Organization – With a Data and AI Mindset

Machines of the industrial revolution performed repetitive mechanical tasks at a higher level of accuracy, reliability, productivity, and cost efficiency than humans, and as a result, eliminated many jobs. Similarly repetitive information jobs such as granting approval for opening a bank account, granting loans, filling out forms, providing information, transferring information from A to B, call centers etc. will be increasingly delegated to AI, making many jobs which humans perform today redundant. The new job roles in organizations will be expected to leverage data and the power of AI in order to accomplish higher level task.

An HR specialist of tomorrow will automatically have AI seek out the best candidates to hire and set up the interviews with hiring managers. The HR role will shift to a higher level ?—? salary negotiations and ensuring fast integration of the new hire. When I was first hired, mature PC and mobile skills were a basic requirement. The new hires of the future will be required to be AI-aware and AI-friendly, just as we are PC- and mobile-friendly in our current work.

There will be new AI tools used as a standard practice – such as Einstein from Salesforce.com – which will provide a sales or marketing person with much richer information. For example, analysis and prediction by AI about a customer’s intent, and for suggesting alternatives on how and when to best submit sales proposals.

Most jobs will be transformed, making Change Management one of the biggest challenges for companies seeking success in the AI age. Beyond changing individual job roles and skills, the organization of employees will also need a fresh approach. The exact nature of the transformation is not easy to predict, as it will depend on the nature of business, country of operation, and company culture.

Alex Osterwalder & Yves Pigneur, the authors of the popular books, Business Model Generation and Value Proposition Design, have made a compelling case for appointing a Chief Entrepreneur in a company to focus on the future of the company while the CEO focuses on the present. (http://blog.strategyzer.com/posts/2017/7/11/an-open-letter-to-ceos)

New Skills and Competencies

As with any project, success depends on a number of fundamental skills and efficient processes. These are areas such as strategy articulation, getting project approval, planning, project management, and customer validation. However, AI projects also require a number of new skills:

  • Understanding of new AI capabilities
  • Knowledge of available AI services
  • Working with AI platforms
  • Accessing data sources and sensors
  • Machine Learning
  • Neural Network setup, configuration and optimization
  • AI – Human interfacing

AI has the power to radically transform a company’s entire approach to products and way they are offered. This requires a very open mindset regarding the company’s business objectives and core strengths. It is vital that the company strategy and product planning team have people with excellent insights into the potential of AI, and new capabilities enabled by it. External expertise can be of big help for guidance on the maturity and applicability of new AI to company goals.

When it comes to AI basic services, it makes little sense to develop internal expertise on AI services, like speech recognition, translation, or smart mail response services, as these basic services are available as open source, or from multiple vendors. You just need to pick the service that best matches your needs. Major AI players like Google, Amazon, IBM, and Microsoft offer platforms for development of AI projects. These platforms offer many AI services, tools and consulting services, which can significantly cut down prototyping and customer validation time. AI specialist companies can be used for specific areas such as, setting up neural networks, training the AI system, and collection of data.

The design of AI systems needs a good understanding of the data needed by the AI systems for reliable operation, as data is their raw material for AI systems. Machine learning and neural networks form the core of any AI system. Machine learning needs validated data sets for training the neural network as well as reliable, up-to-date and good quality data as input for decision making. Some of the required data originate from dedicated sensors. Data scientists are the specialists in this area, and must be a part of the AI team, since they ensure that the data is appropriate, neural networks are setup and optimized, and the network is trained.

Humans have learned how to interact and communicate with humans over thousands of years. We have learned to interface with one another. AI is learning to mimic humans in these specific areas but interfacing AI with humans remains far from trivial. Even the simplest AI devices such as, the Amazon Echo or Google Assistant need excellent interfacing with humans, making the communication feel natural and human-like. I am more likely to use the Echo when my communication with it is so natural that I can hardly differentiate the experience from that of interacting with a human.

Developing Your AI Strategy

The first step in developing an AI solution is to discover and finalize a creative idea at the core of your solution. You may have devised several ideas, therefore you need to have a process for short listing the most viable idea and honing in on it. We recommend carrying out customer interviews and experiments that validate your assumptions and prevent you from delving into areas that are not relevant. We often use the method of Google Venture’s Sprints method which brings together a team of 5-8 people to find answers to big problems in just 5 days, and validates the answers with real customers. By doing this in only 5 days, it ensures the team focuses only on the important aspects, and ensures nobody becomes too attached to the solution before it is validated or discarded. If you are using a cloud-based AI service, you can also add developers to your SPRINT team, which will help you to develop a real and functioning prototype. Running these such experiments is critical to making fast progress.

Now you need to develop a strategy around the creative AI idea. Since AI is a new technology, you may find that for most companies, multiple iterations of strategic reviews and management approval are needed to get a go ahead. This is not only because AI is new in nature, but also management may not have a clear idea on the impact on the bottom line. Jeff Bezos, CEO of Amazon, says that many benefits resulting from AI are internal to the company, meaning a reduction of costs may not be immediately obvious. The value to customers can sometimes be translated in the form of significantly better personalization, higher accuracy, and ease of use.

As with any project, an AI project must start with absolute clarity on two areas defining the strategy:

  • Value Proposition (VP): What new value are you offering to your customer segments? Which customer segments are you addressing and what are their needs? Will your new value offering better satisfy customer needs?
  • Business Model (BM): How will your business benefit from the new offering? How will you monetize this value proposition? This involves a clear understanding of the sources of your revenue, costs, partners, and interactions involved.

These two areas describe the total value of your AI project to customers and to your business overall. You will find that these two areas are non-trivial and often need the highest quality of attention and time. The rest is a matter of implementation.

Fig. 12.1 Developing your AI strategy – from the creative idea to implementation

Articulating a VP for AI based projects requires an excellent understanding of the new capabilities that are enabled by AI today and in the near future. The low hanging fruits of AI capabilities are speech input, computer vision input, people and face recognition, image and object recognition and scene description. New capabilities are evolving every day. The core VP for your business will be centered around a creative AI idea that leverages one or more new capabilities of AI, in order to deliver new and unique value to your customers. The BM converts the VP into a profitable business. Development and approval of a solid VP and BM for AI solutions is the first and most important step for AI projects in most businesses. Since AI is a new technology with benefits that are often not well understood, this often takes much longer than expected. The availability of AI services and platforms help in implementation of the solution. The AI&U canvas is useful in capturing all critical elements of your solution, and allowing you and other decision makers to discuss, and form a consensus.