board the right skills.  The right team is the first building block to driving the AI transformation and results needed.  The second challenge is Time. It is important to assess how fast you can achieve business results by implementing the AI strategy and to create a safe environment where people can fail fast. Gaining Trust in your AI technologies that are underpinned by machine learning models is the final challenge.  In an increasingly uncertain and data-cynical world, keeping regulators and broader stakeholders on board is key, so being able to explain the results of your ML models is paramount, and will help ensure uptake of the underlying technologies.

Given these challenges, what’s next?   

1.       Enable a Data Driven Culture

To effectively use the volume of data generated, companies need to build and encourage a data-driven culture to evolve.   Here are some key considerations to support this.

Effective Questioning: Asking the right questions is key to building the right company-wide data culture.  How do we go about customer acquisition, who would the ideal customers be and is  my supply chain optimised may be some initial commercial questions.  Assessing the business problem is key to any AI implementation.  To develop relevant questions, companies need people who are creative, understand the business constraints that they’re working in, have an analytical mindset and who can provide solutions that are backed up by data.

 

Data Capture: To enable a data driven culture to emerge, firms need to proactively collect data, which can be obtained from sources, including the marketing and sales departments, product monitoring and customer analytics.  Together, this will help form the foundation of a data culture.

Data Accessibility:  The data that’s been collected needs to be accessible to all appropriate departments and individuals within the company.  This means that the data should be presented in a format that is easy for people to work with, allowing them to glean actionable insights.

 

Finding the right Expertise: Data is a team sport, so while companies need experts to build models and algorithms, they also need people with other technical and commercial abilities, that allow them to uncover useful data insights. This helps utilise the existing workforce, since they have the essential domain experience for the job. ML is as much of a cultural transformation as it is a business one.  Instead of rebuilding an entire team from scratch, companies should consider hiring several data scientists and utilise their existing pool of talented employees to assist them.

 

2.     Ensure a Competitive AI Strategy

 

Progressive firms are already implementing their AI and machine learning strategies cross-industry, ranging from Know Your Customers (KYC) and Anti-Money Laundering (AML) in financial services to early Cancer Detection and Personalised Prescription Matching in healthcare to Customer Churn Prediction and Fraud Identification in telecoms – to many more. 

Using AI across sector can save time and money, whilst enabling companies to gain a competitive edge.

Determine outcomes: Asking pertinent questions determines what outcomes can be generated from specific applications.  The priority is to capture your firm’s high-level goals, translate this to a business challenge, and then determine the final results.

 

Measure Success: Companies must also identify metrics that can measure success. The definition of success may vary for different companies, but the goal remains the same; making a profit and delivering value.

 

Connect with the data/IT Ecosystem:  Community plays a vital role in driving change. There are many ways to connect with the machine learning community, including online webinars, as well as offline at meetups, when the time is right again for that.  This will enable community members to exchange knowledge and learn from each other.  Learning together, participating in online sessions and sharing relevant insights are great ways to connect with the community, wherever you are globally.

 

 

 

 

 

 

 

 

3. Establish Trust in AI and Technology

Machine learning models should not be seen as ‘black boxes'.  We should be able to explain them coherently, identify the logic behind the predictions and document the building process. Being able to describe the model’s decision adequately, having sound documentation and eliminating bias from the results are key considerations for companies, to instil trust in AI.  Deciding what technology to use is essential, as this can have a profound business impact.

 

Open Source or Proprietary software: When companies start on their AI journey, they will need to decide between open source or proprietary software, or perhaps both.  Many existing pioneers in machine learning and AI regularly open-source their technologies, which provides a good starting point for others.  As these new AI players mature, organisations may need to evolve to the available commercial platforms.  

Cloud or on-premise environments: This depends on how soon you want to start.  If you are starting from ‘ground-zero’ and have no existing infrastructure in place, going the Cloud route makes more sense.  It eliminates the need for procuring and setting up hardware, as well as the security, infrastructure and maintenance issues.  However, if you already have a decent DevOps infrastructure in place, the on-premise option can reduce costs.  Many companies may opt for a hybrid model, which is good practice, and enables companies to switch between cloud providers and on-premise.

 

Data Access: Once again, data is a critical point. Understanding how to generate, save and make data accessible is of primary importance.  Areas such as data privacy, data governance, security and data lineage are some of the points that need to be addressed by IT leaders, in advance of their AI transformation journey.

 

Address Challenges and Get on the AI Road

So where do you go from here? By assessing these three key challenges, Talent, Time and Trust, and how they might be addressed, companies can get a sense of direction about where and how to kick-off their AI transformation journey.  Identify the current problems that you are trying to solve and evaluate how you can leverage machine learning and AI to give you that competitive edge.  An AI culture needs to be encouraged and nurtured, and, like any important task, requires an investment of time and resources.