Organization Structure and AI : What it means to you

Anil Yadav
4 min readAug 5, 2020

Beginning of last decade, a brain storming session suggested simple to complex algorithm for product recommendation engine for a web commerce portal. Also the team discussed how similarity in the products should be suggested for a buy. Fast forward in 2020, we have AI that can recommend product using many dimensions and not just what it was programmed to.

During that time, most of the data was structured and unstructured data was difficult to make use of. As 80% of data today is unstructured unlike earlier where it could not be utilize , AI today has opened the doors to make a sense out of this on multi dimension scale and in quick time.

So what this means . Is it simply a signs of the technologies, processes, business etc to come. Considering AI’s capability , it’s no surprise that its affecting the way business is done. AI is already shaking the traditional business processes , improving efficiency and touching a whole wide range of fields.

Thus the question remains, is AI changing the business model or it’s just an enabler. Let’s explore some functions being touched and what it means to be a professional in an AI world. No questions if all of us one day have to be AI practitioners.

Org Structure: For AI adoption, Business must be prepared to make Org Changes for real time decision. Traditional hierarchies ie Sales, Marketing, CX teams must share seamless experience. Logistics, NPD, Finance etc needs to embrace analytic tools for centralize and customisation dashboards for Org wide KPI.

Most of the startup have flat hierarchy, AI led companies have cross functional teams rather than silos that slows down decision making. A AI led Org structure deems C level executive from business, IT , Analytics leaders with respective accountability

CX: This is the most basic. “Chatbots” and “Virtual Assistants” are capable of a human-like conversation with the customers via text or audio. With the advent of more intelligent bots, solving complex problems in next on the list. The CX insights are sourced from additional sources of big data, IOT etc.

CX Analyst , CX data scientist (behavior Model), data miners , ML Architect are some of the roles which intersects AI and CX

Marketing : To target new customers , an AI system can quickly process large data from disparate systems for customer insight in real time. Web Scrapping tools scraps large unstructured data from social media , text PDF , xml etc and can help to predict market trends, helps in new customer acquisition and win over competition with offers targeted to the needs to a specific customer.

A marketing data scientist , digital marketer , campaign managers, campaign designers etc will compliment marketing function in new age business led by AI. While AI will help in customer retention, supported with IVR etc , for new business , marketing insight will help marketer to large extent.

MR : For small or large business, a Market Research activity with AI is used for new Product Development, check customer preference , pilot small batches of product , predict treads, forecast demand . MR+ Big Data + Predictive Analytics is new field for better decision making.

A big data developer, big data architect, analyst, administrator with data scientist (Deep Learning ) expertise can help in A/B testing, experimentation and ML algorithm design.

HR: AI uses NLP to make candidates assessments based on expertise and aptitude, assessment on new age skills such as empathy and likely success of candidate in the roles and can predict effectiveness of the whole process. Small companies are using AI extensively, Hiretual uses AI and integrates with Google/Gmail and helps to build persona of a candidate.

Business Data Analyst, UX designers, HR Insight Analyst etc are some of the roles to be vied for

Financial Services: Financial advisors uses AI tools for increasing assets, helps to retain clients and especially helps in Risk identifications, management, helps in client retention and growth with smart analytics. These are likely to be replaced all together with AI, Bank tellers, cashiers, Tele operators,Call center employees. financial advisors, financial analyst.

New roles that are emerging are , Designers, AI scientist, AI backend Engineers, PMs , AI Auditors, Data Portfolio Managers , Conversational AI Content Strategist, Digital Product Consultant AI, Quantitative Analytics Consultant — Decision Science and AI Financial Crimes, Model Validator etc.

Risk Management : Companies can use predictive analytics to mitigate risk and make more informed decisions. regulatory Reporting, Real Time Fraud Analysis , Market Risk, Credit Analysis , Trade surveillance etc are some of the application of AI for risk management.

The emerging roles are Data Manager GRC, Quant Analyst, Data Scientist, Julia Programers ( a language for analytics)

AI COE: There are some proponent of a dedicated COE within an Org with cross functional teams utilising the COE . The same is not discussed here as its a functional which most are aware off.

Final Thoughts : It’s predicted that by 2023, 35% of workers will start working with bots or other forms of AI, requiring company leaders to redesign operational structures , processes, metrics, and recruitment strategies. Adoption across BU , geographies is highly variable, although AI is becoming ubiquitous almost everywhere. This require structural changes to the mix of skills employed ie both functional and technical but the landscape will change drastically in the years to come.



Anil Yadav

Not a geek but interest include one , i write on practicing work that genuinely reflects the experience | Runner | Avid Walker