How AI Can Help Grow Your Startup

By TechFunnel Contributors - Published on July 12, 2021
Article explain how AI can help to grow the startup busniess

Artificial intelligence is the buzzword in business today, much like digital marketing a decade ago. It is taking the business world by storm. With its ability to automate mundane tasks, and drive efficiency, every bit of attention it gets is well-deserved.

Yes, the benefits are known to most. However, the question that lingers in the minds of most founders and CEOs is, “Well, how exactly AI is going to benefit our business?”

Here, we unravel the mysteries that cloud your mind about AI and its relevance to business. We have listed a few ways you can use AI right away and see the magic unfold. Hang on tight!

( Also Read: 12 New Trends in AI to Know )

How can AI benefit your business?

Let’s understand a few instances where AI can help transform your business for the better.

  1. Making automation efficient

    Over the past couple of decades, businesses have been moving towards automation. The need for greater efficiency drives this shift.

    Even as automation has improved over the years, it still lacks intelligence. It works by a set of rules, which are hard-coded into the system. Any deviation or difference either confuses the software or throws up an error. Here a self-learning machine learning algorithm can make a big difference.

    ML algorithms can organize the data by themselves, learning where specific types of data belong. They are also quick to spot anomalies and deviations, making automation far more efficient.

  2. Improving the effectiveness of programmatic advertising

    AI in programmatic advertising helps you target the audience who are more likely to convert. It also ensures the product-message-consumer fit by showing the relevant ads to the right set of people.

    Facebook and Google use AI to learn and group consumers based on their behavior. Several businesses today leverage the power of AI by advertising on these platforms.

  3. Unlocking the potential in consumer data

    Customer feedback comes in from different sources and levels in any organization – call centers, retail outlets, customer complaints, and the top management. Understandably, these would be documented in varied file formats and might also contain a few (human) errors.

    To draw intelligent insights, you need to collate data and rectify errors before analyzing it. If you are dealing with spreadsheets with virtually endless rows and columns, manually sorting it could easily take days or weeks.

    Machine algorithms can identify data and group them under different labels with little or no supervision. Natural language processing or NLP can understand the feedback from the information entered in the spreadsheets. It can also analyze the call records if need be.

    So, AI can help you organize data and process it to provide actionable insights. It can help you unlock the potential that remains scattered and hidden at different locations in your organization.

  4. Adding accuracy to planning and forecasting

    Business plans and annual forecasts often use historical data for making future predictions. Most small and medium-sized organizations take into account a few factors like seasonality, trends, and guesstimates to make a forecast. However, it’s far from accurate as it depends on a lot more variables.

    Let’s understand this with an example of the forecast for a fashion retailer.

    For instance, if the weather department has predicted a colder than usual winter in a region, the sales of winter wear may change drastically. It would impact demand for both style and quantities.  Other factors could be the emergence of a new competitor or discounts provided by e-commerce players. An ideal forecast, hence, must take into account several internal and external variables. The sheer number of variables could make manual forecasting a daunting task.

    The ability to analyze a large number of variables is an AI strength. AI-based demand forecasting solution not only helps make accurate forecasts, but it also cuts down on the hours spent on the forecasting exercise. At the same time, it provides a logical explanation for the numbers predicted.

    Startups, in particular, need simple but more comprehensive tools that can help plan better. Tools like lean startup canvas can prove to be immensely beneficial if you’ve just started up.

  5. De-risking contract lifecycle management

    As a growing business, you would have several partners, vendors, clients, and other external parties working with you. Undoubtedly, you will have a contract with each one of them defining various aspects of the association.

    Right from the initiation to negotiation and finally signing, a contract goes through multiple changes. Contract implementation also involves many moving parts. With too many concurrent agreements, ensuring the sync in legal and operational elements becomes tough.

    For example, if a contract demands “n” deliverables to a client, it is not uncommon to find the number changing during execution. On the other hand, there could be benefits associated with early renewal with certain vendors, which might remain unutilized.

    Often such problems occur because of the lack of an intelligent system that stores and analyzes contract data. The unstructured form of contracts makes it impossible for conventional databases to highlight the value locked in them.

    Using an AI-powered CLM solution, you can get timely alerts about implementation, deviations, and renewals. It helps you tap into the full strength of contracts. As an additional benefit, you can avoid legal hassles and maintain a transparent relationship with your business partners.

  6. Making the hiring process seamless and data-driven

    If the legacy systems have conclusively fallen short in one area, it is their ability to understand humans. Hiring is one of the best examples where this understanding of humans is required. Particularly with diversity becoming one of the central themes of hiring, algorithms with hardcoded rules don’t work anymore. Manually finding the right profile from a large pool of applications isn’t an option either.

    AI, with its self-learning capabilities, can help solve this problem effectively. Recruiters can use the data points from their existing high-performance employees to find suitable candidates from a pool for a given role.

    Recruiters can save time and effort of sifting manually through a ton of CVs. On the other hand, it would help avoid the filters of legacy applications that reject a qualified candidate based on superficial markers.

  7. Beefing up cybersecurity

    With more than half the world working from home and carrying out all activities – both business and personal – online, cybersecurity has become a challenge.

    Businesses with sensitive customer data have always been on the radar of cybercriminals. With the entire workforce moving online, they are all the more vulnerable. Keeping a constant check using legacy systems is a challenge. Consequently, organizations are now looking for intelligent solutions that can prevent data security breaches.

    Artificial intelligence-based solutions can detect anomalous behavior, regardless of the breadth of data. It can block suspicious activities and also provide alerts in real-time. Hence, AI is turning out to be an essential component of modern-day cybersecurity solutions.

How do you choose and implement an AI solution?

Now that you know the various benefits of AI, the next step is to identify and implement the right solution. Let’s dive right in. Here’s how you can go about it:

  • Identify the problem and define the objective: Without a clear objective, you cannot define the solution.
  • Define the solution and features: Once you have a clear picture of the ideal solution, the build or buy decision becomes easy. Evaluate the available solutions in the market and your internal capabilities. Get the solution that best suits your organizational needs.
  • Be aware of your capabilities: Ensure you do not spend time building a solution already available in the market. Unless you have significant cost benefits or can develop a superior one, reinventing the wheel does little good.
  • Collect and clean the available data: You find that data is often distributed across different locations and levels.
  • Run a pilot project: Before you go all out and deploy an organization-wide solution, run the AI application on a small data set. Alternatively, you can try it on a limited number of functions of your business. Based on the initial response, make necessary corrections and expand the scope in incremental steps.

At each stage, make sure you have buy-in from all the stakeholders. Clearly defining the problem and scope of the solution helps you avoid speculations and fears that AI often inspires.

TechFunnel Contributors

TechFunnel Contributors | TechFunnel.com is an ambitious publication dedicated to the evolving landscape of marketing and technology in business and in life. We are dedicated to sharing unbiased information, research, and expert commentary that helps executives and professionals stay on top of the rapidly evolving marketplace, leverage technology for productivity, and add value to their knowledge base.

TechFunnel Contributors

TechFunnel Contributors | TechFunnel.com is an ambitious publication dedicated to the evolving landscape of marketing and technology in business and in life. We are dedicate...

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