By Sameer Dhanrajani – CEO, AIQRATE & 3AI
Large & Medium Enterprises recognise the significance of building enterprise wide AI adoption in their core strategy , many are hoping to use AI to drive innovation and accentuate their business decisions and performance. While most enterprises are still embarking upon on full scale journey of AI adoption ; they still need to understand the importance of AI to drive innovation at scale, fewer than 20 percent in the fortune 2000 list have maximised the potential and achieved AI @scale to scale up innovation.
Whilst, several GCC’s initiate their AI journey eagerly, but most start without a clear strategy. As a result, their efforts often end up as small pilots that fail to scale or have significant impact. Some of these pilots have been mere exercises in “intellectual curiosity” rather than a serious effort to change the business. Consequently, they are not designed with an end-to-end approach that incorporates the necessary conditions for implementation. Instead, the pilots are carried out in small cohorts with limited connection to the innovation and business impact , and fail to provide the answers the business needs to move forward. Even if a pilot does answer the right questions, it may not address the cultural nuances that would, for example, make a sales representative trust a model more than her own experience.
AI Centre of Excellence within GCCs are evolving not only from being cost killers to innovation drivers. AI CoEs are set up not just for cost arbitrage but go all the way to tap into right talent and nurture in-house innovation. Business impact is being generated through AI driven process innovation and revealing new sources of revenue for stakeholders. No doubt , Artificial intelligence is one of the most powerful strategy for reshaping business in decades. It has the ability to optimise many processes throughout organisations and is already the engine behind some of the world’s most innovative platform businesses. AI will become a permanent aspect of the business landscape and AI capabilities need to be sustainable over time in order to develop and support potential new business models and capabilities.
GCCs need to establish COE’s and AI capability units to entrench AI. The idea of establishing a COC or COE in AI is not particularly radical; large firms using AI, 27% had already established AI CoE or COC. However, AI Centre of Excellence within GCC need to be reimagined not only for value arbitrage but go all the way to tap into right talent and nurture in-house innovation.
Strategic considerations for GCC’S to build AI Centres of Excellence to Scale up Innovation:
- Devise an AI strategy for Enterprise & GCC: It’s important for GCC senior executives to discuss ; ideally with AI experts — what’s the art of possible with AI, what it can do, and how it might enable new innovation models and strategies. Otherwise it may sub-optimise AI can do for the business. Identify business-driven problem statements: AI driven problem statements will need a prioritised list of applications or use cases within the GCC. They should balance strategic value with what is achievable. GCC’s may develop some of these use cases as pilots or prototypes, but they should also have a “pipeline” — regularly monitored by the AI centre and by executives — that leads to production deployment.
- Determine the incremental AI CoE/ CoC roadmap: Since AI typically supports tasks rather than entire jobs or business processes, it is usually best to undertake less ambitious projects as opposed to “moon shots.” But in order to get management attention and have a substantial impact on the business, GCCs may want to undertake a series of smaller projects in one area of the business. This may require a “road map” with multiple use cases across a timeline. An AI Centre can help a GCC “think big, start small , fail fast & scale quickly” with AI.
- Create a robust data engineering capability for AI strategy and ensuing problem statements define the data platform and tools needed to deliver. This is key for all (data-relevant) projects, to include all types of data — structured, unstructured, and external. AI CoE needs robust buildup of data pipes to feed sophisticated ML algorithms and also decide between on-premise versus cloud variations, and self-maintained open source solutions versus licensed solutions (e.g. Hadoop on Cloudera or AWS or open-source). Data engineering strategy can constitute of blending right data structures , data lake and cloud architecture essential for GCC’s to build scalability and robustness in the AI CoE.
- Device a robust innovation ecosystem creation to orchestrate relationships with universities, vendors, AI start-ups, and other sources of expertise and innovation. The GCC can develop an AI ecosystem, and perhaps even invest in firms that show promise of adding value to the business. This is also important for the tools and technology to be best-in-class. One of the crucial ways that GCCs can boost their innovation agenda within AI CoE/COC is by collaborating with start-ups, research institutes , accelerators. Hence, GCCs need to deploy a variety of strategies to build the ecosystem. These collaborations are a combination of build, buy, and partner models.
- Create AI evangelists to spread success stories and cultivate a network of influencers and champions across the businesses. Given the commodification of programming (with readily available auto driven and open source scripts), the focus for in-house capability building should be on statistical and mathematical modelling, rather than pure programming. A key success factor with AI is to spread early success stories with prioritised problem statements. This will build the appetite for more Gen AI activity.
- Reset Structures and Processes for an innovative and differentiated an AI centre. As AI talent is scarce, it is difficult to develop critical mass if it is scattered around the organisation. And experience with analytics functions says that that centralisation contributes to greater job satisfaction and retention for this type of role. To avoid excessive bureaucracy, a centralised group should embed — at least some of them — to business units or functions where AI is expected to be common. That way the centre staff can become familiar with the unit’s business issues and problems, and develop relationships with key executives. As gen AI starts to become pervasive, these embedded staff may move their primary organisational reporting line to business units or functions.
- Curate Innovative Insights & Intelligence in the GCCs AI COE for parent organisations to aid decision making and also serve as model of transformation & innovation thru incrementally pushing the ante to develop intelligent products , solutions for the business lines and horisontals . The AI CoE must strive at reaching at this pinnacle stage to ensure that the early success of the CoE are translated to building innovative products & solutions and transforming the businesses within the AI CoE and ultimately , becoming the nerve Centre aka strategy cell of the enterprise.
In the midst of large and mid-size enterprises coming out with rallying cry of AI-first enterprise , it is virtually impossible to succeed as an “AI first” enterprise without GCC enabling a robust AI Centre of excellence dedicated innovate the enterprise at scale. Time to action starts NOW…