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AI is very well capable of interacting with customer and make them feel personal in each interaction by merging data, context and empathy at scale without crossing boundaries of trust. Better customer interactions with less customer efforts and high CSAT scores will definitely help the business expand Customer’s expectation from any service are · Quick response on any queries raised · Receive accurate response on queries raised to avoid asking repeat questions · Faster query resolution with minimum interactions · Easy to interact with and have a sense of personal touch by letting them feel valued, heard and understood · Expect trust on data privacy AI helps in better interaction with customer by: · Collecting the context with consent by pulling required data keeping in mind not using hidden data · Knowing customer better and in depth by building customer profile using data from past purchases, past interactions or support tickets, browsing history, etc · Use smart prompts for Bots that works on prefilling summary and also acknowledge earlier context showing respect and care · Feel customer valued by predicting needs and suggesting next steps that will help query resolution prior customer asks the question · Switch tone basis customer tonality and empathize basis sentiment analysis that reflects customer sentiment For example, Account Payable Helpdesk is the function included in Procure to Pay process that handles queries from vendors, internal or external stakeholders related to invoices processed, payments done and PO created. Below are few AI capabilities mentioned that can be used in AP Helpdesk across PTP: · Build AI Chatbots for automated query handling with 24/7 support and less repetitive tickets · Routes the ticket to required department basis query raised for faster and convenient resolution · Captures invoice data using OCR and NLP from documents or emails · Proactively inform vendors or required stakeholders on invoice status thereby reducing escalations · Resolve complex queries by searching policies, SOPs, etc using intelligent search & knowledge base · Adjust the tone basis customer’s response and urgency thereby building trust · Flags invalid invoices, duplicate payments thereby preventing financial losses

Every interaction feels personal with AI due to many factors. Some of these are listed below. Randomness: One can choose the randomness in GPT to create varying answers, this is done exactly to make the users feel that they ain't talking to a bot. Hence, it gives the interaction a human touch. This feature in AI is defined by the term ‘temperature’. Higher the temperature, more the randomness. Historical Data: The AI keeps on learning from the interactions. It adapts to what the user is looking for and also from the external world. This learning makes the AI feel more and more personal as more interactions take place. Transformers and Sentiment Analysis: The GPTs use transformers to drive the tone, meaning and context of a conversation. They can also understand when someone is angry/happy/sad via sentiment analysis. Both these tools help them be more accurate and adaptive while responding back. Omnichannel presence: AI reads through all the interactions and touchpoints like emails, chats, notes, etc. This makes them more personalised. They really know you well with all this data. This in turn helps AI become as personal as possible. Pattern Finding: We all know AI is super fast at crunching numbers and processing data in this new era of smarter and faster computational power. With the omnichannel presence, AI captures data at each and every touchpoint, but it does not stop there. AI uses that data and its innate computational power to create patterns out of it. Ex: I play songs on youtube while driving to office. But I never play songs when I am home, I only listen to news or watch some reels. Youtube is smart enough to know this pattern and recommend me songs when I am not home and news when I am in my home. When we talk about an organisation and particularly a customer interaction using AI, know that AI has all the above ability to become more and more personal. But, the data points we collect here can also invade his/her privacy. There are some points to keep in mind while utilising AI as a customer answering agent, these are: Data capture consent: All the data points and their source should be made clear to the customer upfront. Also, only data that is relevant to the conversation (not personal) in nature must be captured. Data Usage: Along with what data is being captured, it is very important to inform the customer about the way it will be used. Prompt Guardrails: In the backend of an AI agent one can add some guardrails. Like not inferring finances, health, other very personal things when replying back to a query. Remember, only the relevant data should be collected, which is data required to sensibly answer the question. And true picture about data collection should be presented to customer to add a value without infringement of privacy.

The roots of an organization lie in its culture. To stay competitive, be the innovator and the go-to market brand, organization transformation is needed at a cultural level. For an organization to have this transformation journey, AI can be one of the trusted partner only if used judiciously. Let us debate this with an example. To promote organization wide learning and development, upskilling and efficiency improvement, from a strategy point of view an organization invested on an AI solution. Every month AI report was shared which gave visibility on number of training hours spent at Department level. Say we have Department A with 40 hrs and Department B with 240 hrs. It was but obvious that Department B got recognized. This was misleading as Department A had 5 team members with an average of 8 hrs per spent on training per member whereas Department B had 120 team members with an average training hrs of 2 per member. The feedback was given to the involved stakeholders and right behaviors were promoted. AI workflows were rebuilt to trigger training modules as per Department needs and dashboard was re-aligned to report correct metrics. This was possible because leadership had the visibility to the dashboards and timely actions were taken to make the AI platform more interactive and enabled the organization to promote a learning culture. An average 2hrs of training spent per member moved to an average 8 hrs of training per member within 6months.This also contributed to employee engagement and continual improvement initiatives. For AI to be the trusted advisor for leaders, recommending following steps to be taken by leadership - Creation of Core team - Before onboarding the AI journey, it is crucial to have the buy in and alignment of all key stakeholders. Create core cross functional team having leaders representing domain, business and technical expertise. Strategic Initiative - Leaders are the influencers for setting the strategy. Leaders need to communicate the need and the objective for adoption and adaptation. Corporate to sponsor the strategic initiative. Go big bang with communication. Set the tone at the top. AI goals, KPIs , resources, etc. AI tools to be part of the short-term Annual Business Plan and long term 5-year strategy roadmap. AI awareness – Educate leaders on what AI is and make them aware of its limitation. Leaders to be mindful and cognitive to overcome biases related to cost, technology and speed. Message from leaders to team to maintain ethics and transparency. Building capability – One of the key reasons why AI can be the trusted advisor is for the speed of data availability, accuracy, reliability and faster decision making. Core team to explore and build AI solutions that would meet the business objectives. Core team to involve domain and technical expertise for bias in and bias out checks. Few key criteria’s for evaluation could be Cost, Quality, Risk of failures, Flexibility – Scalability, Processing Time, Implementation speed, data collection, processing, storage capability and ROI. Proof Of Concept - Create prototypes, use cases, assess opportunity, prioritize and validate, design, build, test and deliver. Course correct if needed. Calibrate, train and update knowledge repository. Roll out the solution. Communicate – Address myth that AI is people reduction method and promote it as value creation to customers and business. Send organization wide communication when milestones) are achieved. Share failure and success stories. Capture lesson learnt and submit solution in Knowledge repository. Governance and oversight – Management to have periodic governance. Reinforce ethical adoption of AI and compliance to data security and privacy and role based access to daily dashboards for better monitoring and oversight. Reward & Recognize – To promote and encourage continual improvement celebrate achievements and reward all those involved in making and leading the transformation. AI thus can be the trusted advisor for leadership if adopted in spirit and maintaining integrity and business ethics at the root level.