STRATEGIC IMPERATIVES FOR GCC’S TO BUILD & SCALE STATE- OF-ART GENERATIVE AI CENTER OF EXCELLENCE(CoE)

Enterprises recognize the significance of building enterprise wide Generative AI adoption in their core strategy?, many are hoping to use Generative AI to drive their business decisions and performance. While most companies still need to understand the importance of Gen AI and are far away from adopting common best practices, fewer than 20 percent in the fortune 2000 list have maximized the potential and achieved AI @scale.

In working closely with a wide & seasoned GCC’s-Data ,Analytics & AI leaders , 3AI has witnessed many GCC’s start their analytics journey eagerly, but 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 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.

Thus, these GCC’s & parent organizations quickly become impatient when they see their efforts falling .Democratization of data is blurring sector boundaries; businesses will increasingly find themselves disrupted not by the company they have been monitoring for the last several years, but by a newcomer from another industry. Being the best in an industry is no longer enough; now companies must aspire to be at least at par across industries to compete effectively. Functional expertise, beyond specific sector expertise, will become more and more relevant.

Generative AI Centre of Excellence within enterprises are evolving not only from being cost killers to value drivers. Gen 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 Gen AI driven process innovation and revealing new sources of revenue for stakeholders.

No doubt , Gen Artificial intelligence is one of the most powerful strategy for reshaping business in decades. It has the ability to optimize many processes throughout organizations and is already the engine behind some of the world’s most valuable platform businesses. In our view Gen AI will become a permanent aspect of the business landscape and Gen AI capabilities need to be sustainable over time in order to develop and support potential new business models and capabilities.

Specifically, we believe that enterprises need to establish dedicated organizational units to entrench Gen AI. This is an important business tool that cannot be left to bottom-up whimsy. GCC’s are devoting considerable financial resources to Gen AI, and necessary skills and experience are too rare to assume that they will be scattered around the organization with little coordination or collaboration. Just as e-commerce led to Chief Digital Officers and groups to support online presence and commerce, we believe that Gen AI will engender new competence & capability centres (CC) or centres of excellence or capability (COE)/ (CoC), and new roles within them.

The idea of establishing a COC or COE in Generative AI is not particularly radical; large firms using AI, 27% had already established AI CoE or COC. However, Gen 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. Business impact is being generated through Gen AI driven process innovation and revealing new sources of revenue for stakeholders.

STRATEGIC IMPERATIVES FOR GCC’S TO CONSIDER TO BUILD & SCALE TOP – OF- LINE GENERATIVE AI CENTER OF EXCELLENCE

1. Create a vision for Gen AI in the GCC :

It’s important for executives to discuss ; ideally with AI experts — what Gen AI is, what it can do, and how it might enable new business models and strategies. Otherwise it may sub-optimize what Gen AI can do for the business. Identify business-driven problem statements: Gen AI driven problem statements will need a prioritized 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 Gen AI centre and by executives — that leads to production deployment. Determine the incremental Gen AI CoE/ CoC roadmap: Since Gen 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, organizations 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 Gen AI Centre can help a GCC “think big, start small , fail fast & scale quickly” with Gen AI

2. Create a robust data engineering strategy & capability :

The Gen 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. Gen AI CoE or CoC 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 Gen AI CoE /CoC

3. Device a robust Ecosystem creation :

An Gen AI CoE/CoC can help to orchestrate relationships with universities, vendors, Gen AI start-ups, and other sources of expertise and innovation. The GCC can develop an Gen 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 Gen 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:

• Platform Evangelization: offer access to their Gen AI platforms to start-ups
• License or Vendor Agreement: Gen AI tart-ups enter into a license agreement to create solutions
• Co-innovate: collaborate to co-create new solutions & capabilities
• Acqui-hire: acquire AI start-ups for the talent & capability
• Research centres : collaborate with academic institutes for joint IP creation , open research , customized programs
• Joint Accelerator program : build joint program for customized startups cohort

4. Create Gen AI evangelists to spread success stories :

An Gen AI CoE/CoC will work best if it cultivates 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 Gen AI is to spread early success stories with prioritized problem statements. This will build the appetite for more Gen AI activity

5. Develop a Talent Mapping Strategy:

With the evolution of analytics ,data sciences to Gen AI , the lines between different skills are blurring. We are witnessing a convergence of skills required across verticals. The strategic shift of GCCs towards Gen AI centre of capability model has led to the creation of several new age Gen AI roles. To build skills in Gen AI & data engineering, GCCs need to adopt a hybrid approach. The skill development roadmap for Gen AI is a combination of build and buy strategies. The decision to acquire talent from the ecosystem or internally build capabilities is a function of three parameters –Maturity of GCC ’s existing AI capabilities in the desired or adjacent areas ,Tactical nature of skill requirement & Availability and accessibility of talent in the ecosystem. There’s always a heavy Inclination towards building skills in-house within GCCs and a majority of GCCs have stressed upon that the bulk of the future deployment in Gen AI areas will be through in-house skill-building and reskilling initiatives. However, talent mapping strategy for building Gen AI capability is a measured approach else can result in being a Achilles heel for GCC and HR leaders.

6. Reset Structures and Processes :

While there is no single best organizational structure for an Gen AI centre, we think that in most cases GCCs would be well-served by a central structure with deployed or embedded start, reporting to an enterprise-wide business function. Since gen AI talent is scarce, it is difficult to develop critical mass if it is scattered around the organization. And our experience with analytics functions was that centralization contributes to greater job satisfaction and retention for this type of role. To avoid excessive bureaucracy, a centralized group should embed — at least some of them — to business units or functions where Gen 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. Rotational programs across business units can improve knowledge growth and transfer. As gen AI starts to become pervasive, these embedded staff may move their primary organizational reporting line to business units or functions. There are a variety of possible areas into which an Gen AI CoE might report, but we’d argue that the best one is a central strategy group that is also responsible for strategic tasks.

7. Curate Insights, Intelligence & Recommendations :

Gen AI COE /COC need to generate top of the line insights and recommendations for parent organizations 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 horizontals . The Gen 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 Gen AI CoE and ultimately , becoming the nerve Centre aka strategy cell of the enterprise.

Through the interactions with several AI & Analytics leaders , 3AI believes that it is virtually impossible to succeed as an “AI first” enterprise without GCC enabling a robust Gen AI Centre of excellence dedicated to transform and innovate the enterprise . Time to action starts NOW…

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