In September 2017, AIBusiness.org–an educational hub working to bring forward the latest news deciphering the impact of AI in business–hosted AI Summit in San Francisco, CA. The event brought together over 120 speakers, 50 exhibitors, 80 press/media, and more than 2000 delegates in total. The information, strategies and critical success factors which surfaced from executives at leading corporations spanning the globe provide key insights for companies’ journey with incorporating AI.
AI Integration Journey: Rapidly Moving Beyond the Early Adopter
Companies are amassing substantial wins with AI and traction is quickly moving beyond the ‘Early Adopter’ populous. Businesses tend to fall into one of several categories in terms of their integration journey of AI:
AI is a key component of a company’s Digital Transformation program. Employing AI to fuel a holistic transformation of the company’s product/service offerings and operational delivery models.
Employing AI for specific high-yield opportunities or use cases.
Commercial technology providers enhancing existing product suites with AI and/or incorporating new AI products and platforms. Additionally, there a few outliers like Google who are adopting an ‘AI First’ mindset in every aspect of their business
While the items below were identified as critical in all cases, the manner in which they’re addressed will vary based on which category they’re in and where they are in their journey.
Insight #1: Business vs Technology Driven Initiative
AI efforts need to begin and end with an emphasis on delivering business value. The most successful efforts showcased at the Summit were measured in terms of business outcomes and business value. These metrics should then drive solution design, testing, and implementation approaches, ensuring the direct tie of the solution to business value realization. In effort to combine business value with the end user experience, one of the leading practices highlighted a methodology where they vet Key Performance Indicators with end-users before starting any project to ensure the appropriate focus and linkage from business value realization all the way to the end user experience. More broadly speaking, presenters consistently highlighted the importance of business value realization being at the helm of prioritization and governance modeling. Conversely, failure examples cited instances where teams led with operational and technology related metrics. In turn, these examples fell short on business outcome realization. While both business and operational metrics are important, a focus on business value based metrics help narrow the focus on sub-metrics in areas of people, process and technology, and ensure a direct benefit to the business.
Insight #2: People First
The human element was trumpeted more loudly than any other overarching theme. While workforce/talent gaps were cited as a major challenge, many other people-centered topics were discussed as being critical success factors.
First was the notion of required culture changes to enable success. The need for C-Suite ownership, engagement, and actions were highlighted specifically. An example is the imperative to create and encourage a safe environment allowing teams to safely ‘fail fast’, and operate as a learning community. This sentiment echoes other recently available research. In “The Digital Transformation PACT,” a study performed by Fujitsu, 68% of survey respondents cited ‘fear of failure’ as being a hindrance to successful digital transformation projects. Additionally, under the culture change moniker was the need to provide sufficient resource support in terms of subject matter expertise (both functional and technical) which requires a combination of internal and external resource allocation.
Second, Executives consistently voiced the belief that implementing AI was primarily was a change management challenge rather than a technology challenge. While debatable, it’s clear those who are winning have an enormous amount of emphasis in the change management arena. Furthermore, criticality of fusing together elements of people, processes, and technology through techniques such as Design Thinking, were touted as effectively enabling success.
Governance & Ethics
Third, AI brings a unique set of challenges when it comes to governance and ethics. Specific governance mechanisms arise with algorithm development, business rules, monitoring and escalation protocols, especially in cases where human life, community safety, and other critical matters are at stake. AI also introduces a myriad of new and/or increasingly complex ethical challenges, which admittedly, are just beginning to surface.
Insight #3: Technology Needs
In the technology arena, several points were illuminated. With the proliferation of AI technology providers and the components required in a given solution stack, company’s need to be ensure they’ve got capacity and expertise to address vendor/partner management. Several more prominent technology providers, also perform the integrator role to assist with managing solution providers in the solution stack. Designing an extensible solution architecture is imperative recognizing components within the solution stack will continuous evolve. Last, and most frequently communicated, was the need to have a strong focus on data. Organizations need to account for a data preparation phase in their timelines, and incorporate data management as an ongoing workstream through the program lifecycle.
Companies in various categories and stages of their AI journey need to ensure their roadmap accounts for these three topics (amongst others) to realize intended business objectives.