Get Set AI: Fast-track your company’s transformation

Anand Ramanathan
Inscripta AI
Published in
5 min readNov 29, 2019


By: Team Inscripta

Photo by Nik MacMillan on Unsplash
Photo by Nik MacMillan on Unsplash

The term Artificial Intelligence (AI) has caught the imagination of policy-makers, businesses, journalists, and even the lay public. The potential for an “AI revolution” that, in equal parts, promises and threatens a transformative impact on how we live and work, is forcing businesses to integrate AI into their practices and products. Even as many companies are looking to transform themselves through AI technologies, they face significant challenges with respect to choice of platform, talent acquisition/up-skilling, effective strategy, culture, and so on. In fact, talent acquisition is considered to be one of the foundational barriers to the adoption of AI. Business leaders often need to make a difficult choice: executing through an in-house team versus working with an external partner. While this dilemma is well understood for technology in general, AI-based transformation has its own nuances. In this article, we explore the scenarios where it is more effective to engage with an external partner, and the factors that play a key role in your decision.


1. Medium to big enterprise looking to create an AI strategy

At the beginning of your AI journey, it is important to:

  • zero in on the aspects of AI most relevant to creating a competitive advantage within your industry
  • foresee the ways AI will interact with your current workflows
  • understand data requirements and measurable objectives
  • create momentum and confidence by executing a few pilots

Hiring a large team or creating detailed strategy roadmaps before these steps could lead to misalignments at a later stage. For example, you may invest in a platform unsuitable for the solutions you wish to build. Or you may end up hiring a team without extensive skills in the specific AI areas most important to your needs. An external partner can help you not only with the steps above, but also with building an effective in-house team.

2. Startup with an AI product idea

To convince investors and initial customers of your groundbreaking product, you need to:

  • validate your idea through quick experiments
  • create a working prototype that shows the feasibility of the key AI components
  • hire a small, competent AI team

While working on these as a startup, you don’t often have the luxury of hiring a rock-star AI team upfront. It may be tempting to cobble together something using off-the-shelf tools, but the downside is that a hackish approach may lead you to an incorrect feasibility assessment (e.g., AI doesn’t work for this problem), a technological cul-de-sac (e.g, tool incompatibility), or a solution that turns out to be a dead-end in terms of improvement (e.g., built around an over-engineered toolkit). An experienced AI partner can help you make prudent choices right at the beginning, and create a framework that your growing team can pick up and use to set their direction.

3. More mature AI company (startup or enterprise) with a niche requirement

You are further down the road in your AI journey, and are faced with a very specific solution requirement. With a settled AI team, it may be better to build the solution in-house, except when:

  • Your team does not have expertise in the specific technology sub-area in question
  • Speed is of the essence — market needs dictate that you look for a ready solution

In such cases, a specialist AI consultant can help plug gaps quickly, at a relatively lower cost.


Regardless of your organizational context and strategy, there are some basic elements that bear on your build vs consult vs buy decision. Here we summarize the key factors:

  1. Hiring: Getting on board good AI talent at short notice is a big challenge. A common myth is that the ubiquity of AI platforms and open-source tools makes hiring good, specialized talent less critical. This is certainly not the case, if you want AI to be a competitive differentiator for your business. Complementing your in-house team with the right external talent can provide a huge impetus to your efforts, especially for a fast-moving field such as AI.
  2. Requirements and Feasibility Assessment: A plug-and-play approach to AI integration is often the bane of transformation projects. To move successfully from a pilot to a core business technology, various steps need attention: proper formulation and experimental setup, assessment of data requirements, understanding of trade-offs (e.g., extent of automation vs accuracy), and considering the impact on existing workflows. It is crucial to get early inputs from someone who can see the bigger picture, and understand how value can be created in your organization through AI.
  3. Quick Wins: While it is important to look at AI as a long-term investment, at the beginning of the journey it is critical to gain momentum and confidence by executing a few working prototypes or pilots. It is common for organizations to create extensive roadmaps and data integration pipelines upfront, and then get frustrated with the long wait to see value. Working on a few pilots helps organizations understand the impact of AI better, generate buy-in from other parts of the organization, hire strategically, come up with good starting points for implementation, and iteratively improve their long-term plans.
  4. Sense of control: With an in-house team there is a sense of control due to the possibility of frequent fine-tuning of strategy based on the results achieved. With an external partner, however, you need to define the overall goals, measurable objectives, and plan (to a certain extent) at the outset. The clarity obtained by asking these questions at the beginning enhances the chances of success greatly, and avoids sub-optimal utilization of resources.
  5. IP: In-house development is preferred sometimes due to IP-related considerations. With the recent advances in AI, devising a patentable algorithm would take sizeable time and effort. The real differentiator for most organizations these days is probably the data owned or curated by them, and the best way forward at the preliminary stages, would be to utilize the data in an effective manner to achieve competitive advantage.


Our team at Inscripta is excited by the challenge of helping companies integrate AI into their processes and products with greater confidence and efficiency. Through our focused expertise, long experience with real-world solutions, and pre-built technology components, we can help you leapfrog to advanced, research-based solutions, which can be customized and deployed quickly.

Learn more about our offerings and technology by visiting our website. Get in touch with us at to discuss how we can fast-track your AI integration!