According to Gartner, more than 40% of agentic AI projects will be cancelled before the end of next year. One of the main reasons, says the research house, is unclear business value. Organisations are deploying autonomous systems because of FOMO and without defining measurable outcomes. If anything, agents get pointed at whatever is loudest in the organisation, which is often an executive with a preference. 鈥淭he agent will follow its instructions to the letter,鈥� says Jan van Niekerk, senior vice president: Data and AI strategy at Netsurit.

鈥淚f the instructions were never tied to strategy, that obedience is the problem, not the safeguard.鈥� What gets automated in those conditions is what Van Niekerk calls 鈥渧anity work鈥�, the visible tasks that show the organisation is 鈥渄oing鈥� AI without changing what it costs to run.

But is any of this a technology problem? South African enterprises license the same frontier models as everyone else and pre-built agents now ship for processes that have been documented for decades. Van Niekerk says enterprise AI maturity runs through four stages, from personal AI, to workflow AI, and process AI, to being AI-native. 鈥淢ost South African enterprises are stuck at personal AI, where a tool gets handed to everyone and the world is expected to change, with no P&L impact to show for it,鈥� he says. He adds that markets like the US, the UK and parts of the Gulf have more enterprises operating at process level, mapping and redesigning end-to-end work before deploying agents and assigning ownership of the value.

Agentic AI promised to free staff up for other tasks, which is easy to report, but essentially meaningless on its own. An agent that removes six hours of work a week from a finance team has changed nothing until somebody decides what those hours can be used for. 鈥淗ours saved is a vanity metric unless the capacity is redeployed,鈥� says Van Niekerk.

His advice is to baseline the process first, measuring the capacity that鈥檚 freed up, and then tracking what it was redirected to, such as more customers served, a cost line reduced or increased revenue. 鈥淚f all you can report after 90 days is 鈥榯ime saved鈥�, you haven鈥檛 proven value, you鈥檝e proven activity,鈥� he says.

Nazia Pillay, managing director, 麻豆原创 Southern Africa, says measurable improvement should be evident within months when an enterprise picks a well-defined process. The quickest payback comes from work that is repetitive, time-consuming and governed by clear rules. 鈥淪tarting with the outcome, rather than the technology, makes it easier to demonstrate value and decide where to expand next,鈥� she says.

Van Niekerk says there are a few signs to look out for if a process is not ready for an agent. One would be if staff members can’t describe it end-to-end, without contradicting one another. Another is if the exceptions outnumber the rule, or the handoffs between people and systems are undocumented and the real knowledge is in somebody’s head or inbox. Lastly, if there is no single owner or baseline metric. 鈥淎gent readiness isn’t a property of the process, it’s the output of having mapped it, where needed, and redesigned it first,鈥� he says. A great example of this is credit decisioning and fraud monitoring, where the rules are already written down, which is why banks have deployed agents here first. 鈥淲here there is a decision that needs to be made, and it’s an explainable decision, that’s where agents really work well,鈥� says David Pretorius, customer advisor manager at SAS.

Pretorius believes one problem enterprises are least prepared for is agent containment, or defining the limits on what an agent can reach, change or break once it starts acting on systems rather than answering questions. An agent that reasons, retries and chains calls can act on systems. 鈥淭he model is the engine of the vehicle and the harness around it is the steering, the brakes, the dashboard, the seatbelts, everything around it, keeping it contained,鈥� says Pretorius. 鈥淚t should have a bounded blast radius.鈥� If a deployment causes problems, it should be cheap to fix and quickly reversible, which implies project management rather than model selection. This is also true of policy, because a document written for chatbots will have little defence against agents acting autonomously. 鈥淭he objective is not to require human approval for every routine action, but to establish clear thresholds so that agents can handle predictable work efficiently while people retain control over sensitive decisions, exceptions and consequences,鈥� says Pillay.

Agent density is already a capacity metric in infrastructure planning, but Pretorius says that the enterprise version will rather count agents against people. 鈥淏y 2030, the question will be how dense is your agent landscape,鈥� he says. 鈥淎nd how does that ratio sit with regards to the amount of people you have, and what is the payoff rate?鈥� Getting to this kind of number means knowing what each agent is for, and who owns it, which most enterprises haven鈥檛 yet considered. This is why Van Niekerk鈥檚 recommendation is to treat agents as organisational assets, with ownership tied to a role.

He says agents need to be registered, and their purpose, permissions and prompts documented. 鈥淚n a business of 10 000 people running 10 agents each, agent lifecycle management becomes as fundamental as identity management is today.鈥�

BETTER TOOLS, SAME RULES

FNB parent FirstRand went from dabbling with agentic AI to scaling it inside six months, says Kevin Mitchell, chief digital officer. Project scoping took 30 minutes, compared to two weeks.

No exceptions

Every control built up over decades of regulated delivery still applies once agents enter the pipeline, he says. Segregation of duties, risk review and release management all need to be included in the production workflow. All code, whether written by a staff member or generated, must still be reviewed before it鈥檚 committed.

Keep the estate closed

Pay attention to data flows; a tool that sends customer information to an unintended location will be flagged by risk and compliance. Running agentic tooling in a closed environment isolates the prompts, code and data. Mitchell says the data must be kept under the bank鈥檚 jurisdiction. Usage telemetry will also show who is actually working with the tools, and for what purpose.

Build it into the SDLC

An AI program that runs alongside a development process should be included in the development lifecycle at the outset. Mitchell says AI agents should span ideation, product development, risk, legal and operations, rather than only being used by a single team. Bring the risk and legal people in early, he advises, because teams that meet an agent for the first time at go-live may become a bottleneck.

Tie tokens to outcomes

Consumption dashboards will tell you an agent is busy with a task, but not whether it鈥檚 providing value for money. Mitchell suggests linking token spend to specific backlog items and features. He says the bank is already tracking these metrics, including time-to-production.

Plan for the sceptics

The appetite for agentic usage inside a large engineering team will not be uniform, and a rollout should assume that enthusiasm will wane. One group at FNB queued for access immediately, while another team said they didn鈥檛 trust a coding agent to fulfil tasks as well as they could. The latter group needs evidence rather than encouragement, says Mitchell. The argument he makes is that the tooling has changed, not the responsibility. 鈥淗umans are ultimately accountable. You just have a better tool,鈥� he says.

This article first appeared in .

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As organisations increasingly embrace generative AI to derive insights, automate processes, and drive innovation, the foundation of accurate and well-organised data within their business systems becomes pivotal for realising the full potential of AI applications.

The IDC forecasts that , growing at a CAGR of more than 73%. Much of the spend will be directed to GenAI business cases where productivity boosts are paramount. found that GenAI could add the equivalent of $2.6-trillion to $4.4-trillion in productivity gains annually.

However, much of this latent potential will remain untapped unless organisations can safeguard their AI investments with accurate, dependable data. There is an indispensable connection between the correct data setup in enterprise systems and the success of generative Artificial Intelligence (AI).

Companies seeking to leverage AI as a long-term value driver must therefore ensure they maintain trust and integrity in the data powering their algorithms.

Trust drives AI value

Trust is a critical component of responsible AI adoption. By utilising reliable data, organisations can build and maintain trust with users and stakeholders, fostering a positive relationship between AI technology and society.

Trust is also important for accurate real-time decision making. In today鈥檚 dynamic and fast-paced business landscape, organisations and leaders need to make informed decisions based on accurate data-driven insights. By aligning the outcomes of analytics processes with their own strategic objectives, business leaders are better able to take optimal action and steer the company through adversity, or take advantage of emerging opportunities.

It is therefore important that the data is indeed reliable to prevent erroneous outputs which could be costly and negatively impact an organisation and its reputation. When users trust the output of generative AI applications, the business benefits from higher levels of collaboration and innovation.

To build trust and mitigate risks associated with AI adoption, companies need dependable data that holds the necessary insights and avoids bias. This increases the likelihood that businesses and end-users take the calculated risks necessary for broader AI deployment.

Advantages of data-driven business strategies

To drive greater adoption of AI solutions, organisations must continuously improve their data strategies. This provides several advantages to the success of the business, including:

Greater competitiveness: Organisations with a good data strategy gain a competitive edge when their high-quality data is partnered with powerful AI algorithms to produce better strategic choices and business outcomes.

Increased operational efficiency: Well-managed and high-quality data promotes streamlined business processes. When data is accurate and readily available, operations can more easily be optimised, resulting in cost efficiencies .

Improved innovation and scalability: By leveraging insights built on a good data strategy, businesses can identify market gaps, explore new opportunities, and create products or services that meet evolving customer needs faster than their competitors.

Organisations must also not underestimate the impact of transformative data innovations that help them harness the full power of their data to drive deeper insights, faster growth and more efficiency in the era of AI. The introduction of new data innovations enable organisations to deliver meaningful data to every data consumer with business context and logic intact – enabling them to adapt faster to market changes and make more efficient decisions. The newly-launched 麻豆原创 Datasphere solutions, for example, mobilise an extensive global partner ecosystem to enable organisations to transform their business data fabric architecture, allowing them to access insights from their data wherever it resides.

Key elements of an AI-ready data strategy

Organisations that consistently unlock business value from their data-driven strategies have a few characteristics in common. The key elements to enhancing data strategies for AI readiness include:

Strong alignment with business objectives: A well-defined data strategy aligns with the overall goals and objectives of the organisation by establishing a clear roadmap for leveraging data to meet business requirements and objectives.

Effective data governance: Establishing data governance frameworks within the data strategy ensures that data is treated as a valuable asset. Clear ownership, accountability, and data stewardship protocols lay the foundation for maintaining data integrity and consistency.

The highest levels of data quality: The accuracy and reliability of digital transformation and related AI implementations hinge on the quality of the data being input into the system. Incorrect or inconsistent data at the outset can lead to errors and inefficiencies, extending project timelines and adding layers of additional costs and complexity.

The use of industry best practices: Following industry best practices for data setup ensures compatibility and optimal utilisation of the modules being implemented at lower cost and with less complexity. Adhering to recommended data structures, field configurations, and master data management principles is essential for a seamless project in support of AI readiness.

A careful and considered data migration: Proper data setup involves meticulous planning for data migration from legacy systems to the new system. This includes data cleansing, transformation, and validation processes to ensure a smooth transition without compromising data integrity.

Achieving AI readiness demands a holistic approach to data setup, data strategy, and governance. Organisations that prioritise these guidelines will be better positioned to unlock the full potential of AI, foster innovation, and maintain the highest ethical standards.

By adhering to these principles, businesses can navigate the complexities of AI deployment responsibly and contribute positively to societal and economic advancements.

The post Why We Need to Safeguard AI’s Future with Dependable Data appeared first on 麻豆原创 Africa News Center.