During the recent ‘AI. It’s beyond technology’ AI virtual conference hosted by SwissCognitive, together with a diverse panel list – Ria Persad (European Chamber of Digital Commerce, President | Founder & CEO, StatWeather), Antonio Russo (Head of Analytics & Cognitive Advisory, Deloitte) and Umberto Michelucci (Head AI Center of Excellence | TOELT llc, Founder, Helsana) we had an opportunity to discuss the non-tech components of the AI adoption and in the course of the debate a draft framework emerged.
The simple and very initial idea of an enterprise AI adoption framework could include the following components:
- start with a need/problem – the most successful AI projects start with a business problem to solve for, while the most successful AI adoption journeys start with a few properly delivered AI projects which pave the path to the next steps. Therefore starting an AI journey where the true need/problem is, seem key to the future success.
- do not over inflate the expectations – there’s a lot of misconceptions about AI, including several myths, which should be addressed before starting the AI adoption journey, often the AI projects fail due to misaligned expectations, therefore ensuring the alignment is fundamental
- do not underestimate the complexity – AI projects can be complex to tackle, they rely on the data and its quality, they can take more time than initially anticipated, they can also not deliver on the initial promise. Certain level of understanding the matter before embarking on the AI adoption journey is necessary.
- have ethics in mind – there has never been a time when the engineers have such a profound impact on the society and the software & AI products could impact billions of lives. Therefore keeping the ethics in mind is critical. In the course of the discussion we stressed that ethics could be approached at three different levels – #1 it should be embedded into the curriculum of the future engineers during the study times, #2 the organizations which are used to ‘Code of Business Conduct’,‘Data Code of conduct’ type of the documents should look into creating ‘Ethics Code of Conduct’. #3 Ethics should be taken into account at the AI project level as well – designed in
We fully understand that we have not explored the topic in depth that it requires. Some of the dimensions that we have not touched upon are fundamental to any AI adoption transformations, these dimensions include and are not limited to:
- executive sponsorship (questions which will need to be answered include: how to ensure the top down leadership is in place? how to secure funding for the AI transformation?
- cultural impact on an organization (questions: what’s the future impact of the AI adoption on the organization, how to communicate the change? how to ensure the buy in from the team is in place? how to counter the change resistance?)
- people & skills (questions: how is the AI adoption going to impact the existing workforce? Which skills will be needed? How to up skill the team?)
- methods (questions: Which methods have proven to accelerate AI adoption? How to design the implementation methods designed for the particular organization)
The topics of change management and implementation of the framework should be further considered, as they will be the key to the success of enabling the adoption of AI.
Given the initial discussion about the creation of the framework and the post-conference enthusiasm of the panelist team, the decision has been made to come together and work jointly to deliver a more comprehensive framework which will hopefully help the organizations set themselves on the journey to drive enterprise wide AI adoption plan.
Copyright by Kamil Zalewski, Head of Growth / AI Paractice Lead, Unit8
“As Unit8 we’re starting to see certain signals from our customers and partners about the upcoming market rebound. COVID19 spread became the ultimate digital transformation catalyst, driving the need for the adoption of digital technologies as a way for organizations to adapt to the dynamic conditions. AI is definitely among the technologies which will benefit from the ‘new normal’ and the companies which will want to create a competitive advantage will look to accelerate the adoption of AI.”
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