From the Leaders’ Desks
Artificial Intelligence (AI) will transform companies, industries and businesses. That is the future, and not a very distant one. Having said that, it’s not current reality either. At least, not yet in a complete way.
AI is a broad area of knowledge which is delivered by numerous promising technologies, tech stacks, and companies. For a business user it can be tedious, time consuming and possibly even never ending to explore the technologies available and plan for the right ones which fit the need.
The following few paragraphs are aimed towards business leaders to help them craft the right AI strategy for their organization using benefit driven approach, sans the hype. This essay is organized into two broad sections, first section aims to bucket the various AI approaches & technologies based on a “value delivered to business” standpoint and the second section offers mandatory enablers for AI to succeed and thrive.
The following classification attempts to evaluate the ROI which AI applications bring to the table based on qualitative factors like maturity of underlying technologies, time taken for benefit realization, amount of effort needed to be invested, possible end applications etc. Understanding AI technologies through the value delivery lens and planning to be on top of the wave when it comes to transform your industry is the key to long term survival and success.
There is no one size fits all approach. Factors, like maturity of the industry, type of company, need to differentiate from competitors, dictate the decision on the blend and the amount of tech investment needed in each of the above categories. But as a general thumb rule – planning for incremental steps on high end ML with mature technologies like RPA, Insights acting as support struts will bolster the org strat plans today and lay strong foundations for a sustained value delivery mechanism for the years to come.
Irrespective of the technology mix chosen, the facilitation of the following factors is an absolute must for a thriving AI practice. These factors can act as blockers or enablers based on how they are treated and applied. Business leaders must ensure that their organization leverages these to their advantage.
For AI approaches to be successful, business units should co-operate for collective action. During the advent of digital media, the most effective teams on Twitter were those who were empowered to interact across the business units – retail store fronts, customer service departments, client DB teams. Similarly AI offers benefits across the org like 360 degree customer view which can be achieved and consumed only via an integrated effort. Teams should also be open to taking insights from, say a data science team, without treating it as “outsider interference”. The processes should support this giving data and taking insights too.
Usually business processes generate data as a byproduct which gets accumulated in silos. Analytics, on the other hand, thrives with deep /wide data, even if it just means bringing up previously unseen cross tabs across data sets. Data collection done with a purpose is sharper in delivering value than data generated as a byproduct.
Also the infrastructure in the organization has to allow access & merge of various data sets seamlessly – A siloed MS Access database of the customer service team & the SAP systems of the finance team have to be equally amenable for sharing and merging with a ML model.
Not a scientist or an analyst or a technical team lead, get a thought leader who can transform a business situation into an analytical use case. A leader who can carve out a path with a pipeline of projects would be crucial for a thriving analytics team and value adding to the business. This is not to say execution is of the least priority but if the plan is to build a team then the first hire should be someone who can bridge both sides – business and data science, because execution is something which can still be outsourced and overseen but a business problem to use case creation has to be internal to really shine.
This can’t be stressed enough but AI & analytics initiatives have to be top down and have to be incorporated into the strategy tagged to a business benefit. Bottom up approach / free hand exploration of data are welcome but should not be the mainstream way. A non-directional insights initiative will deliver value which might or might not be consumable by the business. An initiative driven top down also engenders a collective vision for all the teams involved.
AI brings together various elements – business data, public sources, internal teams and external partners. There will be real value in sharing data for analysis & monetization but creation of frameworks and agreements which take care of privacy of the data , anonymity of parties involved and further propagation or use of the data or the insights would be critical.
An internal pilot for any large project, especially if intended to face the end customer is always a safe bet. If a customer service bot is being built to support or replace the customer support team, build & deploy as an internal help desk bot which can support internal FAQ for prolonged testing under near real life scenarios. Pilots will warm up the teams for usage, allowing them evolve mechanisms to cope with this new supporting bot colleague and will aide easy adoption and further advocacy.
Here is how an ideal strategy blend would look like with 50-60% of the portfolio delivering immediate value starting from as quick as 3 months and the rest of the portfolio structured to deliver long term value appreciation.
Gramener AI Labs is specially designed to focus on the latest state-of-the-art technology and solve complex business problems with data. Gramener’s AI and Machine Learning consulting stays abreast with the latest research in the field of AI, ML and Deep Learning and uses it to help businesses achieve a competitive advantage with innovative AI Solutions.
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Nice article. Great information shared by the author.
It's true that Artificial intelligence will change businesses but not immediately. It's taking the time.