Decipher your AI advantage right now


Artificial Intelligence (AI) is poised to disrupt the business world as we know it. Companies cannot wait any longer to establish their AI strategy and place their bets in an already fast paced disruption everywhere era. Any advantage they can gain with a strong AI strategy, and forging ahead to create, deliver and capture value, without delay, can help build the necessary momentum in the right direction for a rapidly changing environment. As executives develop their strategy on timing, it’s important to recognize that the progress in AI will in many cases be exponential rather than linear. Computer processing power and connectivity among devices is increasing rapidly. The level of investment is increasing rapidly. The quality-adjusted cost of sensors is falling exponentially. And the amount of data being generated is increasing exponentially. Executives of all business from all industries must recognize the magnitude of the opportunity and act now, towards gaining any advantage from AI that they can.

The pursuit of machine’s ability to mimic human cognition, including learning, reasoning, perception, problem-solving and even exercising creativity, began in the 1950’s with Alan Turing. The insurgence of AI in business has been through 2 busts already and lessons learned from those experiences must be reaped for capability building today to ensure success across the current pursuit of benefits from AI. While much uncertainty still persists, to capture value in this growing market, companies are experimenting with different strategies, technologies, and opportunities, all of which require large investments. Having built an AI practice in an environmental engineering consulting practice in the late 80’s through early 90’s during the AI’s last boom, here are some lessons that could be applied to today’s pursuit. Companies should focus on strong solutions that allow them to establish a presence now, rather than striving for perfection. With an early success under their belt, they can then expand to more speculative opportunities.

End-to-end AI-based solutions and use-cases must emerge in combination with other emerging technologies like internet-of-things (IoT), blockchain (distributed ledger technology DLT), augmented reality (AR), etc. While awareness and existing use-cases of these technologies will help defining the solution, identifying and defining the problem area is of primary importance. Executives must appreciate the relationship and distinctions between technical constraints and organizational ones, such as cultural barriers; a dearth of personnel capable of building business-ready, AI-powered applications; and the “last mile” challenge of embedding AI in products and processes. Be prepared to put in the hard work of managing the interplay of data, processes, and technologies that must happen in-house. Develop a portfolio of short-term actions, based on current trends, and prepare for future opportunities by building up capabilities and data infrastructure. Expect to pursue the problems, solutions, testing, piloting and scaling of AI-based applications in phases.

Phase 1: Identify best use-cases & test them for viability

Companies must identify opportunities in context of ongoing business and digital transformation efforts, so that they only add to existing strategic imperatives rather than conflicting with them. Put together small self-organizing squads with strategists, process experts, data-scientists, technologists, and facilitators who will ensure that for every opportunity identified, they pin down the problem statement, requirements, decompose the processes, identify data sources, and technologies needed for the solution developed. Internal and external customer requirements offer crucial guidance in discovering valuable AI uses. An in-depth understanding of developments in AI building blocks will be critical for systematically incorporating technology advances. Rich data pools, especially new ones extended by digital advances, provide another essential perspective, given AI’s dependence on them. Finally, by breaking down processes into relatively routine and isolated elements, companies may uncover areas that AI can automate. These squads must experiment, test and refine various potential solutions and approaches, depending on the variables presented to them and validate the solution for customer desirability, technological feasibility and business viability. These tests will help the organization to gain familiarity with AI and will highlight data or data integration needs. Such tests also bring to the forefront the organizational and capability hurdles—that become critical inputs for the next phase.

Phase 2: Pilot Launch

As viable solutions that have undergone preliminary tests emerge from the previous phase, they must be prioritized for value it can create, deliver and/or capture on the Y-axis versus the speed with which they can be built and rolled out depending on its complexity. Put together agile teams that launch the pilots selected to build the solution using test-and-learn sprints. Identify and cope the data integration projects and other capabilities needed to scale the fully operational AI-based solution. In addition, these pilots will further define the infrastructure and integration architecture needed for the solution.

Phase 3: Scale-up the solution

After succeeding with the pilots, they must be rolled-out at scale across the organization at scale with solid run-time processes and offerings, and building the capabilities such as talent, processes, organization, IT, data infrastructure and right behavior necessary for its success. Executives should be able to develop a functional understanding of the topic, have a clear view of their starting position regarding technology infrastructure, organizational skills, setup, and flexibility. In addition, they should also understand the level of access to internal and external data. Don’t under-estimate the workplace communication, education, and training that will be needed for knowledge transfer and capability building across the impacted organization.

Conclusion – Path forward

The future of AI and how it will be used—including the extent of its potential to shift value creation in a radical way—remains highly uncertain, given the ongoing technological advances and profound disruptions in industries. The best way to combat this uncertainty is to plot out and test several scenarios and to create a roadmap tying together the individual initiatives. These efforts will enable companies to sensibly modify their original plan and address its implications. Meanwhile, they will be more prepared with their data, skills needed, organization, processes, infrastructure and culture necessary for success with it. Your best chance to succeed with AI is to tune out the hype and do the necessary work.

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