A featured contribution from Leadership Perspectives, a curated forum for enterprise technology leaders, nominated by our subscribers and vetted by the CIOApplications Editorial Board.

IBM Global Business Services

DEBUNK AI adoption for Enterprises

AI is open for business

Let’s move on from the fact AI is not a hype anymore, enterprises are evaluating in all seriousness how they can approach AI. AI as a piece of technology is a disruptor as it sways the pendulum across business and IT, forcing a multidisciplinary organizational consensus. The playbook for AI can’t just come from technology providers in this space; it’s very much an output from the market which uses it.

Realizing AI in enterprises is composite. It’s not just about using your data or getting insights as intelligence, but about enabling them to be integrated in the systems of records, making them actionable and importantly visible to the end user.

The ensemble of being aware of the cloud, data strategy, IT organizations becoming your enterprise platforms where technology companies are service providers, and open source is your library of features to plug and play. If all is new it becomes easy now there are existing applications which needs TLC and modernization.

None of the above is new and all enterprises have clarity in these questions? Are there answers; is there a method/framework to this AI adoption approach? Do we have best practices? How do we evaluate what is the right stack and are there solutions available in the market that offer what and how can one be on top of this emerging space, which startups should I follow?

These unknowns act as an inertia in the journey of AI for enterprises. It’s not prudent to wait till it’s all unraveled. Intelligent enterprises are indeed a huge competitive advantage it’s only strategic to chunk this complexity and move with clear intermittent milestones.

Automate, Augment and Innovate have been the Key strategic themes of adopting AI

So much has been done to run the business the way it is, how to optimize the existing process, modernize, and re-engineer business process is a great example for this is robotic process automation.

Human cognition has a limit, enabling the information workers with knowledge and decision making improves productivity and performance of existing investments, chatbot, service assistance are classic examples. Lastly looking at AI capabilities from the market, technology providers such as natural language processing self-learning systems, reinforcement learning, computer vision finding problems within the organization, disrupt and come up with new business models brings the best of AI solutions.

Some of the must-dos to get started

1) Current state: Having a clear representation of current enterprise landscape the systems, data, integration, services.

2) Data landscape: AI is fundamentally about creating data-driven insights enabling decision more smartly, timely resulting in the best outcome. Knowing the ownership and temperature of data and ability to leverage data which is public and federated, is key to understand the value you might get out of it.

3) AI Experience to end user: AI promises to provide super powers to the end user; it’s about simplifying things for the user, like checking 50 stores for a best price in few minutes, simulating complex financial drivers, ensuring no out-of-stocks by proactively forecasting including current store conditions.

4) Value: Identifying the new-found value through AI is very entrepreneurial, what would be the feature/power beneficial to the end user; a value-to-cost realization allows the creation of a pragmatic roadmap. It’s often possible to fall in love with the capabilities of Machine learning/ AI such as NLP, Visual recognition, conversational interfaces, automation, and knowledge management systems. This is where enterprises act like a startup to build nimble consumable use cases.

AI capabilities are available to enterprises in many ways:

•PEOPLE creating a talent pool of people within the organization who can evaluate and internalize the AI adoption, additionally they can create open source inspired solutions in house.

•TECHNOLOGY AI APIs/ Services from technology providers such as IBM, Google, Microsoft and Amazon.

•PRODUCTS Industry specific solutions such as healthcare, service ticket classification for airlines, Anomaly detection in Trading, Legal compliance in contracts, Product recognition in retail companies with enterprise functional expertise build this end to end integrated solution and startups also play very well in this ecosystem.

•STRATEGY In addition to technology aspects there are many strategy consulting firms who can provide the POV on value and investment and VC firms who also monitor the space and foot print of IP. Artificial intelligence is not a wave which will subside with the hype curve. It started in 1956 and matured over the years not only with advances in machine learning and perception but in computation, data availability and infrastructure. It’s a necessity and integral in how we should be building data-driven intelligent enterprises.

Artificial intelligence is not a wave which will subside with the hype curve. It started in 1956 and matured over the years not only with advances in machine learning and perception but in computation, data availability and infrastructure. It’s a necessity and integral in how we should be building data driven intelligent enterprises.

The articles from these contributors are based on their personal expertise and viewpoints, and do not necessarily reflect the opinions of their employers or affiliated organizations.