Steps to becomes an AI classified Organisation

With the recent advancement of AI, it has become imperative to adopt AI in order to sustain in a competitive market and have an accelerated business growth. Here, we will discuss the very basic steps to develop an AI mindset and approach the problems with AI specific solutions.

4 Driving Forces for AI adoption

1. Less Inefficiency = Cost Reduction
2. Predicting a system’s failure in advance makes the system less inefficient. When you automate to eliminate inefficiencies and reduce human errors, in the long term, the savings in costs add up, providing a boost to revenues.
3. Personalization = More Revenue

• According to McKinsey, 35% of Amazon’s revenue is generated by its recommendation engine alone, which Amazon uses in email campaigns and throughout its website.
• Netflix estimates that only 20% of its subscribers’ video choices come from search. The remaining 80% comes from recommendations.

1. Personalization has the power to do wonders to boost revenues, and AI makes it all the more possible.
2. Intelligent Insights = Innovative Products
3. AI-assisted decision-making can help in various facets of your business. From finding new product ideas to understanding how to improve existing processes — all of this can have a significant impact on revenues and cost savings.
4. AI-Driven Innovation = Upselling

• 14% of enterprises who are the most advanced using AI and ML for new product development earn more than 30% of their revenues from fully digital products or services and lead their peers successfully using nine key technologies and tools.
• AI-driven upselling uses machine learning algorithms to predict what products or services a customer is most likely to be interested in and recommends them at the right time, resulting in increased revenue and customer satisfaction.

Now, the question is how to maximise AI success? Adoption of AI in the respective business and strategies to maximise its success is the responsibility of top level executives.

Actions for Top Level executives

1. Understand AI: As per the industry research done by Garner, 85% of AI projects will “not deliver”. Instead of taking AI as a blackbox, Business leaders need to understand
2. Kind of business problems those can be addressed with AI.
3. Does the necessary data exist? AI models need huge data. If you have this, then GO for this.
4. Is the problem your product is trying to solve complex? If YES, then GO for this or else the problem can be solved by coding a few dozen rules.
5. Does the problem change over time? If YES, then GO for this or else Rule-based algorithms or statistical analysis can accommodate the problem.
6. Can the solution tolerate imperfect results? If YES, then GO for this. AI solutions are imperfect. because they rely on probabilities. No model will be correct 100% of the time, even after years of optimisation.
7. Will the solution require exponential scaling? If YES, then GO for this. AI capabilities are a good choice if you expect your solution to scale fast and generate exponential data.
8. AI vendor assessment for any ‘Buy’ option for the solution.
9. Required infrastructure and skills set to address the solution.
10. What are all the factors responsible for the predicted outcomes?
11. Address Foundational Gaps: Data is the foundation of AI projects. So, make sure that your Org has sufficient data to initiate the AI project or else gather the data by building the required infrastructure.
12. Be Clear on ROI: Clearly define the benefits that you are going to get out of AI implementation
13. What immediate pain point would the AI solution ease for your organization?
14. What benefits would you see by addressing the pain point?
15. What’s the added advantage of an AI solution over a simpler one, such as a manual approach?
16. Consider Budget: Make sure you have sufficient budget to improve the technical infrastructure to accommodate AI specific projects, budget to hire additional personnel, collect the necessary data and afford engineering support.
17. Be Committed: AI is not quick, nor is it the most cost-effective solution. If time is of the essence or you have cost concerns, don’t start with AI. Keep it as a long-term goal. Determine if, at the moment, you can solve a problem with better software engineering or manually. This also allows you to establish a baseline approach that you can later replace with AI when time or cost is less of a concern.

Find out the division where AI can be applied.

This can be Internal process

AI in Customer Service

• Chatbots
• Sentiment Analysis
• Request Routing and Prioritization
• Voice Analysis
• Data Management
• Multilingual support
• Machine Learning and Predictive Analytics
• Personalised Recommendation system

AI in Human Resources

• Faster Recruitment
• Personalized Learning and Development
• Promotions and Rewards
• Performance reviews
• Employee onboarding/offboarding processes
• Employee engagement initiatives
• Talent development and training
• Workforce planning

AI in Sales

• Creating an Accurate Prospect List
• Predicting Sales Actions
• Increasing Leads
• Sales content personalization and analytics
• Sales data input automation
• Sales rep response suggestions
• Sales rep next action suggestion

AI in Marketing

• Churn Reduction
• Personalized Recommendation
• Uncommon Uses of AI in Marketing

AI in IT Operations

• Preventative Maintenance
• Event resolution guidance
• Learning process flows optimized with machine learning
• Proactive problem resolution improved by big data analytics
• Anomaly detection by flagging unusual repeat incidents
• Identifying security vulnerabilities

Now the question is

1. Which problems should you invest in first (i.e., which are the low hanging fruits)? 2. Should you build the AI solution from scratch or buy an off-the-shelf one? 3. How would you know if an AI initiative is helping your business?

First point refers to identifying AI opportunities, which can be divided into 2 types

• New opportunity: Assess your legacy system with the driving motivations for AI project.
• Efficiency: What is causing the system to be inefficient?
• More man power
• More cost
• More time to address any customer request/issue
• Personalisation
• Is the system not personalized? Everyone likes personalized service.
• Intelligent Insights
• Do we have enough evidence to make the right decision?
• Solution to address existing problem present in the legacy application or improve customer experience
• Is any of the tasks in the workflow a manual step?
• Do we have enough data to make it automated?

Second question is related to ‘Build’ vs ‘Buy’ strategy

AI applications that commonly come prepackaged include:

• Virtual AI assistants
• Facial recognition systems
• Sentiment analysis tools
• Language translation services
• Product recommendation engines
• Speech recognition systems

Before starting for ‘Buy’ option,

• Understand the problem statement clearly and frame it properly
• Test the prepackaged AI application

For a quick search of prepackaged solutions, you can use software search websites such as G2.com and Capterra.com.

‘Build’ strategy

• “Hire” internal data science teams: The benefit of using your internal data science teams is cost and convenience. Additionally, as these are employees from within your company, they’ll already have a good grasp of your internal data stores, infrastructure, and processes.
• Hire AI consultants: When hiring consultants, the arrangement you choose for your implementation project will depend on how much control you’d like to have. For example, you can hire an independent contractor who works at your company full time. In this case, you’re buying their time and you often have control over their day-to-day tasks and deliverables. If they’re on-site, you will work with them as you would with any other employee. Toptal.com and UpWork.com are two sources where we can get many skilled consultants.
• Hire new in-house data science personnel: To be strategic, before bringing on data scientists, consider hiring a strong team of data engineers. They can be instrumental in many ways. They can start supporting your internal data needs, help implement parts of your AI and data strategy, and support AI initiatives whether you outsource, buy, or build internally.

Measure success of AI strategy

Pillar 1: Is the model performing adequately in development and production?

Pillar 2: Are you observing business improvements?

Pillar 3: User Success: Are users happy with the solution with no critical issues from the model output?

When do you measure success?

• Building from Scratch: When you’re developing a solution from scratch, you need to ensure that the solution is working as expected, starting to create value for your business, and worthy of deployment. In this case, success measurement will help you decide if you should iterate on the current solution, fully deploy it as is, or start over. Success evaluation in this scenario partly starts during development and continues through post-development testing (PDT).
• In Production with No Evaluation before Deployment: Success measurement in this scenario will help you decide if it’s wise to keep the solution as is, iterate on it, or start over. Suppose, through evaluation, you find the results to be disappointing. In that case, you may choose to pursue an alternate solution or build a new AI solution with an entirely different set of assumptions.
• Evaluating an Off-the-Shelf Solution AI vendors often publish impressive model performance numbers and persuasive case studies. However, their solution hasn’t been tested on your data and with your employees and customers. Consequently, you’d need to ensure that the solution works with your company data and within your workflow. It’s crucial to ensure that the three success pillars look strong, even for an off-the-shelf solution.
• After Full Deployment After deployment, some of the evaluation initiated during PDT will continue to be instrumental for ongoing success. It’ll help inform your company about how well the AI solution continues to create value for your business.