“I’m more frightened than interested by artificial intelligence – in fact, perhaps fright and interests are not far away from one another. Things can become real in your mind, you can be tricked, and you believe things you wouldn’t ordinarily. A world run by automatons doesn’t seem completely unrealistic anymore. It’s a bit chilling.”
—Gemma Whelan

Artificial Intelligence has been transforming the world ever since its inception. Sci-fi movies are being the new reality and our Reality seems like a sci-fi movie. With some of tech goliaths dedicated to taking Artificial Intelligence and Machine Learning technology to new heights, they are expanding the scope of automation to the levels that were never believed.

Who would have ever thought drones and robots delivering your packages, robots in the defense field, drones for agriculture and whatnot. Although some fear the impact of automation in the business arena most industry experts have welcomed it both arms open. Technology is not only making it easier for the world to run in the way that is making our lives easier, but is adding to the efficiency and standards of life too.

If you want your business to expand and stay ahead of the best in the market, leverage the power of technology with the right business model to keep yourself ahead in the game. For this, before you decide the right business model, you need to work on the framework of the AI and ML business model.

Framework for Deploying Machine Learning Predictive Models

It is important that before choosing the right machine learning and AI business models, you understand what would be the ideal predictive model and how it can help your organization grow stronger and better. When you decide to choose to work on a predictive model, you need to take care of the fact that the 3P’s of the business world (Product, People, Process) are in line with your objectives of integrating ArtificiaI Intelligence solutions and Machine Learning with your business. Along with this you need to see how data is governed and how data infrastructure communicates with your business.

The parameters that define the right predictive model are:

  • Resilience
  • Trust
  • Prevalence
  • Measurability
  • Advancement

Let’s discuss about each of these aspects in detail here one at a time.


When talking about ai as a service business model the first thing that you need to keep in mind is the fact that the information that is gathered is resilient against any disruption like tweaking or lack of governance. Since your data would be coming from various sources it is important that you take into account the reliability of the data and the way it is processed within your business.


It is important that you work on the data in a way that it is confidential and private to your use. You have a resilient pipeline and along with this you need to make sure that the third party you are collecting data from is trustworthy. Quality is an important parameter that defines the credibility of the data that is shared and the source from which it is shared.


You need to have meaningful and reliable that must be cultivated regularly for newfound insight. If you do not consider the prevalence of the data that is being used in the work it becomes quite difficult for the business model to bring on the table suggestions and solutions that would help you upscale your business to new heights with better results.

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Everything that you do to your business should translate in measurable consequence. This could be in terms of ROI, profit, efficiency, etc. without the ability to use the data to bring measurable consequences you would fail to make the use of technology for your business in the right manner. If it can be measured and compared the scope for improvement and further development continues to grow.


The advancement of your organization’s information and infrastructure is centered on the data, results and the predictions. When considering the Machine Learning business model with your work, it would become quite difficult for the data scientists associated with your business to come up with the right outcomes if the advancements are not recorded in the right manner.

These are the most influential parameters that determine the right artificial intelligence business model that you need to implement in your business. With any of these parameters failing to fit your business framework you would find it quite confusing to bring on the results that are expected and the business model may fail terribly.

Thus, for successful implementations of the business model with your organization’s functioning make sure that you double check the results and the parameters on a regular basis.

There are various aspects of the business that would define the business model that you want to execute with your business. Before choosing the right ai business model you want to consider, let us see the basic business model that we would consider as the base of your research and study.

Bow Tie Funnel to Build AI and ML Based Business Model

The bow tie funnel is the basic business model that we would take as a base to explain the various models that you can consider for your business functioning. The model here talks about the driving forces of the business that need to be considered for the growth of the work.

If you have taken no measures to map out how your customers behave with your business then you surely have lost the battle here. When talking about the strong driving forces that help your business in advanced digital transformation, it is important that they are studied and worked on well.

The bow tie model that we talk about here basically covers the following points:

  • Attract
  • Nurture
  • Convert
  • Engage
  • Adopter
  • Loyalist
  • Advocate
  • Brand Ambassador

These points define how your business should interact with the employees, customers and the business world for the best results.

Recommended Read: Top AI Development Companies

Important Aspects of the Business Model Considered One at a Time

We share here the ML business models that are defined on the following aspects:

Lead: The clients that visit your website to make a purchase.
Customer Retention: The first time visitors should turn to your loyal customers.
Life Time Value: The customer should interact with the business each time they need assistance
Employee Retention: Hold your assets to make sure the future of your business is secure and results well.

Let’s now take one model at a time and see what technology has to offer for your business.

1. Lead Conversion Model

The first model that we would discuss here works in the bow tie model.

The model in the business world is recognized as a passionate and sensible trigger of clients who have a higher penchant to make a purchase. The information collected and analyzed fuel the business strategies that would return more customized and personalized messages. The fact is, more than 50% of the clients want to offer customized responses to their customers and suggest explicit recommendations that can help them solve their problems. To communicate with the customers the customized messages that share the most important information into a lead/opportunity conversion model to foresee precisely what the buyer needs to hear and when they need to hear it. With an opportunity to foresee the truth when a choice is made, methods organizations can guarantee they appear at the perfect time with the correct message while conveying a more customized insight en route. It’s less about being intuitive and more about providing the information. The ai business model canvas is designed by experts for high efficiency and strong lead generation.

As indicated by various studies, a potential buyer interacts with your business only if they have any of the following reasons that hold them on your business:

  • They have a profound energy for the reason
  • They accept the association relies upon their gift
  • They realize somebody influenced by the organization’s central goal

The absolute most significant factors to consider include:

  • Past gifts
  • Territories affected by the gift
  • Relationship to current contributors

When considering the sympathetic model, where you want your business to grow and build goodwill with your customers it becomes quite significant to take a more compassionate position with the business owners. AI models can be developed precisely to maintain a strategic distance from inadvertent and inconsequential predisposition while driving more grounded results. Outside the boundaries of the business aren’t the places these models hold esteem. In any industry, not simply with small business, the sympathetic position this model requires can help once a client or contributor has effectively made a move with your association and moved further through the tie channel.

2. Attrition/Customer Retention Model

Considering the pace at which the market is evolving, it can easily make any alert CEO have little clammy under the arms. With this evolution, the need to have a stronger customer base has become the need of the hour. It is not only because they drive business, but because they can help you with better results and help you work on the loopholes in your business. With predictive learning being a strong element ai business model you can simply understand the buyer’s churn when a new competitor would join the market. Thus, with proactive strategies you can surely get the ball in your court and be ahead of the competition that is to enter the market.

The first thing that you need to take care of is the fact that with a 360 degree view of the customer relationship you can surely move interestingly to better solutions. Collecting data that spans over the bow tie funnel.

Must Read: Best Artificial Intelligence and Machine Learning Apps Ideas to Startups

Thus instead of just thinking of individual touch points, you’d be getting a better idea of the end-to-end journey or build buyer persona that helps you understand their needs better. A marketing team at a renowned organization made use of this technique and swoop into meeting customer needs at once. If you think working on an idea of your own is difficult then in that case you can surely think of some better ideas where you come at the situation from your purchaser’s perspective, it’s important that you put in the difficult work of being sympathetic.

In the wake of looking all the more carefully at the inclination to stir in the main year, and at which months had the most noteworthy pace of takeoff, they understood that they expected to plan something for wow clients around the seventh month of their agreement. Along these lines, in the seventh month, they called clients all of a sudden and offered a rebate on the following month’s administration. In the 11th month, they offered a free extra.

Moreover, with ai business model innovation the organization realized how personal vermin control could feel on the grounds that their clients were inviting them into their home, so they prepared their staff to approach those opened entryways with deference. They urged them to exceed any and all expectations by getting vacant garbage bins from the control, getting the client’s paper and then some. These contributions were exactly what clients should have been consoled that they made a difference to the organization — and every one of these endeavors paid off.

3. Life Time Value Model

The next model we are to discuss is centered on customers and employees. It is important that we can gather intelligent data around the customers and employees experiences with your business and further map this data into visual representations like radar graphs and innovation and idea matrices. Some businesses follow traditional methods to collect data for analyzing and understanding the reactions and loopholes in their business. Well, to be honest you miss a lot here. With traditional methods the data collected isn’t efficient and may leave you behind your competition.

Your team can map quantitative and qualitative data collected after conducting the shoulder-to-shoulder interviews of your potential users who are able to shift how you would build your product even before applying to any working model.

Must Read: Ways AI and ML are transforming Fintech Industry in Unexpected Ways

With questions centered to your audience and employees it becomes quite important that you work on myriad variable that could be focused on various stages of the bow tie funnel that includes the cost of acquisitions, offline ads, promotions, sponsors, discounts and more. The research data collected here is quite myriad variables to consider at each stage of the bow tie funnel, including acquisition costs, offline ads, promotions, discounts and more. Even then, these are very quantitative data whereas much of the heavy lifting comes from mining qualitative patterns.

When data scientists working with your team reach to you for the interesting solutions all you need to do is focus on the right set of questions that you used to gather the questions. The machine learning business models should be designed in a way that it can spot points where strategic changes are to be made for better returns. Include empathetic questions that can guide you through the journey and help you understand how and where things for your customers may go wrong. With ML and AI business models at your assistance you may find some biases that can help you infiltrate through logic and drive the data towards what your consumers actually want.

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To overcome general human biases in making strategic business decisions a lot of organizations rely on the LTV business model to predict what results in the highest consumer engagement. By designing a machine learning model that is highly insight centric you can avoid general human biases and pass one of the largest pitfalls of using data in an absolute in taking decisions that would define the future of your business. Machine learning can analyze, categorize, sort and arrange your data in a manner that you always have data available to make certain significant decisions.

4. Employee Retention Model

This is a strong model that you need to consider for your organization as a little investment now would yield great results in the long run. It hardly matters how big or small your team is, employee turnover happens at a much higher rate than expected. With cross employee experiences comes cross customer experiences that results in cross pollinated experiences and thus paving way for stronger competitions to enter the market. In terms of correlation, one can easily map out the areas where there are issues and the spots that turn to friction points for the brand and further work on them.

With an in-depth study of these pain points this element ai business model can rectify them to match up to the industry standards and decrease the possibility of employee churn rates along with strengthening the most significant areas for employee retention. All this can be automated by simply integrating sophisticated and intelligent data looping system that can help in analyzing each layer of the organization efficiently and effectively. The traditional method talks about annual reviews and general one-o-one discussions to determine employee engagement and identify the pitfalls in the process.  Although the approach seems ideal, there is one issue with it. By the time the admin department would collect all the data, it sometimes becomes too late to respond.

The major reasons that we hold in the way during the analysis are:

  • Monthly income
  • Overtime
  • Age
  • Distance from home
  • Total working years
  • Years at the company
  • Years with the current manager

Considering these aspects there need to be designed solutions that can easily deal with the scenario. With predictive analytics working on the data collected with these aspects, organizations can easily leverage machine learning models to understand the patterns of highest possibility of the churn rate. This helps to hold the functioning of the human resource department. With Human Resource Management Software to automate most HR related tasks, analyzing and evaluating the data and finding the major causes of employee turnover becomes quite easier.

Read Also: How Artificial Intelligence and Machine Learning Help in Building Employee Force

With the right information from the employees joining and leaving the organization the HRD can easily work on the loopholes and increase employee retention rate. With this information the department can plan a strategic model that would help in crafting a plan where low, medium, and high-risk employees can easily be retained. Having data that can help you design a business model that can help you hold your experienced and valued employees can easily be pictured with machine learning for your assistance.

Artificial Intelligence and Machine Learning Making the Journey to Success Easier

When talking about artificial intelligence and machine learning in the first place, the businesses are making it quite interesting for employees and customers to engage with the task. These four models may seem a little complicated when it comes to holding your organization at a place. Just implementing these solutions and calling it a day is not enough. Once built you have to interpret the results and disseminate the knowledge from business models in your organization. Especially considering the point that the world that we live in todays is a silo wall that holds internal communication. Data scientists and Analytics make it a point that they explore the gap between bleeding-edge science and your business’s ability to explore the benefits of the AI and ML models that we have talked about. It is also important that these implementations are closed in the right manner to come up with an interesting solution and have a viable solution that manages the insights available.

Our AI and ML solution providers can help you leverage the power of predictive analytics to expand your business to new heights with artificial intelligent at the core of business operations. Although this may be seen as a far off dream, but over the years technology has become so immersed in your business that it helps you take the most important decisions of your business without much human intervention and biases.

Design Team Lead

Jitendra Badgujar stands out as a tech blogger due to his commitment to staying current with the fast-paced tech world. He diligently researches & explores new advancements, ensuring his readers receive accurate and timely information. His dedication keeps his content accurate and relevant.

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