The future of all business operations fundamentally relies on utilizing AI and Machine Learning technologies – even conservative estimates put it in the billions for large enterprises. The big technology companies have already realized this and have been acquiring both unique technologies and the ’s best talent. This is evident with all the new product announcements of late (Siri/Apple HomePod, Google Assistant, Google Duplex, Facebook facial recognition).

It is not just companies that see the opportunity, many countries, having recognized this trend are already investing aggressively, China, Singapore, France, Japan or have plans like Canada.  

Andrew Ng puts it nicely:

“In the past, a lot of S&P 500 CEOs wished they had started thinking sooner than they did about their strategy. I think five years from now there will be a number of S&P 500 CEOs that will wish they’d started thinking earlier about their AI strategy.”

Despite the advancements it is important to note that AI is still a rapidly growing field – there is significant new research that comes out every day. This is much like the internet was in the 2000’s – wildly important, but still being actively developed.

What is AI?

It is important to address what we mean by AI before we go any further. AI is a very broad concept with very fuzzy boundaries and as such remains hard to define. Some use the term to refer to AGI – Artificial General Intelligence, which is the pursuit of creating hardware/software systems such that the same system is at least competitive with human beings in a broad range of tasks. We are very far from being in this ballpark.

The main difference between regular programs and AI programs is that regular programs are carried out by executing a fixed algorithm. AI programs on the other hand, are parameterized by data. In other words, the behavior of the algorithm can vary vastly based on the data that it is exposed to. For the purposes of this article, I will use the term AI and Machine Learning synonymously.

That said, it is important to think about the problem domains where AI is applied in the enterprise context. There are broadly two problem domains – where the data is based on perception and where it is not:

  1. Perception based problems work with data such as images, audio, video, sound etc. The key fact about perception data is that it tends to be _overcomplete_ for a certain class of inference tasks. In other words, imagine taking a picture with a 12 mega-pixel camera and the same scene with a 1 mega-pixel camera. If you showed the two pictures to a passerby, they will likely make correct, general inferences. Thus, an image with 90% fewer pixels contained enough data to make inferences – i.e. the problem is robust to deleting some data. Many of the challenges of enterprises interfacing with their customers likely fall in this category (e.g. biometrics, chatbots etc.).
  2. Non-perception based problems work with data that is high-dimensional and sparse i.e. data where for every entity we have lots of information. Imagine the rich data that an enterprise has about their customers – as we add sensors to the world, this will keep getting richer. Now, if you look at a table with each row representing a customer, the table will have lots of columns – this is high dimensionality. Unlike perception based data, these problems tend not to be robust to deleting data – you need every last bit of it to make inferences. This is a fundamental difference. Most of the challenges of backend operations in an enterprise fall in this category (e.g. regulatory risk modeling, financial crimes, clinical variation management etc.)

A Note about Models

The thinking around practical use of AI is often simplified to mean predictive analytics models – i.e. constructing models that are predictive. This is by far insufficient. Here’s why:

  1. Predictive models need clean, labelled data. The vast majority of data in large enterprises is unlabelled and it turns out that labelling is a very expensive exercise. So what do you do with the 99% of your data that is unlabelled?
  2. Models are very difficult to deploy in production. Often large enterprises take models developed in Python/R etc. and re-implement them in production systems in Java/.Net etc.
  3. Models are not applications. I believe that AI is useless without UX. It is really important that front end line of business owners be able to benefit from AI and for that to happen the results of models must be wrapped in UX that makes business sense.
  4. Models don’t evolve. Typically, organizations re-build models manually every so often. The issue with this approach is that the underlying distributions in data can change without the end user realizing – making the model less effective.

What Makes an AI Platform?

Here are 5 basic ideas to look for in any AI platform:

  1. Discover – the capability to learn patterns from large complex data without upfront human intervention. This relies on unsupervised, semi-supervised and generative machine learning techniques and outputs artifacts such as segments and anomalies. Note that this does not require labelled data – it can learn from unlabelled data (and utilize labels if they are available).
  2. Predict – the capability to make predictive models.
  3. Justify – as narrow AIs start getting integrated into the enterprise fabric, these systems will need to build trust with their human operators. Justify relies on student-teacher learning to provide independent justification behind every ‘decision’ proposed by an AI system. This is especially important in regulated industries.
  4. Act – is the ability to integrate AI systems from an engineering point of view. This means the ability for the development system to:
    1. Consume data regularly
    2. Process and push out results to other systems (e.g. push predictive leads into a CRM system)
    3. Provide an interface to build and deploy applications which can be used directly by line of business users.
  5. Learn – is the ability of the AI system to monitor data and suggest to its human operators when the models degrade, or when it finds a new segment in data or a new type of anomaly etc.

Why You Need a Center of Excellence

Every major platform technology wave that gets adopted in the enterprise exhibits the same pattern:

  1. Slow adoption at the fringes where either the need is great or there’s freedom in the enterprise to experiment.
  2. Select customers or enterprises begin dominant use of the technology, both reaping huge benefits as well as training a set of future leaders.
  3. Other enterprise customers, who were left behind, see the broad benefits and start adopting the technology.

This has happened over and over again, from the transition to based software (SAAS), to mobile development. Every large enterprise on the planet today has a Center of Excellence for and mobile. AI will be no different.

The Mission for the Center of Excellence

Alright, if you are reading this, you likely are bought into the fact that you need a Center of Excellence (COE). So what does the COE need to accomplish:

  1. A delivery center. While there is scope for experiments, it is really important that the COE deliver results to the business. This is what most COEs get wrong – they setup it up to experiment and research, but not deliver.
  2. Deliver applications. Today, the thinking around AI  and ML mostly stops at models or dashboards. This is a good first step, but to have real impact with this technology, it must be expressed within applications that are usable by the end line of business users, who should not need any expertise in AI/ML to use these applications. Think Apps not Models.
  3. Data culture. Data is a pre-requisite to successfully using and deploying AI and in most enterprises, it is quite broken and siloed. An AI COE needs data and a very pleasant by-product is the culture of treating data as an asset.
  4. A program that leverages AI software to help achieve key business objectives, while building strong capabilities in AI.
  5. Self-sufficiency in deploying AI based applications.

Delivery

A COE is a vehicle, a framework of execution. That framework will differ from organization to organization but some common themes remain consistent.

  1. Value Management: Ensure the organization invests in valuable projects and derives optimal benefit from its AI investments
  2. Demand Management: Allocating resources (people, ) across all projects in flight and monitoring spend. This will be critical as choosing the right projects is key as is them for success.
  3. Execution Support: The CoE supports various lines of business during its lifetime. This requires a provision of dedicated technical and subject matter experts, working together with operational resources from the line of business
  4. Data management: Manage data through stages in its lifetime, from growth to maturity to a managed decline
  5. Enablement: Provide systematic and iterative education by role, with support at every step in the form of classes and consultations
  6. Application delivery: Delivery of business specific applications (and associated documentation) that integrate the workflow end to end, from incoming data, to output into downstream applications or dashboards

 

The People Part

While the media discussion is about the machines – for AI to work in an enterprise setting requires people – and ones at that. In our experience the following roles and requirements are needed:

  1. Program Manager. Accountable for the overall COE program, communicate priorities between the leadership and program teams, manage schedule and budget, provide risk management. The ideal candidate will be an excellent leader and will have experience in managing staff of different disciplines to produce results in a timely manner. The program manager needs experience and the ability to command a team of both direct and matrix reporting relationships. Program managers who are technical and have a consulting background have a high degree of success in our experience.
  2. Business Solution Architect. Understands the nature of the line of business, the driving imperatives and can architect a solution that clearly defines the desired end application state, and the benefits driven by moving from the current state (as-is) solution. This role favors individuals that can juggle conflicting requirements and arrive at a clear well documented solution, which they can communicate clearly. 
  3. Technical Solution Architect. Accountable for understanding and inspecting business requirements, collaborating and architecting a technical solution that clearly defines the desired end application state, and the benefits driven by moving from the current state (as-is) solution. This is a critical role and demands someone that can drive a team to deliver the approved technical solution, following the design and methodology selected. They also need to be able to execute across the full life cycle from conceptual design, to documentation. These are technically skilled resources with the ability to code in Python, query in SQL and play with big data stacks like Hadoop, Hbase, Sqoop, Hive, Spark etc..
  4. Developer. The developer is responsible for delivery of robust, high quality applications following an approved design.
  5. Data Scientist. Responsible for driving strong analytical strategy given the requirements of the business, and a strong ability to inspect available data. These resources are adept at converting analytical ideas using production-quality Python code, building a pilot that holds up to robust data science principles. They are not easy to find but are key to a successful COE.
  6. IT project manager/SME. The COE is about action. That means IT needs to be involved. This role is responsible for ensuring the COE has alignment with IT, clears IT hurdles and everything is in order and on time as the ML application moves from a pilot stage through to a production deployment.

For most sophisticated organizations – all of these folks are in the building. The challenge for the management team is to get them all in the same room and pursuing the same goals.

Final Thoughts

AI is here. It is real and is already impacting major industries. This is a field where a headstart has massive implications. The best, most proven way to get that headstart, or a least ensure the organization doesn’t fall behind is to put a Center of Excellence in place. A COE provides the structure, the governance, the prioritization capabilities and the measurement mechanism to impart accountability on this, most critical of initiatives. If you want to talk further, please don’t hesitate to reach out and we can arrange a time to talk through our experiences establishing a Center of Excellence for some of these pioneering organizations.  



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