The AI-FIRST CEO/ CXO
Enterprise AI: The CEO/ CXO Way
Several current surveys including the ones conducted by AISWITCH and our research partners, conducted between 2021 and 2023, show:
- Even though AI is increasingly becoming mainstream in terms of applications at enterprise levels, i.e. in enterprise IT usecases as well as several business process-related and functional usecases, in 48-50%+ cases the applications are still driven by enterprise IT, about 40% by digital tech teams, and not led by business and functional teams.
While this is a totally acceptable trend during early adoption and initial state of maturity of enterprise applications of any relatively new technologies, it is important for enterprise leaders to note that AI TECHNOLOGIES NOW ARE JUST AS SIGNIFICANT STRATEGIC DISRUPTORS FOR BUSINESSES, AS COMPUTERS HAD FUNDAMENTALLY BEEN, IN THE '80's.
That is why- it's high time the the CEOs OWN, LEAD AND DRIVE THE ENTERPRISE AI AGENDA, and not the CIOs, COOs or CDO's.
Why should the CEOs take charge of their enterprise AI agenda?
Because of these 5 reasons:
- AI is the Biggest Source of Strategic Differentiation, for your company and your industry.
- Your company's AI agenda and strategy, and abilities to execute and deliver on that agenda, is going to impact Your main KPIs. So is the case of all your peer CEOs and competitors. Net net, the differentiating abilities, vision and execution efficacies of a CEO of almost any company in any sector, are going to be evaluated to a large extent by his/her ability to utilize AI in the company to drive drastically different business outcomes.
- Enterprise AI as a technology disruption is a bit different from other IT evolutions. The foundational understanding about the strategic applications (i.e. not the technologies in themselves) of the algorithms and autonomous self-learning machines on enterprise data, need to be embedded in solid understanding of the company's business, operating models and strategic levers. That's the CEO and board's primary territory of ownership. The DIRECTIONS have to come from this top layer of the enterprise, of course with inputs and considerations from other CXOs. The agenda and strategy need to be owned up by the CEO, the enterprise leader.
- AI is hugely expensive, with a potentially long-term break-even and RoI, even though the RoI will be hugely impactful and non-linear, if and when done right beyond just point solutions and applications.
- AI is long-term. So, just as the investments will take time to break-even, they also need to considered in a 360, holistic manner, i.e. in sync with the strategic business roadmap, long-term growth projections, potential regulatory hurdles, market predictions, business disruptions, ESG requirements and so on. Only the CEO is likely to have a full view of this enterprise value and market spectrum, in its entirety. The other CXO;s, e.g. the COO, CFO, Chief Risk and Compliance Officers or Legal office, talent office etc. will have specialized but fragmented view, focussed on their key functions.
AI Investment Centers Glidepath
The Reverse Z
The model above describes the practical path of an enterprise CEO/ CXO to move in a proven and tested glidepath of AI investments.
- The CEO and leadership team AI evangelists start AI experimentation in siloes, starting with a few easy and inexpensive low-hanging fruits low-risk usecases. These are done as pilots and POCs. Based on success in a 6 months to 1 year horizon,, the leaders assign AI CoE teams to scale these up, while primarily focussed on cost reduction in operations as your key targeted outcomes and impact. This is AI as a cost center.
- Once leadership team members plus a few board members get excited by the operational outcomes due to a reasonable scale of adoption of the cost-saving AI usecases,, they encourage AI CoE leaders to experiment, build, test and deploy strategic AI usecases that can help run and grow your topline business indicators. These will include advanced agentic AI usecases in critical revenue-impacting functions and services e.g. new market addition / expansion, new agentic/ digital-only service delivery channels for new revenue generation and new users/ customer segments, new product/ service development. These will transform the cost-focussed, operations-driven AI CoE to a strategic value-driven revenue center.
- Once the revenues from the AI CoE becomes significant, consistent, predictable and manageable, the CXO leaders and the board become confident to leverage the AI CoE to drive profitability and non-linear/ disruptive growth. These initiatives include experimentation and usage of latest AI techniques, algorithms and infra stacks, to create high-profitability products, markets, customer segments, services e.g. enabling hyper-personalized flexible investment products for industry-specific HNI customers, in case of retail banking. These complex but innovative usecases will drive profitability and become sustainable sources of competitive advantage, beyond just "me-too" usecases of cost and revenue. Hence, at this level, the AI CoE will become a profit center.
- When more than 50% of the profitability-targeted AI investments become successful in actually increasing company profits significantly (e.g. improving profit margins by 25% +), the company, its leaders, large customers and board-members will see it as an AI-first company, irrespective of whichever sectors it operates in. Then the AI CoE becomes a strategic investment center i.e. AI usecases become the de facto dynamic business operating models and platforms, in a composable architecture mode. As an investment center the AI CoE becomes an active R&D center to experiment with AI technologies in collaboration with tech partners and other research institutions. That way, it continues to disrupt the industry in operates in, being a first-mover or an early-mover of applying the new disruptive AI techstacks into a new business value proposition and operating model.
AI Innovation Fund: AISWITCH Steps
Pivot AI Valuestreams from Operational to Strategic
Strategic AI Innovation Mandate and Fund:
The AI-powered CEOs should start a top-down AI agenda, to complete the AI 360 value cycle, along with bottom-up user-driven AI e.g. Gen AI applications and piecemeal experimentations by developers and business users.
Why, what and how
Why a Strategic AI Innovation Fund:
AI as the Key Source of Sustainable Competitive Advantage: To move AI beyond operational agenda.
What is a strategic AI innovation fund:
· A dedicated, corporate/ group-level AI Leverage fund with CEO and Board as Sponsors.
· AI for Systems of innovation and differentiation: Beyond current-state AI usecases for Systems of Records and Transactions.
For example:
- Current-state operational AI usecases for Fraud/ anomaly detection in BFS, claims fraud identification for insurance. ==> Future-state strategic usecases for RT GenAI algorithms doing real-time tracking, prediction and proactive resolutions/ prevention of fraud attempts/ threats.
- Current-state KYX usecases to ==> Future-state strategic usecases e.g. AI to generate dynamic hyper-personalized value propositions/ offerings/ contracts/ pricing models, with creative and imaginative genAI usecases for different customer and suppliers partners/ segments
How to set it up in partnership with strategic SPs:
- To enable co-innovations with strategic SPs as Innovation Partners
- CXO leaders and SP partner leaders together set up, co-sponsor and co-own AI Disruptive Experimentation testbeds and innovation garages/ labs
- The Service Provider as Innovation Co-sponsors allocates funds in existing/ new contracts to be used on innovation projects that are agreed to by the client and Provider.
Each Innovation project must minimally include:
§ clear outline of business goals/outcomes,
§ measurable KPIs, baselines, best practice discoveries- by persona’s top-down (UDA- Up Down and Across)
§ strong and clear, practicable management/governance policies, frameworks, best practices on codified experiential knowledge (e.g. on types of data security/ privacy errors, plagiarisms, legal liabilities, unpredictable behavior/ responses from Bots)
This AI innovation fund should be an investment by the Provider Partners vs offsetting by raising the price. It will improve both the stickiness and probability of renewal for the Providers, while also increasing the intrinsic value/ impact of the contract and the QoR (Quality of Relationship) with client leadership teams.
Joint AI Innovation KPIs:
Innovation Projects should have shared KPIs and KRAs (key responsibility areas) between Client and Provider leaders/ teams, to work on scenarios where 1) parts of the client enterprise are resistant to change, 2) cost and time overruns go outside planned boundaries e.g. too expensive 3) disrupt the steady state business. KPIs should be established upfront to identify innovation projects, exec owners, timelines, and systems, applicable policies and GRC frameworks.