Q&A WITH ASHISH SHEKHAR RE SUSTAINABILITY
Q&A WITH ASHISH SHEKHAR RE SUSTAINABILITY
QMany companies rush to adopt digital tools, only to find ROI doesn’t follow. Drawing from your time leading transformation at Re Sustainability, where do organisations often go off track, and how can they realign digital programs to ensure they're delivering meaningful value?
Ashish – ROI does not follow in most cases. The primary reason is wrong selection of tool which does not outperforms human intervention and results in additional cost. In AI 95% cases (Supervised learning) AI will work when the algo is trained on your data (unless it's too generic) and tested to outsmart human. Instead, what actually you get is readymade algo which suits Saas based solution sellers business needs. It will struggle to beat human and as a result you pay for both algo and human.
Example – A pre trained predictive maintenance algorithm won’t be able to predict machine failures in advance. It will be an unsupervised algorithm which will give you some hint on the anomaly which may lead to potential failures. This comes with a large number of false positive cases. Ultimately you deploy the algorithm and also do preventive maintenance on weekends without any significant improvement in the process.
QYou've overseen digital strategy through roles at Makson Group, P&G, and now Re Sustainability. What’s one strategic decision or process shift that reliably turns digital projects from “nice to have” into “must-have”?
Ashish – The major strategic decision or process shit is commitment of top management to digital transformation and clear understanding of where it will work and where it won’t. 99% of top management is never averse to digital transformation but its unsure of the value part which ultimately leads to low adoption and growing resistance.
QAt Re Sustainability, tech must serve both environmental mission and operational efficiency. How do you ensure technology investments align with both sustainability goals and tangible business performance?
Ashish – Technology can be a great enabler when it comes to achieving sustainability goals. We identify use cases where technology can lead to overall process improvement and outsmarts the manual process. Some of the examples can be digital control tower monitoring sustainability scorecard and key ESG KPIs. Use of Computer vision based robotic process automation for waste segregation.
QDigital transformation often stalls not because of a lack of tech, but because of people or processes. Based on your experience, what cultural or organisational bottlenecks tend to hold companies back, and how have you tackled them?
Ashish – The best way to tackle them is to ensure 1. 100% alignment of top management 2. Initial awareness session on AI – this should never be generic but specific to industry, clearly explaining how digital transformation a game changer and risks of can be ignoring it. The objective should be to get maximum buy in from all key stakeholders. 3. One must also consider that the product should must address key business pain area to reduce chances of low business adoption.
QWith limited resources and high expectations, companies need visible results fast, but also sustainable gains. How do you balance early wins and deeper, structural change when you're leading transformation?
Ashish – Digital transformation should always be looked from investment perspective and when the objective is achieved. The cost and resources should never be a constraint as the benefit are much more than the cost. Early wins reinforce the entire idea and provide major validation to facilitate fast adoption and contain change management issues.
QAs CIO, your role extends beyond tech execution, it’s about translating complexity for leadership. How do you help non-technical stakeholders understand and support transformation efforts that might only pay off later?
Ashish – A CIO should not just have tech exposure but a seasoned expert on the technology and must have a clear vision of its impact on business. He should speak the language of stakeholders, use analogies and co create the shared vision.
QIf you had to offer one practical insight for other CIOs or business leaders trying to close the gap between simply adopting new tools and really extracting value, what would it be?
Ashish – Analyse the solution in depth from technical and cost benefit perspective. Ensure it’s solving a business pain area. The final outcome (assuming smooth business adoption should add value to the company in terms of time and cost saved or profit maximized.
Example – if a vendor offers a computer vision-based algorithm to auto detect if the workers are in right dress when the come to office.
Claim – it will auto detect and eliminate need of human detection and save time and cost.
Expectation – Accuracy should be 99%+ as computer vision based algorithms are suppose be super accurate when trained with right data.
Reality – Vendor offers a ready-made algorithm and claims that accuracy will improve with time. Intially this will be around 80%.
Analysis – This is one of the major issues with solution sellers. In order to maximize their profit, they prefer pre trained algorithms which lacks accuracy and ultimately results in human and algo both. So cost is increased with negative value.
Verdict – Go for a custom-built algorithm on your company data to ensure 99% accuracy and value for the company.
