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Core components of an AI programme

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agnesa_aix

https://www.bankingtech.com/2018/06/core-components-of-an-ai-programme/
 

Artificial intelligence (AI) is having a moment in the spotlight. While there’s no denying that AI holds a lot of promise, it’s often difficult for organisations to determine their path to implementation. There are many vendors offering AI solutions – from chatbots, to machine learning (ML) algorithms – with more solutions popping up by the day. So how do you decide which third-parties to engage with? Or, if AI is the future, should you be building solutions yourself?

 

There’s certainly more than one right answer to this question. No matter what AI strategy you deploy, well-organised data is a critical foundation for success. Many organisations get stuck early on by viewing AI through a narrow lens of either data science or technology, both of which are important partners in expanding AI. However, that narrow view tends to overlook how machine intelligence can redefine strategies and opportunities across the organisation.

At Wells Fargo, we’ve created a cross functional team in charge of accelerating the adoption of AI throughout the organisation, touching everything from the customer experience to operations and risk management. We have a bird’s eye view of all AI concepts we want to test throughout the organisation, ensuring businesses throughout the entire enterprise benefit from all the lessons we are learning.

If you have resources to devote to a dedicated AI team, I would recommend it. If not, it’s important to have an AI evangelist in your company who will break down silos.

There are a number of areas where we are testing and learning AI solutions, and to fast-track how quickly we can bring those experiences to market, we’ve used a mix of outside vendors and self-built solutions and have learned some lessons along the way.

 

1. Keep a pulse on start-ups

Regularly collaborating with start-ups on a wide range of technologies helps us explore big ideas with innovators outside our walls and industry, and shape future customer experience in areas like AI and analytics.

We’ve been able to closely work with the Wells Fargo Start-up Accelerator, a six-month program where we match early-stage companies with mentors in various lines of business and help them refine their technologies for financial services. Our AI team presents specific use cases we have in mind, and the Start-up Accelerator team has a constant eye on tech start-ups that may be able to help us test and learn concepts through very specialised technology. In fact, Wells Fargo was one of the first big banks to work on a Facebook Messenger chatbot pilot with Kasisto, one of our accelerator alumni.

2. Identify use cases and determine key capabilities you’ll need

It’s likely you’ll have similar AI use cases in various areas of your organisation. When we centralised our AI work last year, we conducted internal research and developed a comprehensive list of projects and products where AI already existed, was being researched or tested, or could be a good fit. We also created a list of assets we already had, assets we needed, and vendors we were working with.

Once we had a strong list of core AI capabilities, resources, and tools we already had, we were in a better position to prioritise use cases and determine longer term strategies to buy or build in different areas.

Having a centralised AI team has helped us share expertise about solutions, cut down on duplicative work and prevent us from building or buying duplicative solutions.

3. Test and learn

Because we often have several similar concepts or use cases, we use proof of concepts as an opportunity to test out various vendors as well as self-built platforms to compare strengths and weaknesses and pinpoint the right solution.

Right now, most AI solutions are not technologies that plug-and play. If you pick a vendor, it doesn’t mean you have to stick with them forever. However, you’ll need to deliver on the meaningful solutions and insights your customers and employees come to expect.

4. Map out a formal selection process

In addition to having a solution that works well for your needs, determine specific criteria checklist before formalising an agreement with a vendor. Are they easy to work with? In proof of concepts (POCs), did they deploy the solution in the agreed upon timeframe? Do they meet your data security requirements? Are there performance metrics you can benchmark against? There are a variety of standards you can use, so make sure you’re identifying criteria that fit with your mid- and long-term AI strategy to save headaches later.

5. Determine your core competencies

Depending on the size of your organisation and use cases where you plan to integrate AI, it may make sense for you to outsource some of your AI projects.

Building and maintaining systems is a lot of work, and you need employees with specialised skillsets, some of which are very difficult to find and scale. It makes sense for someone with a strong knowledge of internal capabilities to be the point person for AI vendors. He or she will be in the best position to negotiate, as well as understand the internal organisational dynamics to make sure a project gets up and running.

AI is an exciting tool, but it’s important to remember that it’s not a magic tool, and it’s not appropriate for every use case. Don’t be afraid to start small.



photo_2018-05-15_09-12-58.jpg.e713d52a0f9871083b13772ec6ad34cc.jpg

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