How to build a Strategy for becoming a AI first company
Succeed with a holistic and sustainable AI first strategy that affects the entire enterprise.
Transform your business into a data-driven enterprise
A worrying number of companies are developing an AI strategy that aims merely to apply AI to their regular business processes and improve them slightly. The AI-first strategy differs from such approaches by radically changing business-as-usual.
The AI-first strategy is about unlocking new business opportunities, creating greater customer value, and solving old business problems with disruptive solutions. A potentially disruptive technology requires an equally disruptive approach to make it happen. Not all companies are aware of this, and that is why 50% of all AI projects fail!
Let us help you with a guidance first published in the book from Ash Fontana on how to implement an effective AI-first strategy:
"We will move from a mobile first to a AI first world"
Sundar Pichai, Google CEO
Build an AI ready
AI-first strategy embeds AI into the very fabric of the company’s approach to business. It empowers the workforce to utilize AI and develop innovative solutions that add value to customers and improve the company’s bottom line. Naturally, a paradigm shift in the organizational culture would be required to make room for such an approach. And, as with any change initiatives, it will be met with inertia.
Your AI strategy must receive support from every level, every department within the organization to succeed. Here are some ideas to enlist organization-wide support for your AI strategy:
Get Top Management Buy In
Lead the change from the top. With the top management spearheading the AI transformation, the workforce gets an unequivocal message that it’s essential, it’s happening, and there’s no way around it!
Create an AI SWAT Team
Enlist enterprising talent from various departments and appoint them as change agents. Obtain feedback from them on their respective departments’ needs, potential AI applications, and roadblocks to AI transformation. Empower the change agents to lead AI transformation in their respective departments. Reward individual accomplishments to boost team accomplishments.
It’s not enough that your change agents agree to lead AI transformation. They must be invested in its success. And, that happens only when they believe in AI’s potential. Educate them on AI, its potential applications, and its potential impact on their department and/or the company.
Skilled talent is perhaps the second most important pillar of an effective AI-first strategy, right behind quality data. Here are some things to consider when outfitting your AI team:
Finding the Right Talent
The right talent is crucial for realising your AI goals. However, finding specialized machine learning skills is easier said than done. Artificial intelligence is still in its nascence. Therefore, AI talent is not available in abundance, which makes it expensive.
Also, AI projects require a variety of resources. You need data engineers or researchers to organize your data, data scientists to extract insights from it, and software engineers to create applications on top of them.
Do you have the budget to support an AI team?
Hiring vs. Grooming
Now, the potential ROI on AI investments far outweighs its costs. That doesn’t mean you splurge money on hiring. Sometimes, it may be more sensible to groom in-house talent than go for external hiring. With some training and experience, your in-house staff may be able to shoulder the ownership of your AI project.
Value- based Strategy
Ensure that your AI team is aware of the organization’s goals and values and is developing AI goals in line with them. Simultaneously, the newly formed AI team must have clear titles and team structure to function like a well-oiled machine towards achieving common organizational goals. And the goals should be prioritized based on the business reality and organizational values.
An AI project is unlike any other business-as-usual project within organizations. Its success will have a far-reaching impact on every aspect of the organization and its future. So, a clear implementation plan is in order.
Create a comprehensive list of processes, tasks, and problems where AI can deliver maximum and/or immediate impact. Also, prioritize these AI goals based on their importance to the business, and work on them in that order. Articulate the AI goals for each department, and define their scope, including technical prerequisites and ideal outcomes.
Formalize Them With an AI Framework
Develop an AI framework with a standard set of criteria for evaluating AI's success on all pre-defined goals. The criteria should organically lead you to suitable metrics that help you assess the success of AI initiatives at every level.
A robust data policy sits at the heart of a successful AI strategy. Developing one involves three steps:
Identify Data Sets
Have a clear vision of the benefits you intend to extract from your AI and identify the data sources necessary for realizing those benefits. Some use cases of AI can be:
Developing improved or radically new products or services.
Automating operations and processes.
Delivering personalized customer experiences.
Once you formalize your AI use cases, you can then specify the data required for those purposes.
Create Data Processing Guidelines
The next step is identifying data sources, data governance, and technologies needed to collect, store, process, and manipulate data. Also, it’s vital to consider the privacy concerns surrounding data acquisition, processing, and utilization. Establish clear data processing guidelines to ensure that there’s no room for privacy compliance issues.
Define Errors and Data Quality
Some of the data may need to be cleaned before use, so define data errors and set up data quality standards as required.
Several ML models are available for implementation, and none of them are all-weather solutions for every business problem. To extract maximum value from your AI, you must adopt an ML model that delivers the best results for your unique context.
Select the Right ML Model
Start your project with a suitable machine learning model. You can arrive at the right ML model for your purpose by answering the following questions:
How do we deliver our AI projects?
Who owns the AI project – Analytics Group, Automation Group, AI Center of Excellence, etc.?
What are the next steps?
Who is responsible for delivering each action, task, and milestone?
What workflows should we follow?
Evaluate the ML Models
You can compare different ML models based on your organization’s context and business applications. For instance, accuracy can be one factor to consider. For a self-driving car, 99% accuracy is far from desirable. On the other hand, 60% accuracy is more than adequate for a movie recommendation engine. No matter what ML model you choose, it comes with a unique set of tradeoffs. So, understand them before you make your choice.
Define Workflow for ML Model
A typical ML model workflow is divided into three steps. The first step is Data Engineering, which includes data collection, data pre-processing, and building datasets. The next step is ML Model Engineering, which includes model training and refinement, and evaluation, and deployment to production. The final step is Code Engineering, which integrates the ML model into the product and includes deployment to production.
Keep Iterating Over the Model
Once you implement your ML model, keep iterating over it until you get the desired results. Perfecting an ML model requires vast amounts of high-quality datasets. Also, the ML algorithms may have to be tweaked to optimize them and improve their accuracy. So, spend sufficient time getting the results you desire from your ML model instead of rushing through a half-baked product.
Models and Results
A successful AI-first strategy delivers continuous benefits for the long term. However, the AI initiatives may lose momentum if they take too long to deliver results. There are some ways to overcome this obstacle without compromising the quality of the AI’s performance:
Leverage Agile to Deliver Iteratively
The priority must be on the quick realization of AI’s benefits and win over the naysayers in the organization. Naturally, an agile delivery model is vital to the success of an AI-first strategy. An iterative delivery of results consistently exceeds the organization's expectations and obliterates internal inertia to AI adoption.
Establish a DevOps Mindset
A DevOps mindset can drastically improve the rate at which AI delivers business results. By racking up some easy wins early on or by providing a high-impact result, the AI team can kill any latent skepticism lurking within the organization against the AI.
The DevOps mindset makes the AI team far more responsive to the organization's needs and the market.
Take an Approach of MLaaS
In the future, AI technology should be accessible and easy to use for the entire workforce within the organization. Ideally, an ML-as-a-service model slashes the need for coordination with the AI team and empowers the other staff to use AI to suit their business use cases.
Metrics and Measurement
A transformation project’s success should not be measured on the subjective analysis of its performance. Doing that would expose it to unfair criticism by internal agents who are resistant to change. Therefore, the success of AI must be measured against the predetermined metrics discussed in step 3. If the AI fails to meet the expected results, iterate on it until the desired quality levels are met.
An AI-first strategy is a continuous improvement project. No matter how much value your AI initiatives offer and how many problems they solve, there’s always room for improvement. By educating all stakeholders and inspiring them to think of ways to solve business problems using AI, your organization will be able to progressively leverage AI across a wide range of processes and contexts, ultimately gaining a competitive edge in every aspect of your business activities.
AI-first strategy is not an IT project. It’s an organization-wide transformation in mindset and approach to business.